system

Abstract

The system according to this embodiment aims to analyze PC operation logs and automatically identify and execute tasks that can be automated. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a discrimination unit, a generation unit, a notification unit, and an introduction unit. The collection unit collects PC operation logs. The analysis unit analyzes the operation logs collected by the collection unit and learns the business content. The discrimination unit determines which tasks can be automated based on the content learned by the analysis unit. The generation unit generates code for automating the tasks determined by the discrimination unit. The notification unit notifies the PC of the code generated by the generation unit. The introduction unit introduces the code notified by the notification unit.

Description

Technical Field

[0004] ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the automation of PC operations is often performed manually, and efficient business automation is difficult.

[0005] The system according to the embodiment aims to analyze PC operation logs and automatically identify and execute operations that can be automated.

Means for Solving the Problems

[0006] The system according to the embodiment comprises a collection unit, an analysis unit, a discrimination unit, a generation unit, a notification unit, and an introduction unit. The collection unit collects PC operation logs. The analysis unit analyzes the operation logs collected by the collection unit and learns the business content. The discrimination unit determines which tasks can be automated based on the content learned by the analysis unit. The generation unit generates code for automating the tasks determined by the discrimination unit. The notification unit notifies the PC of the code generated by the generation unit. The introduction unit introduces the code notified by the notification unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze PC operation logs and automatically identify and execute tasks that can be automated. [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 such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

[0019] The smart device 14 comprises a computer 36, a 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 automation system according to an embodiment of the present invention is a system that uses AI to learn PC operations and automates simple, repetitive tasks (routine tasks). This automation system installs dedicated software on the PC and collects PC operation logs when the user performs their work as usual, understanding and learning the content of the work. Based on the learned content, it identifies tasks that can be automated and generates automation code such as Python or VBA. The generated code is notified on the PC, and the user implements the code according to the instructions. This completes the automation of routine tasks, leading to a reduction in man-hours and a reduction in human error. For example, a collection unit is provided to collect PC operation logs. This collection unit collects PC operation logs when the user performs their work as usual. For example, it collects detailed operation logs such as click operations and keyboard inputs performed by the user. Next, an analysis unit is provided to analyze the collected operation logs. This analysis unit analyzes the operation logs collected by the collection unit and learns the content of the work. For example, it analyzes whether a specific operation is performed repeatedly and identifies routine tasks. Furthermore, a discrimination unit is provided to determine tasks that can be automated based on the content learned by the analysis unit. This discrimination unit identifies tasks that can be automated based on the information learned by the analysis unit. For example, it identifies tasks that involve a lot of routine work, such as data entry and the creation of standardized reports. A generation unit is provided to generate code for automation based on the identified tasks. This generation unit generates code to automate the tasks identified by the discrimination unit. For example, it generates automation scripts using programming languages ​​such as Python or VBA. A notification unit is provided to notify the PC of the generated code. This notification unit notifies the PC of the code generated by the generation unit. For example, it prompts the user to install the code in the form of a pop-up notification or email notification. Finally, an installation unit is provided to install the code based on the notification. This installation unit installs the code notified by the notification unit onto the PC. For example, by executing the code according to the instructions, the automation of the routine task is completed. In this way, the automation system can train the AI ​​to learn PC operations and automate simple, repetitive tasks.

[0029] The automation system according to the embodiment comprises a collection unit, an analysis unit, a discrimination unit, a generation unit, a notification unit, and an introduction unit. The collection unit collects PC operation logs. The collection unit collects detailed operation logs, such as click operations and keyboard inputs performed by the user. For example, the collection unit can record click operations performed by the user and identify which application was used. The collection unit can also record keyboard inputs and analyze the content of the entered text. Furthermore, the collection unit can perform screen captures and visually record the user's operations. The analysis unit analyzes the operation logs collected by the collection unit and learns the content of the work. For example, the analysis unit analyzes whether a particular operation is performed repeatedly. For example, the analysis unit can detect that data entry operations are performed frequently and identify this as a routine task. The analysis unit can also detect that a particular application is used frequently and identify the tasks related to that application. Furthermore, the analysis unit can analyze the order and timing of operations and grasp the flow of the work. The discrimination unit determines which tasks can be automated based on the content learned by the analysis unit. The discrimination unit identifies tasks that involve a lot of routine work, such as data entry or the creation of standardized reports. For example, the discrimination unit can detect that data entry operations are performed repeatedly and identify them as tasks that can be automated. It can also detect that standardized report creation operations are performed frequently and identify them as tasks that can be automated. Furthermore, the discrimination unit can detect that a particular application is used frequently and identify tasks related to that application as tasks that can be automated. The generation unit generates code to automate the tasks identified by the discrimination unit. The generation unit generates automation scripts using programming languages ​​such as Python or VBA. For example, the generation unit can generate a Python script to automate data entry operations. It can also generate a VBA macro to automate standardized report creation operations.Furthermore, the generation unit can also generate scripts to automate operations related to a specific application. The notification unit notifies the PC of the code generated by the generation unit. The notification unit prompts the user to install the code, for example, in the form of a pop-up notification or email notification. For example, the notification unit can display a pop-up notification and instruct the user to install the generated code. The notification unit can also send an email notification and instruct the user to install the generated code. Furthermore, the notification unit can notify the user through a chat application and instruct the user to install the generated code. The installation unit installs the code notified by the notification unit onto the PC. The installation unit completes the automation of routine tasks, for example, by having the user execute the code according to the instructions. For example, the installation unit can execute a generated Python script to automate data entry operations. The installation unit can also execute a generated VBA macro to automate routine report creation operations. Furthermore, the installation unit can execute a script related to a specific application and automate operations related to that application. As a result, the automation system according to the embodiment can train the AI ​​on PC operations and automate simple, repetitive tasks.

[0030] The data collection unit can analyze the user's past operation history and select an appropriate collection method. For example, the data collection unit can prioritize collecting operations that the user has frequently performed in the past. For example, the data collection unit can analyze past operation history and identify frequently performed operations. The data collection unit can also predict operations performed during specific time periods based on the user's past operation history and concentrate data collection during those times. For example, the data collection unit can predict operations performed during specific time periods based on past operation history and perform data collection during those times. Furthermore, the data collection unit can analyze the user's past operation patterns and propose efficient collection methods. For example, the data collection unit can analyze past operation patterns and identify efficient collection methods. This enables efficient data collection by analyzing past operation history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past operation history data into a generating AI and have the generating AI select an efficient collection method.

[0031] The data collection unit can filter operation logs based on the user's current work status and areas of interest when collecting them. For example, the data collection unit can collect only operation logs related to the tasks the user is currently performing. For example, the data collection unit can analyze the current work status and identify relevant operation logs. The data collection unit can also prioritize the collection of highly relevant operation logs based on the user's areas of interest. For example, the data collection unit can analyze the user's areas of interest and identify relevant operation logs. Furthermore, the data collection unit can filter out unnecessary operation logs according to the user's work status, enabling efficient data collection. For example, the data collection unit can analyze the current work status and filter out unnecessary operation logs. This allows for efficient data collection by filtering based on the user's work status and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input current work status data into a generating AI and have the generating AI perform the filtering.

[0032] The data collection unit can prioritize the collection of highly relevant logs based on the user's geographical location information when collecting operation logs. For example, the data collection unit can prioritize the collection of operation logs performed by the user at a specific location. For example, the data collection unit can analyze the user's geographical location information and identify operation logs performed at that location. The data collection unit can also filter highly relevant operation logs based on the user's geographical location information. For example, the data collection unit can identify relevant operation logs based on the user's current location. Furthermore, if the user is on the move, the data collection unit can prioritize the collection of operation logs related to the current location. For example, the data collection unit can analyze the user's movement pattern and identify relevant operation logs. This enables efficient data collection by collecting highly relevant logs based on the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input geographical location data into a generating AI and have the generating AI collect highly relevant logs.

[0033] The data collection unit can analyze a user's social media activity and collect relevant logs when collecting operation logs. For example, the data collection unit can collect relevant operation logs based on information shared by the user on social media. For example, the data collection unit can analyze a user's social media activity and identify relevant operation logs. The data collection unit can also prioritize the collection of work-related operation logs from the user's social media activity. For example, the data collection unit can analyze a user's social media posts and identify relevant operation logs. Furthermore, the data collection unit can analyze a user's social media activity and filter out work-related operation logs. For example, the data collection unit can identify relevant operation logs based on the user's social media activity. This enables efficient data collection by collecting relevant logs based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media activity data into a generating AI and have the generating AI collect relevant logs.

[0034] The analysis unit can adjust the level of detail of the analysis based on the importance of the operations during the analysis. For example, the analysis unit can perform a detailed analysis for operations with high importance. For example, the analysis unit can evaluate the importance of operations and perform a detailed analysis for operations with high importance. The analysis unit can also perform a simplified analysis for operations with low importance. For example, the analysis unit can evaluate the importance of operations and perform a simplified analysis for operations with low importance. Furthermore, the analysis unit can dynamically adjust the level of detail of the analysis according to the importance of the operations. For example, the analysis unit can evaluate the importance of operations and adjust the level of detail of the analysis according to the importance of the operations. This makes efficient analysis possible by adjusting the level of detail of the analysis according to the importance of the operations. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input operation importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0035] The analysis unit can apply an appropriate analysis algorithm depending on the category of the operation during analysis. For example, the analysis unit can apply a specific analysis algorithm to data input operations. For instance, the analysis unit can apply a clustering algorithm to data input operations to analyze data patterns. The analysis unit can also apply a different analysis algorithm to report creation operations. For example, the analysis unit can apply regression analysis to report creation operations to analyze data trends. Furthermore, the analysis unit can select the optimal analysis algorithm depending on the category of the operation. For example, the analysis unit can evaluate the category of the operation and select the optimal analysis algorithm. This improves the accuracy of the analysis by applying the optimal analysis algorithm according to the category of the operation. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the category data of the operation into a generating AI and have the generating AI select the optimal analysis algorithm.

[0036] The analysis unit can determine the priority of the analysis based on the timing of the operations during the analysis. For example, the analysis unit can prioritize the analysis of recently performed operations. For example, the analysis unit can evaluate the timing of the operations and prioritize the analysis of recently performed operations. The analysis unit can also prioritize the analysis of operations that have been frequently performed in the past. For example, the analysis unit can evaluate the timing of the operations and prioritize the analysis of operations that have been frequently performed in the past. Furthermore, the analysis unit can dynamically adjust the priority of the analysis based on the timing of the operations. For example, the analysis unit can evaluate the timing of the operations and dynamically adjust the priority. This enables efficient analysis by determining the priority of the analysis based on the timing of the operations. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input operation timing data into a generating AI and have the generating AI determine the priority of the analysis.

[0037] The analysis unit can adjust the order of analysis based on the relevance of operations during analysis. For example, the analysis unit can prioritize the analysis of operations with high relevance. For example, the analysis unit can evaluate the relevance of operations and prioritize the analysis of operations with high relevance. The analysis unit can also postpone the analysis of operations with low relevance. For example, the analysis unit can evaluate the relevance of operations and postpone the analysis of operations with low relevance. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of operations. For example, the analysis unit can evaluate the relevance of operations and dynamically adjust the order. This allows for efficient analysis by adjusting the order of analysis based on the relevance of operations. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input operation relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0038] The discrimination unit can improve the accuracy of its discrimination based on the interrelationships of operations during discrimination. For example, the discrimination unit can analyze the interrelationships of operations and discriminate related operations collectively. For example, the discrimination unit can evaluate the interrelationships of operations and discriminate related operations collectively. Furthermore, the discrimination unit can improve the accuracy of its discrimination based on the interrelationships of operations. For example, the discrimination unit can evaluate the interrelationships of operations and improve the accuracy of its discrimination. In addition, the discrimination unit can prevent misclassification by considering the interrelationships of operations. For example, the discrimination unit can evaluate the interrelationships of operations and prevent misclassification. As a result, by improving the accuracy of discrimination based on the interrelationships of operations, more accurate discrimination becomes possible. Some or all of the above processing in the discrimination unit may be performed using AI, for example, or without using AI. For example, the discrimination unit can input the interrelationship data of operations into a generating AI and have the generating AI perform the improvement of discrimination accuracy.

[0039] The discrimination unit can perform discrimination based on the attribute information of the person performing the operation. For example, the discrimination unit can perform discrimination based on the position and job duties of the person performing the operation. For example, the discrimination unit can evaluate the attribute information of the person performing the operation and perform discrimination based on their position and job duties. The discrimination unit can also perform discrimination by referring to the past work history of the person performing the operation. For example, the discrimination unit can evaluate the past work history of the person performing the operation and perform discrimination. Furthermore, the discrimination unit can adjust the discrimination criteria according to the skill level of the person performing the operation. For example, the discrimination unit can evaluate the skill level of the person performing the operation and adjust the criteria. This makes it possible to perform more appropriate discrimination by considering the attribute information of the person performing the operation. Some or all of the above processing in the discrimination unit may be performed using AI, for example, or without using AI. For example, the discrimination unit can input the attribute information data of the person performing the operation into a generating AI and have the generating AI perform discrimination.

[0040] The discrimination unit can perform discrimination based on the geographical distribution of operations. For example, the discrimination unit can perform discrimination based on the location where the operation was performed. For example, the discrimination unit can evaluate the geographical distribution of operations and perform discrimination based on the location. The discrimination unit can also discriminate related operations collectively based on geographical distribution. For example, the discrimination unit can evaluate the geographical distribution of operations and discriminate related operations collectively. Furthermore, the discrimination unit can prevent misclassification by considering geographical distribution. For example, the discrimination unit can evaluate the geographical distribution of operations and prevent misclassification. This makes it possible to perform more appropriate discrimination by performing discrimination based on the geographical distribution of operations. Some or all of the above processing in the discrimination unit may be performed using AI, for example, or without using AI. For example, the discrimination unit can input geographical distribution data of operations into a generating AI and have the generating AI perform the discrimination.

[0041] The discrimination unit can improve the accuracy of its discrimination based on relevant literature for the operation during discrimination. For example, the discrimination unit can improve the accuracy of its discrimination by referring to relevant literature for the operation. For example, the discrimination unit can improve the accuracy of its discrimination by evaluating relevant literature for the operation. The discrimination unit can also set discrimination criteria based on relevant literature. For example, the discrimination unit can evaluate relevant literature and set criteria. Furthermore, the discrimination unit can prevent misclassification by referring to relevant literature for the operation. For example, the discrimination unit can evaluate relevant literature for the operation and prevent misclassification. As a result, the accuracy of discrimination is improved by referring to relevant literature for the operation. Some or all of the above processing in the discrimination unit may be performed using AI, for example, or without AI. For example, the discrimination unit can input relevant literature data for the operation into a generating AI and have the generating AI perform the improvement of discrimination accuracy.

[0042] The generation unit can improve the accuracy of code generation based on the interrelationships of operations. For example, the generation unit can analyze the interrelationships of operations and code related operations together. For example, the generation unit can evaluate the interrelationships of operations and code related operations together. The generation unit can also improve the accuracy of generation based on the interrelationships of operations. For example, the generation unit can evaluate the interrelationships of operations and improve the accuracy of generation. Furthermore, the generation unit can prevent erroneous generation by considering the interrelationships of operations. For example, the generation unit can evaluate the interrelationships of operations and prevent erroneous generation. This makes it possible to generate more accurate code by improving the accuracy of generation based on the interrelationships of operations. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data on the interrelationships of operations into a generation AI and have the generation AI perform the improvement of generation accuracy.

[0043] The generation unit can generate code based on the attribute information of the person performing the operation. For example, the generation unit can generate code based on the operator's job title and job duties. For example, the generation unit can evaluate the operator's attribute information and generate code based on their job title and job duties. The generation unit can also generate code by referring to the operator's past work history. For example, the generation unit can evaluate the operator's past work history and generate code. Furthermore, the generation unit can adjust the level of detail of the code according to the operator's skill level. For example, the generation unit can evaluate the operator's skill level and adjust the level of detail. This makes it possible to generate more appropriate code by considering the operator's attribute information. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the operator's attribute information data into a generation AI and have the generation AI perform the generation.

[0044] The generation unit can perform code generation based on the geographical distribution of operations. For example, the generation unit can generate code based on the location where the operation was performed. For example, the generation unit can evaluate the geographical distribution of operations and generate code based on the location. The generation unit can also code related operations in a batch based on their geographical distribution. For example, the generation unit can evaluate the geographical distribution of operations and code related operations in a batch. Furthermore, the generation unit can prevent erroneous generation by considering the geographical distribution. For example, the generation unit can evaluate the geographical distribution of operations and prevent erroneous generation. This makes it possible to generate more appropriate code by performing generation based on the geographical distribution of operations. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input geographical distribution data of operations into a generation AI and have the generation AI perform the generation.

[0045] The generation unit can improve the accuracy of code generation by referring to relevant documentation for the operation. For example, the generation unit can improve the accuracy of generation by referring to relevant documentation for the operation. For example, the generation unit can improve the accuracy of generation by evaluating relevant documentation for the operation. The generation unit can also set generation criteria based on relevant documentation. For example, the generation unit can evaluate relevant documentation and set criteria. Furthermore, the generation unit can prevent erroneous generation by referring to relevant documentation for the operation. For example, the generation unit can evaluate relevant documentation for the operation and prevent erroneous generation. As a result, the accuracy of generation is improved by referring to relevant documentation for the operation. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input relevant documentation data for the operation into a generation AI and have the generation AI perform the improvement of generation accuracy.

[0046] The notification unit can select an appropriate notification method based on the user's past notification history when a notification is sent. For example, the notification unit can prioritize notification methods that the user has preferred to use in the past. For example, the notification unit can evaluate the user's past notification history and select preferred notification methods. The notification unit can also predict the optimal notification timing based on the user's past notification history. For example, the notification unit can predict the optimal notification timing based on past notification history. Furthermore, the notification unit can analyze the user's past notification history and suggest effective notification methods. For example, the notification unit can evaluate past notification history and suggest effective notification methods. This allows the notification unit to select the optimal notification method by referring to the user's past notification history. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input past notification history data into a generating AI and have the generating AI select the notification method.

[0047] The notification unit can adjust the timing of notifications based on the user's current work situation. For example, if the user is busy, the notification unit can postpone the notification. For example, the notification unit can assess the user's work situation and postpone the notification if the user is busy. The notification unit can also send an immediate notification if the user is relaxed. For example, the notification unit can assess the user's work situation and send an immediate notification if the user is relaxed. Furthermore, the notification unit can dynamically adjust the optimal notification timing according to the user's work situation. For example, the notification unit can assess the user's work situation and dynamically adjust the optimal notification timing. This allows for more appropriate notifications by adjusting the timing of notifications according to the user's work situation. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input work situation data into a generating AI and have the generating AI perform the adjustment of the notification timing.

[0048] The notification unit can select an appropriate notification method based on the user's geographical location information when sending a notification. For example, if the user is in a specific location, the notification unit can select a notification method suitable for that location. For example, the notification unit can evaluate the user's geographical location information and select a notification method suitable for that location. The notification unit can also send highly relevant notifications based on the user's geographical location information. For example, the notification unit can evaluate the user's geographical location information and send highly relevant notifications. Furthermore, if the user is on the move, the notification unit can select a notification method relevant to their current location. For example, the notification unit can evaluate the user's geographical location information and select a notification method relevant to their current location. This allows for more appropriate notifications by selecting the optimal notification method based on the user's geographical location information. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input geographical location data into a generating AI and have the generating AI select the notification method.

[0049] The notification unit can analyze the user's social media activity and propose a notification method when sending a notification. For example, the notification unit can propose the optimal notification method based on information shared by the user on social media. For example, the notification unit can evaluate the user's social media activity and propose the optimal notification method. The notification unit can also select an effective notification method from the user's social media activity. For example, the notification unit can evaluate the user's social media activity and select an effective notification method. Furthermore, the notification unit can analyze the user's social media activity and propose a highly relevant notification method. For example, the notification unit can evaluate the user's social media activity and propose a highly relevant notification method. This enables more effective notifications by proposing the optimal notification method based on the user's social media activity. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input social media activity data into a generating AI and have the generating AI propose a notification method.

[0050] The implementation unit can select the optimal implementation method by referring to the user's past implementation history during implementation. For example, the implementation unit can prioritize the implementation method that the user has preferred to use in the past. For example, the implementation unit can evaluate the user's past implementation history and select the preferred implementation method. The implementation unit can also predict the optimal implementation timing based on the user's past implementation history. For example, the implementation unit can predict the optimal implementation timing based on past implementation history. Furthermore, the implementation unit can analyze the user's past implementation history and propose an effective implementation method. For example, the implementation unit can evaluate past implementation history and propose an effective implementation method. This allows the optimal implementation method to be selected by referring to the user's past implementation history. Some or all of the above processing in the implementation unit may be performed using AI, for example, or without AI. For example, the implementation unit can input past implementation history data into a generating AI and have the generating AI perform the selection of the implementation method.

[0051] The implementation unit can customize the implementation method based on the user's current work situation during implementation. For example, if the user is busy, the implementation unit can simplify the implementation procedure. For example, the implementation unit can assess the user's work situation and simplify the implementation procedure if the user is busy. The implementation unit can also provide a detailed implementation procedure if the user is relaxed. For example, the implementation unit can assess the user's work situation and provide a detailed implementation procedure if the user is relaxed. Furthermore, the implementation unit can dynamically adjust the optimal implementation method according to the user's work situation. For example, the implementation unit can assess the user's work situation and dynamically adjust the optimal implementation method. This allows for a more appropriate implementation by customizing the implementation method according to the user's work situation. Some or all of the above processing in the implementation unit may be performed using AI, for example, or without AI. For example, the implementation unit can input work situation data into a generating AI and have the generating AI perform the customization of the implementation method.

[0052] The implementation unit can select an appropriate implementation method based on the user's geographical location information during implementation. For example, if the user is in a specific location, the implementation unit can select an implementation method suitable for that location. For example, the implementation unit can evaluate the user's geographical location information and select an implementation method suitable for that location. The implementation unit can also perform highly relevant implementations based on the user's geographical location information. For example, the implementation unit can evaluate the user's geographical location information and perform highly relevant implementations. Furthermore, if the user is on the move, the implementation unit can select an implementation method relevant to their current location. For example, the implementation unit can evaluate the user's geographical location information and select an implementation method relevant to their current location. This allows for more appropriate implementation by selecting the optimal implementation method based on the user's geographical location information. Some or all of the above processing in the implementation unit may be performed using AI, for example, or without AI. For example, the implementation unit can input geographical location data into a generating AI and have the generating AI perform the selection of the implementation method.

[0053] The introduction unit can analyze the user's social media activity and propose implementation methods during the implementation phase. For example, the introduction unit can propose the optimal implementation method based on information shared by the user on social media. For example, the introduction unit can evaluate the user's social media activity and propose the optimal implementation method. The introduction unit can also select effective implementation methods from the user's social media activity. For example, the introduction unit can evaluate the user's social media activity and select effective implementation methods. Furthermore, the introduction unit can analyze the user's social media activity and propose highly relevant implementation methods. For example, the introduction unit can evaluate the user's social media activity and propose highly relevant implementation methods. This enables more effective implementation by proposing the optimal implementation method based on the user's social media activity. Some or all of the above processing in the introduction unit may be performed using AI, for example, or without AI. For example, the introduction unit can input social media activity data into a generating AI and have the generating AI execute the proposal of implementation methods.

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

[0055] The data collection unit can analyze the user's device usage patterns to determine the optimal timing for collecting user operation logs. For example, the unit can learn when a user typically uses their device and concentrate data collection during those times. It can also analyze how often a user uses a specific application and collect data when that application is being used. Furthermore, the unit can dynamically adjust the frequency and method of data collection based on the user's device usage patterns. This enables efficient data collection based on the user's device usage patterns.

[0056] The discrimination unit can adjust its discrimination criteria based on the skill level of the person performing the operation. For example, if the person performing the operation is a novice, the discrimination unit can perform discrimination using strict criteria. Conversely, if the person performing the operation is an expert, the discrimination unit can perform discrimination using flexible criteria. Furthermore, the discrimination unit can dynamically adjust the discrimination criteria according to the skill level of the person performing the operation. This enables appropriate discrimination based on the skill level of the person performing the operation.

[0057] The notification unit can analyze the user's past notification history and select the optimal notification method. For example, it can prioritize notification methods that the user has preferred using in the past. Furthermore, the notification unit can predict the optimal notification timing based on the user's past notification history. In addition, the notification unit can analyze the user's past notification history and suggest effective notification methods. This allows the system to select the optimal notification method by referring to the user's past notification history.

[0058] The data collection unit can analyze the user's device usage patterns to determine the optimal timing for collecting user operation logs. For example, the unit can learn when a user typically uses their device and concentrate data collection during those times. It can also analyze how often a user uses a specific application and collect data when that application is being used. Furthermore, the unit can dynamically adjust the frequency and method of data collection based on the user's device usage patterns. This enables efficient data collection based on the user's device usage patterns.

[0059] The discrimination unit can adjust its discrimination criteria based on the skill level of the person performing the operation. For example, if the person performing the operation is a novice, the discrimination unit can perform discrimination using strict criteria. Conversely, if the person performing the operation is an expert, the discrimination unit can perform discrimination using flexible criteria. Furthermore, the discrimination unit can dynamically adjust the discrimination criteria according to the skill level of the person performing the operation. This enables appropriate discrimination based on the skill level of the person performing the operation.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The data collection unit collects PC operation logs. The data collection unit collects detailed operation logs, such as user clicks and keyboard inputs. For example, the data collection unit can record user clicks and identify which applications were used. The data collection unit can also record keyboard input and analyze the content of the entered text. Furthermore, the data collection unit can take screen captures to visually record user operations. Step 2: The analysis unit analyzes the operation logs collected by the collection unit and learns the nature of the work. For example, the analysis unit analyzes whether certain operations are performed repeatedly. For instance, the analysis unit can detect that data entry operations are performed frequently and identify this as a routine task. The analysis unit can also detect that a particular application is used frequently and identify the tasks associated with that application. Furthermore, the analysis unit can analyze the sequence and timing of operations to understand the workflow. Step 3: The discrimination unit identifies tasks that can be automated based on what the analysis unit has learned. The discrimination unit identifies tasks that involve a lot of routine work, such as data entry and the creation of standardized reports. For example, the discrimination unit can detect that data entry operations are performed repeatedly and identify this as a task that can be automated. The discrimination unit can also detect that standardized report creation operations are performed frequently and identify this as a task that can be automated. Furthermore, the discrimination unit can detect that a particular application is used frequently and identify tasks related to that application as tasks that can be automated. Step 4: The generation unit generates code to automate the tasks identified by the discrimination unit. The generation unit generates automation scripts using programming languages ​​such as Python or VBA. For example, the generation unit can generate a Python script to automate data entry operations. It can also generate a VBA macro to automate routine report creation operations. Furthermore, the generation unit can generate scripts to automate operations related to specific applications. Step 5: The notification unit notifies the user of the code generated by the generation unit on the PC. The notification unit prompts the user to install the code in the form of, for example, a pop-up notification or an email notification. For example, the notification unit can display a pop-up notification and instruct the user to install the generated code. Alternatively, the notification unit can send an email notification and instruct the user to install the generated code. Furthermore, the notification unit can notify the user through a chat application and instruct the user to install the generated code. Step 6: The implementation unit installs the code notified by the notification unit onto the PC. The implementation unit completes the automation of routine tasks, for example, by having the user execute the code according to the instructions. For example, the implementation unit can execute a generated Python script to automate data entry operations. It can also execute a generated VBA macro to automate routine report creation operations. Furthermore, the implementation unit can execute a script related to a specific application to automate operations related to that application.

[0062] (Example of form 2) The automation system according to an embodiment of the present invention is a system that uses AI to learn PC operations and automates simple, repetitive tasks (routine tasks). This automation system installs dedicated software on the PC and collects PC operation logs when the user performs their work as usual, understanding and learning the content of the work. Based on the learned content, it identifies tasks that can be automated and generates automation code such as Python or VBA. The generated code is notified on the PC, and the user implements the code according to the instructions. This completes the automation of routine tasks, leading to a reduction in man-hours and a reduction in human error. For example, a collection unit is provided to collect PC operation logs. This collection unit collects PC operation logs when the user performs their work as usual. For example, it collects detailed operation logs such as click operations and keyboard inputs performed by the user. Next, an analysis unit is provided to analyze the collected operation logs. This analysis unit analyzes the operation logs collected by the collection unit and learns the content of the work. For example, it analyzes whether a specific operation is performed repeatedly and identifies routine tasks. Furthermore, a discrimination unit is provided to determine tasks that can be automated based on the content learned by the analysis unit. This discrimination unit identifies tasks that can be automated based on the information learned by the analysis unit. For example, it identifies tasks that involve a lot of routine work, such as data entry and the creation of standardized reports. A generation unit is provided to generate code for automation based on the identified tasks. This generation unit generates code to automate the tasks identified by the discrimination unit. For example, it generates automation scripts using programming languages ​​such as Python or VBA. A notification unit is provided to notify the PC of the generated code. This notification unit notifies the PC of the code generated by the generation unit. For example, it prompts the user to install the code in the form of a pop-up notification or email notification. Finally, an installation unit is provided to install the code based on the notification. This installation unit installs the code notified by the notification unit onto the PC. For example, by executing the code according to the instructions, the automation of the routine task is completed. In this way, the automation system can train the AI ​​to learn PC operations and automate simple, repetitive tasks.

[0063] The automation system according to the embodiment comprises a collection unit, an analysis unit, a discrimination unit, a generation unit, a notification unit, and an introduction unit. The collection unit collects PC operation logs. The collection unit collects detailed operation logs, such as click operations and keyboard inputs performed by the user. For example, the collection unit can record click operations performed by the user and identify which application was used. The collection unit can also record keyboard inputs and analyze the content of the entered text. Furthermore, the collection unit can perform screen captures and visually record the user's operations. The analysis unit analyzes the operation logs collected by the collection unit and learns the content of the work. For example, the analysis unit analyzes whether a particular operation is performed repeatedly. For example, the analysis unit can detect that data entry operations are performed frequently and identify this as a routine task. The analysis unit can also detect that a particular application is used frequently and identify the tasks related to that application. Furthermore, the analysis unit can analyze the order and timing of operations and grasp the flow of the work. The discrimination unit determines which tasks can be automated based on the content learned by the analysis unit. The discrimination unit identifies tasks that involve a lot of routine work, such as data entry or the creation of standardized reports. For example, the discrimination unit can detect that data entry operations are performed repeatedly and identify them as tasks that can be automated. It can also detect that standardized report creation operations are performed frequently and identify them as tasks that can be automated. Furthermore, the discrimination unit can detect that a particular application is used frequently and identify tasks related to that application as tasks that can be automated. The generation unit generates code to automate the tasks identified by the discrimination unit. The generation unit generates automation scripts using programming languages ​​such as Python or VBA. For example, the generation unit can generate a Python script to automate data entry operations. It can also generate a VBA macro to automate standardized report creation operations.Furthermore, the generation unit can also generate scripts to automate operations related to a specific application. The notification unit notifies the PC of the code generated by the generation unit. The notification unit prompts the user to install the code, for example, in the form of a pop-up notification or email notification. For example, the notification unit can display a pop-up notification and instruct the user to install the generated code. The notification unit can also send an email notification and instruct the user to install the generated code. Furthermore, the notification unit can notify the user through a chat application and instruct the user to install the generated code. The installation unit installs the code notified by the notification unit onto the PC. The installation unit completes the automation of routine tasks, for example, by having the user execute the code according to the instructions. For example, the installation unit can execute a generated Python script to automate data entry operations. The installation unit can also execute a generated VBA macro to automate routine report creation operations. Furthermore, the installation unit can execute a script related to a specific application and automate operations related to that application. As a result, the automation system according to the embodiment can train the AI ​​on PC operations and automate simple, repetitive tasks.

[0064] 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 temporarily stop collecting operation logs and resume collection when the user is relaxed. For example, the data collection unit can capture the user's facial expressions with a camera and estimate the user's emotions using an emotion estimation algorithm. The data collection unit can also increase the frequency of operation log collection to obtain more detailed data when the user is concentrating. For example, the data collection unit can record the user's voice and estimate the user's emotions using voice analysis technology. Furthermore, if the user is tired, the data collection unit can minimize operation log collection to reduce the user's burden. For example, the data collection unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate the user's emotions using an emotion estimation algorithm. This allows for more appropriate data collection by adjusting the timing of operation log collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the collection unit may be performed using AI, or not using AI. For example, the collection unit can input user image data captured by a camera into the generation AI and have the generation AI perform the estimation of the user's emotions.

[0065] The data collection unit can analyze the user's past operation history and select an appropriate collection method. For example, the data collection unit can prioritize collecting operations that the user has frequently performed in the past. For example, the data collection unit can analyze past operation history and identify frequently performed operations. The data collection unit can also predict operations performed during specific time periods based on the user's past operation history and concentrate data collection during those times. For example, the data collection unit can predict operations performed during specific time periods based on past operation history and perform data collection during those times. Furthermore, the data collection unit can analyze the user's past operation patterns and propose efficient collection methods. For example, the data collection unit can analyze past operation patterns and identify efficient collection methods. This enables efficient data collection by analyzing past operation history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past operation history data into a generating AI and have the generating AI select an efficient collection method.

[0066] The data collection unit can filter operation logs based on the user's current work status and areas of interest when collecting them. For example, the data collection unit can collect only operation logs related to the tasks the user is currently performing. For example, the data collection unit can analyze the current work status and identify relevant operation logs. The data collection unit can also prioritize the collection of highly relevant operation logs based on the user's areas of interest. For example, the data collection unit can analyze the user's areas of interest and identify relevant operation logs. Furthermore, the data collection unit can filter out unnecessary operation logs according to the user's work status, enabling efficient data collection. For example, the data collection unit can analyze the current work status and filter out unnecessary operation logs. This allows for efficient data collection by filtering based on the user's work status and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input current work status data into a generating AI and have the generating AI perform the filtering.

[0067] 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 postpone the collection of low-priority operation logs. For example, the data collection unit can capture the user's facial expressions with a camera and estimate the user's emotions using an emotion estimation algorithm. Also, if the user is relaxed, the data collection unit can prioritize the collection of detailed operation logs. For example, the data collection unit can record the user's voice and estimate the user's emotions using voice analysis technology. Furthermore, if the user is in a hurry, the data collection unit can prioritize the collection of high-priority operation logs. For example, the data collection unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate the user's emotions using an emotion estimation algorithm. This allows for the priority of operation logs to be determined according to the user's emotions, thereby prioritizing the collection of important data. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the collection unit may be performed using AI, or not using AI. For example, the collection unit can input user image data captured by a camera into the generation AI and have the generation AI perform the estimation of the user's emotions.

[0068] The data collection unit can prioritize the collection of highly relevant logs based on the user's geographical location information when collecting operation logs. For example, the data collection unit can prioritize the collection of operation logs performed by the user at a specific location. For example, the data collection unit can analyze the user's geographical location information and identify operation logs performed at that location. The data collection unit can also filter highly relevant operation logs based on the user's geographical location information. For example, the data collection unit can identify relevant operation logs based on the user's current location. Furthermore, if the user is on the move, the data collection unit can prioritize the collection of operation logs related to the current location. For example, the data collection unit can analyze the user's movement pattern and identify relevant operation logs. This enables efficient data collection by collecting highly relevant logs based on the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input geographical location data into a generating AI and have the generating AI collect highly relevant logs.

[0069] The data collection unit can analyze a user's social media activity and collect relevant logs when collecting operation logs. For example, the data collection unit can collect relevant operation logs based on information shared by the user on social media. For example, the data collection unit can analyze a user's social media activity and identify relevant operation logs. The data collection unit can also prioritize the collection of work-related operation logs from the user's social media activity. For example, the data collection unit can analyze a user's social media posts and identify relevant operation logs. Furthermore, the data collection unit can analyze a user's social media activity and filter out work-related operation logs. For example, the data collection unit can identify relevant operation logs based on the user's social media activity. This enables efficient data collection by collecting relevant logs based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media activity data into a generating AI and have the generating AI collect relevant logs.

[0070] 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 tense, the analysis unit can provide simple and easy-to-understand analysis results. For example, the analysis unit can capture the user's facial expressions with a camera and estimate the user's emotions using an emotion estimation algorithm. The analysis unit can also provide detailed analysis results if the user is relaxed. For example, the analysis unit can record the user's voice and estimate the user's emotions using voice analysis technology. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results. For example, the analysis unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate the user's emotions using an emotion estimation algorithm. This allows for more appropriate analysis results to be provided by adjusting the presentation of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0071] The analysis unit can adjust the level of detail of the analysis based on the importance of the operations during the analysis. For example, the analysis unit can perform a detailed analysis for operations with high importance. For example, the analysis unit can evaluate the importance of operations and perform a detailed analysis for operations with high importance. The analysis unit can also perform a simplified analysis for operations with low importance. For example, the analysis unit can evaluate the importance of operations and perform a simplified analysis for operations with low importance. Furthermore, the analysis unit can dynamically adjust the level of detail of the analysis according to the importance of the operations. For example, the analysis unit can evaluate the importance of operations and adjust the level of detail of the analysis according to the importance of the operations. This makes efficient analysis possible by adjusting the level of detail of the analysis according to the importance of the operations. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input operation importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0072] The analysis unit can apply an appropriate analysis algorithm depending on the category of the operation during analysis. For example, the analysis unit can apply a specific analysis algorithm to data input operations. For instance, the analysis unit can apply a clustering algorithm to data input operations to analyze data patterns. The analysis unit can also apply a different analysis algorithm to report creation operations. For example, the analysis unit can apply regression analysis to report creation operations to analyze data trends. Furthermore, the analysis unit can select the optimal analysis algorithm depending on the category of the operation. For example, the analysis unit can evaluate the category of the operation and select the optimal analysis algorithm. This improves the accuracy of the analysis by applying the optimal analysis algorithm according to the category of the operation. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the category data of the operation into a generating AI and have the generating AI select the optimal analysis algorithm.

[0073] 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 in a hurry, the analysis unit can provide a short, concise analysis. For example, the analysis unit can capture the user's facial expressions with a camera and estimate the user's emotions using an emotion estimation algorithm. The analysis unit can also provide detailed analysis results if the user is relaxed. For example, the analysis unit can record the user's voice and estimate the user's emotions using voice analysis technology. Furthermore, if the user is excited, the analysis unit can provide visually stimulating analysis results. For example, the analysis unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate the user's emotions using an emotion estimation algorithm. This allows for more appropriate analysis results by adjusting the length of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0074] The analysis unit can determine the priority of the analysis based on the timing of the operations during the analysis. For example, the analysis unit can prioritize the analysis of recently performed operations. For example, the analysis unit can evaluate the timing of the operations and prioritize the analysis of recently performed operations. The analysis unit can also prioritize the analysis of operations that have been frequently performed in the past. For example, the analysis unit can evaluate the timing of the operations and prioritize the analysis of operations that have been frequently performed in the past. Furthermore, the analysis unit can dynamically adjust the priority of the analysis based on the timing of the operations. For example, the analysis unit can evaluate the timing of the operations and dynamically adjust the priority. This enables efficient analysis by determining the priority of the analysis based on the timing of the operations. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input operation timing data into a generating AI and have the generating AI determine the priority of the analysis.

[0075] The analysis unit can adjust the order of analysis based on the relevance of operations during analysis. For example, the analysis unit can prioritize the analysis of operations with high relevance. For example, the analysis unit can evaluate the relevance of operations and prioritize the analysis of operations with high relevance. The analysis unit can also postpone the analysis of operations with low relevance. For example, the analysis unit can evaluate the relevance of operations and postpone the analysis of operations with low relevance. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of operations. For example, the analysis unit can evaluate the relevance of operations and dynamically adjust the order. This allows for efficient analysis by adjusting the order of analysis based on the relevance of operations. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input operation relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0076] The discrimination unit can estimate the user's emotions and adjust the discrimination criteria based on the estimated emotions. For example, if the user is tense, the discrimination unit can make a judgment using strict criteria. For example, the discrimination unit can capture the user's facial expression with a camera and estimate the user's emotions using an emotion estimation algorithm. The discrimination unit can also make a judgment using flexible criteria if the user is relaxed. For example, the discrimination unit can record the user's voice and estimate the user's emotions using voice analysis technology. Furthermore, if the user is in a hurry, the discrimination unit can set criteria for quick discrimination. For example, the discrimination unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate the user's emotions using an emotion estimation algorithm. This allows for more appropriate discrimination by adjusting the discrimination criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the discrimination unit may be performed using AI, for example, or without AI. For example, the discrimination unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0077] The discrimination unit can improve the accuracy of its discrimination based on the interrelationships of operations during discrimination. For example, the discrimination unit can analyze the interrelationships of operations and discriminate related operations collectively. For example, the discrimination unit can evaluate the interrelationships of operations and discriminate related operations collectively. Furthermore, the discrimination unit can improve the accuracy of its discrimination based on the interrelationships of operations. For example, the discrimination unit can evaluate the interrelationships of operations and improve the accuracy of its discrimination. In addition, the discrimination unit can prevent misclassification by considering the interrelationships of operations. For example, the discrimination unit can evaluate the interrelationships of operations and prevent misclassification. As a result, by improving the accuracy of discrimination based on the interrelationships of operations, more accurate discrimination becomes possible. Some or all of the above processing in the discrimination unit may be performed using AI, for example, or without using AI. For example, the discrimination unit can input the interrelationship data of operations into a generating AI and have the generating AI perform the improvement of discrimination accuracy.

[0078] The discrimination unit can perform discrimination based on the attribute information of the person performing the operation. For example, the discrimination unit can perform discrimination based on the position and job duties of the person performing the operation. For example, the discrimination unit can evaluate the attribute information of the person performing the operation and perform discrimination based on their position and job duties. The discrimination unit can also perform discrimination by referring to the past work history of the person performing the operation. For example, the discrimination unit can evaluate the past work history of the person performing the operation and perform discrimination. Furthermore, the discrimination unit can adjust the discrimination criteria according to the skill level of the person performing the operation. For example, the discrimination unit can evaluate the skill level of the person performing the operation and adjust the criteria. This makes it possible to perform more appropriate discrimination by considering the attribute information of the person performing the operation. Some or all of the above processing in the discrimination unit may be performed using AI, for example, or without using AI. For example, the discrimination unit can input the attribute information data of the person performing the operation into a generating AI and have the generating AI perform discrimination.

[0079] The discrimination unit can estimate the user's emotions and adjust the order in which the discrimination results are displayed based on the estimated emotions. For example, if the user is tense, the discrimination unit can display important results first. For example, the discrimination unit can capture the user's facial expressions with a camera and estimate the user's emotions using an emotion estimation algorithm. Also, if the user is relaxed, the discrimination unit can display detailed results sequentially. For example, the discrimination unit can record the user's voice and estimate the user's emotions using voice analysis technology. Furthermore, if the user is in a hurry, the discrimination unit can display results that summarize the key points first. For example, the discrimination unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate the user's emotions using an emotion estimation algorithm. This allows for more appropriate result display by adjusting the order in which the discrimination results are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the discrimination unit may be performed using AI, for example, or without AI. For example, the discrimination unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0080] The discrimination unit can perform discrimination based on the geographical distribution of operations. For example, the discrimination unit can perform discrimination based on the location where the operation was performed. For example, the discrimination unit can evaluate the geographical distribution of operations and perform discrimination based on the location. The discrimination unit can also discriminate related operations collectively based on geographical distribution. For example, the discrimination unit can evaluate the geographical distribution of operations and discriminate related operations collectively. Furthermore, the discrimination unit can prevent misclassification by considering geographical distribution. For example, the discrimination unit can evaluate the geographical distribution of operations and prevent misclassification. This makes it possible to perform more appropriate discrimination by performing discrimination based on the geographical distribution of operations. Some or all of the above processing in the discrimination unit may be performed using AI, for example, or without using AI. For example, the discrimination unit can input geographical distribution data of operations into a generating AI and have the generating AI perform the discrimination.

[0081] The discrimination unit can improve the accuracy of its discrimination based on relevant literature for the operation during discrimination. For example, the discrimination unit can improve the accuracy of its discrimination by referring to relevant literature for the operation. For example, the discrimination unit can improve the accuracy of its discrimination by evaluating relevant literature for the operation. The discrimination unit can also set discrimination criteria based on relevant literature. For example, the discrimination unit can evaluate relevant literature and set criteria. Furthermore, the discrimination unit can prevent misclassification by referring to relevant literature for the operation. For example, the discrimination unit can evaluate relevant literature for the operation and prevent misclassification. As a result, the accuracy of discrimination is improved by referring to relevant literature for the operation. Some or all of the above processing in the discrimination unit may be performed using AI, for example, or without AI. For example, the discrimination unit can input relevant literature data for the operation into a generating AI and have the generating AI perform the improvement of discrimination accuracy.

[0082] The generation unit can estimate the user's emotions and determine the priority of the code to generate based on the estimated emotions. For example, if the user is in a hurry, the generation unit will prioritize generating high-priority code. For example, the generation unit can capture the user's facial expressions with a camera and estimate the user's emotions using an emotion estimation algorithm. The generation unit can also prioritize generating detailed code if the user is relaxed. For example, the generation unit can record the user's voice and estimate the user's emotions using voice analysis technology. Furthermore, if the user is excited, the generation unit can generate code with visually stimulating effects. For example, the generation unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate the user's emotions using an emotion estimation algorithm. This allows the generation unit to prioritize important code by determining the priority of the code according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user image data captured by a camera into a generation AI and have the generation AI perform the estimation of the user's emotions.

[0083] The generation unit can improve the accuracy of code generation based on the interrelationships of operations. For example, the generation unit can analyze the interrelationships of operations and code related operations together. For example, the generation unit can evaluate the interrelationships of operations and code related operations together. The generation unit can also improve the accuracy of generation based on the interrelationships of operations. For example, the generation unit can evaluate the interrelationships of operations and improve the accuracy of generation. Furthermore, the generation unit can prevent erroneous generation by considering the interrelationships of operations. For example, the generation unit can evaluate the interrelationships of operations and prevent erroneous generation. This makes it possible to generate more accurate code by improving the accuracy of generation based on the interrelationships of operations. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data on the interrelationships of operations into a generation AI and have the generation AI perform the improvement of generation accuracy.

[0084] The generation unit can generate code based on the attribute information of the person performing the operation. For example, the generation unit can generate code based on the operator's job title and job duties. For example, the generation unit can evaluate the operator's attribute information and generate code based on their job title and job duties. The generation unit can also generate code by referring to the operator's past work history. For example, the generation unit can evaluate the operator's past work history and generate code. Furthermore, the generation unit can adjust the level of detail of the code according to the operator's skill level. For example, the generation unit can evaluate the operator's skill level and adjust the level of detail. This makes it possible to generate more appropriate code by considering the operator's attribute information. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the operator's attribute information data into a generation AI and have the generation AI perform the generation.

[0085] The generation unit can estimate the user's emotions and adjust the display method of the generated code based on the estimated user emotions. For example, if the user is tense, the generation unit can provide a simple and highly visible display method. For example, the generation unit can capture the user's facial expression with a camera and estimate the user's emotions using an emotion estimation algorithm. The generation unit can also provide a display method that includes detailed information if the user is relaxed. For example, the generation unit can record the user's voice and estimate the user's emotions using voice analysis technology. Furthermore, if the user is in a hurry, the generation unit can provide a concise display method. For example, the generation unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate the user's emotions using an emotion estimation algorithm. This allows for more appropriate display by adjusting the code display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user image data captured by a camera into a generation AI and have the generation AI perform the estimation of the user's emotions.

[0086] The generation unit can perform code generation based on the geographical distribution of operations. For example, the generation unit can generate code based on the location where the operation was performed. For example, the generation unit can evaluate the geographical distribution of operations and generate code based on the location. The generation unit can also code related operations in a batch based on their geographical distribution. For example, the generation unit can evaluate the geographical distribution of operations and code related operations in a batch. Furthermore, the generation unit can prevent erroneous generation by considering the geographical distribution. For example, the generation unit can evaluate the geographical distribution of operations and prevent erroneous generation. This makes it possible to generate more appropriate code by performing generation based on the geographical distribution of operations. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input geographical distribution data of operations into a generation AI and have the generation AI perform the generation.

[0087] The generation unit can improve the accuracy of code generation by referring to relevant documentation for the operation. For example, the generation unit can improve the accuracy of generation by referring to relevant documentation for the operation. For example, the generation unit can improve the accuracy of generation by evaluating relevant documentation for the operation. The generation unit can also set generation criteria based on relevant documentation. For example, the generation unit can evaluate relevant documentation and set criteria. Furthermore, the generation unit can prevent erroneous generation by referring to relevant documentation for the operation. For example, the generation unit can evaluate relevant documentation for the operation and prevent erroneous generation. As a result, the accuracy of generation is improved by referring to relevant documentation for the operation. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input relevant documentation data for the operation into a generation AI and have the generation AI perform the improvement of generation accuracy.

[0088] The notification unit can estimate the user's emotions and adjust the notification method based on the estimated emotions. For example, if the user is tense, the notification unit can send a notification in a calm tone. For example, the notification unit can capture the user's facial expression with a camera and estimate the user's emotions using an emotion estimation algorithm. The notification unit can also send a notification in a bright tone if the user is relaxed. For example, the notification unit can record the user's voice and estimate the user's emotions using voice analysis technology. Furthermore, if the user is in a hurry, the notification unit can send a quick and concise notification. For example, the notification unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate the user's emotions using an emotion estimation algorithm. This allows for more appropriate notifications by adjusting the notification method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input user image data captured by the camera into a generating AI and have the generating AI perform an estimation of the user's emotions.

[0089] The notification unit can select an appropriate notification method based on the user's past notification history when a notification is sent. For example, the notification unit can prioritize notification methods that the user has preferred to use in the past. For example, the notification unit can evaluate the user's past notification history and select preferred notification methods. The notification unit can also predict the optimal notification timing based on the user's past notification history. For example, the notification unit can predict the optimal notification timing based on past notification history. Furthermore, the notification unit can analyze the user's past notification history and suggest effective notification methods. For example, the notification unit can evaluate past notification history and suggest effective notification methods. This allows the notification unit to select the optimal notification method by referring to the user's past notification history. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input past notification history data into a generating AI and have the generating AI select the notification method.

[0090] The notification unit can adjust the timing of notifications based on the user's current work situation. For example, if the user is busy, the notification unit can postpone the notification. For example, the notification unit can assess the user's work situation and postpone the notification if the user is busy. The notification unit can also send an immediate notification if the user is relaxed. For example, the notification unit can assess the user's work situation and send an immediate notification if the user is relaxed. Furthermore, the notification unit can dynamically adjust the optimal notification timing according to the user's work situation. For example, the notification unit can assess the user's work situation and dynamically adjust the optimal notification timing. This allows for more appropriate notifications by adjusting the timing of notifications according to the user's work situation. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input work situation data into a generating AI and have the generating AI perform the adjustment of the notification timing.

[0091] The notification unit can estimate the user's emotions and determine notification priorities based on those emotions. For example, if the user is stressed, the notification unit will prioritize high-priority notifications. For instance, the notification unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. Furthermore, if the user is relaxed, the notification unit can prioritize detailed notifications. For example, the notification unit can record the user's voice and estimate their emotions using voice analysis technology. Additionally, if the user is in a hurry, the notification unit can prioritize concise notifications. For example, the notification unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the notification unit to prioritize important notifications based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input user image data captured by the camera into a generating AI and have the generating AI perform an estimation of the user's emotions.

[0092] The notification unit can select an appropriate notification method based on the user's geographical location information when sending a notification. For example, if the user is in a specific location, the notification unit can select a notification method suitable for that location. For example, the notification unit can evaluate the user's geographical location information and select a notification method suitable for that location. The notification unit can also send highly relevant notifications based on the user's geographical location information. For example, the notification unit can evaluate the user's geographical location information and send highly relevant notifications. Furthermore, if the user is on the move, the notification unit can select a notification method relevant to their current location. For example, the notification unit can evaluate the user's geographical location information and select a notification method relevant to their current location. This allows for more appropriate notifications by selecting the optimal notification method based on the user's geographical location information. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input geographical location data into a generating AI and have the generating AI select the notification method.

[0093] The notification unit can analyze the user's social media activity and propose a notification method when sending a notification. For example, the notification unit can propose the optimal notification method based on information shared by the user on social media. For example, the notification unit can evaluate the user's social media activity and propose the optimal notification method. The notification unit can also select an effective notification method from the user's social media activity. For example, the notification unit can evaluate the user's social media activity and select an effective notification method. Furthermore, the notification unit can analyze the user's social media activity and propose a highly relevant notification method. For example, the notification unit can evaluate the user's social media activity and propose a highly relevant notification method. This enables more effective notifications by proposing the optimal notification method based on the user's social media activity. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input social media activity data into a generating AI and have the generating AI propose a notification method.

[0094] The introduction unit can estimate the user's emotions and adjust the introduction method based on the estimated emotions. For example, if the user is nervous, the introduction unit can provide a simple and highly visible introduction method. For example, the introduction unit can capture the user's facial expressions with a camera and estimate the user's emotions using an emotion estimation algorithm. The introduction unit can also provide detailed introduction procedures if the user is relaxed. For example, the introduction unit can record the user's voice and estimate the user's emotions using voice analysis technology. Furthermore, if the user is in a hurry, the introduction unit can provide a method that allows for quick introduction. For example, the introduction unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate the user's emotions using an emotion estimation algorithm. This allows for a more appropriate introduction by adjusting the introduction method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the introduction may be performed using AI, for example, or without AI. For example, the introduction can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0095] The implementation unit can select the optimal implementation method by referring to the user's past implementation history during implementation. For example, the implementation unit can prioritize the implementation method that the user has preferred to use in the past. For example, the implementation unit can evaluate the user's past implementation history and select the preferred implementation method. The implementation unit can also predict the optimal implementation timing based on the user's past implementation history. For example, the implementation unit can predict the optimal implementation timing based on past implementation history. Furthermore, the implementation unit can analyze the user's past implementation history and propose an effective implementation method. For example, the implementation unit can evaluate past implementation history and propose an effective implementation method. This allows the optimal implementation method to be selected by referring to the user's past implementation history. Some or all of the above processing in the implementation unit may be performed using AI, for example, or without AI. For example, the implementation unit can input past implementation history data into a generating AI and have the generating AI perform the selection of the implementation method.

[0096] The implementation unit can customize the implementation method based on the user's current work situation during implementation. For example, if the user is busy, the implementation unit can simplify the implementation procedure. For example, the implementation unit can assess the user's work situation and simplify the implementation procedure if the user is busy. The implementation unit can also provide a detailed implementation procedure if the user is relaxed. For example, the implementation unit can assess the user's work situation and provide a detailed implementation procedure if the user is relaxed. Furthermore, the implementation unit can dynamically adjust the optimal implementation method according to the user's work situation. For example, the implementation unit can assess the user's work situation and dynamically adjust the optimal implementation method. This allows for a more appropriate implementation by customizing the implementation method according to the user's work situation. Some or all of the above processing in the implementation unit may be performed using AI, for example, or without AI. For example, the implementation unit can input work situation data into a generating AI and have the generating AI perform the customization of the implementation method.

[0097] The introduction can estimate the user's emotions and determine the priority of the introduction based on those emotions. For example, if the user is nervous, the introduction can prioritize high-importance introductions. For instance, the introduction can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. Furthermore, if the user is relaxed, the introduction can prioritize detailed introductions. For example, the introduction can record the user's voice and estimate their emotions using voice analysis technology. Additionally, if the user is in a hurry, the introduction can prioritize concise introductions. For example, the introduction can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows for prioritizing important introductions based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the introduction may be performed using AI, for example, or without AI. For example, the introduction can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0098] The implementation unit can select an appropriate implementation method based on the user's geographical location information during implementation. For example, if the user is in a specific location, the implementation unit can select an implementation method suitable for that location. For example, the implementation unit can evaluate the user's geographical location information and select an implementation method suitable for that location. The implementation unit can also perform highly relevant implementations based on the user's geographical location information. For example, the implementation unit can evaluate the user's geographical location information and perform highly relevant implementations. Furthermore, if the user is on the move, the implementation unit can select an implementation method relevant to their current location. For example, the implementation unit can evaluate the user's geographical location information and select an implementation method relevant to their current location. This allows for more appropriate implementation by selecting the optimal implementation method based on the user's geographical location information. Some or all of the above processing in the implementation unit may be performed using AI, for example, or without AI. For example, the implementation unit can input geographical location data into a generating AI and have the generating AI perform the selection of the implementation method.

[0099] The introduction unit can analyze the user's social media activity and propose implementation methods during the implementation phase. For example, the introduction unit can propose the optimal implementation method based on information shared by the user on social media. For example, the introduction unit can evaluate the user's social media activity and propose the optimal implementation method. The introduction unit can also select effective implementation methods from the user's social media activity. For example, the introduction unit can evaluate the user's social media activity and select effective implementation methods. Furthermore, the introduction unit can analyze the user's social media activity and propose highly relevant implementation methods. For example, the introduction unit can evaluate the user's social media activity and propose highly relevant implementation methods. This enables more effective implementation by proposing the optimal implementation method based on the user's social media activity. Some or all of the above processing in the introduction unit may be performed using AI, for example, or without AI. For example, the introduction unit can input social media activity data into a generating AI and have the generating AI execute the proposal of implementation methods. === Hard Collateral 1-1 === Each of the multiple elements described above, including the collection unit, analysis unit, discrimination unit, generation unit, notification unit, and introduction unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 and collects PC operation logs. The analysis unit is implemented by the identification processing unit 290 of the data processing device 12 and analyzes the collected operation logs. The discrimination unit is implemented by the identification processing unit 290 of the data processing device 12 and determines which tasks can be automated based on the analyzed content. The generation unit is implemented by the identification processing unit 290 of the data processing device 12 and generates code for automating the determined tasks. The notification unit is implemented by the control unit 46A of the smart device 14 and notifies the generated code on the PC. The introduction unit is implemented by the control unit 46A of the smart device 14 and introduces the notified code to the PC. === Hard Collateral 1-2 === Each of the multiple elements described above, including the collection unit, analysis unit, discrimination unit, generation unit, notification unit, and introduction unit, is implemented in at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214 and collects PC operation logs. The analysis unit is implemented by the identification processing unit 290 of the data processing device 12 and analyzes the collected operation logs. The discrimination unit is implemented by the identification processing unit 290 of the data processing device 12 and determines which tasks can be automated based on the analyzed content. The generation unit is implemented by the identification processing unit 290 of the data processing device 12 and generates codes for automating the determined tasks. The notification unit is implemented by the control unit 46A of the smart glasses 214 and notifies the generated codes on the PC. The introduction unit is implemented by the control unit 46A of the smart glasses 214 and introduces the notified codes to the PC. === Hard Collateral 1-3 === Each of the multiple elements described above, including the collection unit, analysis unit, discrimination unit, generation unit, notification unit, and introduction unit, is implemented in at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314 and collects PC operation logs. The analysis unit is implemented by the identification processing unit 290 of the data processing device 12 and analyzes the collected operation logs. The discrimination unit is implemented by the identification processing unit 290 of the data processing device 12 and determines which tasks can be automated based on the analyzed content. The generation unit is implemented by the identification processing unit 290 of the data processing device 12 and generates code for automating the determined tasks. The notification unit is implemented by the control unit 46A of the headset terminal 314 and notifies the generated code on the PC. The introduction unit is implemented by the control unit 46A of the headset terminal 314 and introduces the notified code to the PC. === Hard Collateral 1-4 === Each of the multiple elements described above, including the collection unit, analysis unit, discrimination unit, generation unit, notification unit, and introduction unit, is implemented in, for example, 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 PC operation logs. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and analyzes the collected operation logs. The discrimination unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and determines which tasks can be automated based on the analyzed content. The generation unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and generates code for automating the determined tasks. The notification unit is implemented by, for example, the control unit 46A of the robot 414 and notifies the generated code on the PC. The introduction unit is implemented by, for example, the control unit 46A of the robot 414 and introduces the notified code to the PC.

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

[0101] The data collection unit can analyze the user's device usage patterns to determine the optimal timing for collecting user operation logs. For example, the unit can learn when a user typically uses their device and concentrate data collection during those times. It can also analyze how often a user uses a specific application and collect data when that application is being used. Furthermore, the unit can dynamically adjust the frequency and method of data collection based on the user's device usage patterns. This enables efficient data collection based on the user's device usage patterns.

[0102] The analysis unit can estimate the user's emotions and determine the priority of analysis based on those emotions. For example, if the user is stressed, the analysis unit can prioritize high-priority analyses. If the user is relaxed, the analysis unit can perform more detailed analyses. Furthermore, if the user is in a hurry, the analysis unit can prioritize concise analyses. By prioritizing analyses according to the user's emotions, more appropriate analysis results can be provided.

[0103] The discrimination unit can adjust its discrimination criteria based on the skill level of the person performing the operation. For example, if the person performing the operation is a novice, the discrimination unit can perform discrimination using strict criteria. Conversely, if the person performing the operation is an expert, the discrimination unit can perform discrimination using flexible criteria. Furthermore, the discrimination unit can dynamically adjust the discrimination criteria according to the skill level of the person performing the operation. This enables appropriate discrimination based on the skill level of the person performing the operation.

[0104] The generation unit can estimate the user's emotions and adjust the complexity of the code it generates based on those emotions. For example, if the user is stressed, the generation unit can generate simple and easy-to-understand code. If the user is relaxed, it can generate detailed code. Furthermore, if the user is in a hurry, it can generate concise code. By adjusting the complexity of the code according to the user's emotions, more appropriate code generation becomes possible.

[0105] The notification unit can analyze the user's past notification history and select the optimal notification method. For example, it can prioritize notification methods that the user has preferred using in the past. Furthermore, the notification unit can predict the optimal notification timing based on the user's past notification history. In addition, the notification unit can analyze the user's past notification history and suggest effective notification methods. This allows the system to select the optimal notification method by referring to the user's past notification history.

[0106] The introduction section can estimate the user's emotions and customize the introduction procedure based on those emotions. For example, if the user is nervous, the introduction section can provide a simple and easy-to-understand introduction procedure. If the user is relaxed, it can provide a detailed introduction procedure. Furthermore, if the user is in a hurry, it can provide a procedure that allows for quick implementation. By customizing the introduction procedure according to the user's emotions, a more appropriate introduction becomes possible.

[0107] The data collection unit can analyze the user's device usage patterns to determine the optimal timing for collecting user operation logs. For example, the unit can learn when a user typically uses their device and concentrate data collection during those times. It can also analyze how often a user uses a specific application and collect data when that application is being used. Furthermore, the unit can dynamically adjust the frequency and method of data collection based on the user's device usage patterns. This enables efficient data collection based on the user's device usage patterns.

[0108] The analysis unit can estimate the user's emotions and determine the priority of analysis based on those emotions. For example, if the user is stressed, the analysis unit can prioritize high-priority analyses. If the user is relaxed, the analysis unit can perform more detailed analyses. Furthermore, if the user is in a hurry, the analysis unit can prioritize concise analyses. By prioritizing analyses according to the user's emotions, more appropriate analysis results can be provided.

[0109] The discrimination unit can adjust its discrimination criteria based on the skill level of the person performing the operation. For example, if the person performing the operation is a novice, the discrimination unit can perform discrimination using strict criteria. Conversely, if the person performing the operation is an expert, the discrimination unit can perform discrimination using flexible criteria. Furthermore, the discrimination unit can dynamically adjust the discrimination criteria according to the skill level of the person performing the operation. This enables appropriate discrimination based on the skill level of the person performing the operation.

[0110] The generation unit can estimate the user's emotions and adjust the complexity of the code it generates based on those emotions. For example, if the user is stressed, the generation unit can generate simple and easy-to-understand code. If the user is relaxed, it can generate detailed code. Furthermore, if the user is in a hurry, it can generate concise code. By adjusting the complexity of the code according to the user's emotions, more appropriate code generation becomes possible.

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

[0112] Step 1: The data collection unit collects PC operation logs. The data collection unit collects detailed operation logs, such as user clicks and keyboard inputs. For example, the data collection unit can record user clicks and identify which applications were used. The data collection unit can also record keyboard input and analyze the content of the entered text. Furthermore, the data collection unit can take screen captures to visually record user operations. Step 2: The analysis unit analyzes the operation logs collected by the collection unit and learns the nature of the work. For example, the analysis unit analyzes whether certain operations are performed repeatedly. For instance, the analysis unit can detect that data entry operations are performed frequently and identify this as a routine task. The analysis unit can also detect that a particular application is used frequently and identify the tasks associated with that application. Furthermore, the analysis unit can analyze the sequence and timing of operations to understand the workflow. Step 3: The discrimination unit identifies tasks that can be automated based on what the analysis unit has learned. The discrimination unit identifies tasks that involve a lot of routine work, such as data entry and the creation of standardized reports. For example, the discrimination unit can detect that data entry operations are performed repeatedly and identify this as a task that can be automated. The discrimination unit can also detect that standardized report creation operations are performed frequently and identify this as a task that can be automated. Furthermore, the discrimination unit can detect that a particular application is used frequently and identify tasks related to that application as tasks that can be automated. Step 4: The generation unit generates code to automate the tasks identified by the discrimination unit. The generation unit generates automation scripts using programming languages ​​such as Python or VBA. For example, the generation unit can generate a Python script to automate data entry operations. It can also generate a VBA macro to automate routine report creation operations. Furthermore, the generation unit can generate scripts to automate operations related to specific applications. Step 5: The notification unit notifies the user of the code generated by the generation unit on the PC. The notification unit prompts the user to install the code in the form of, for example, a pop-up notification or an email notification. For example, the notification unit can display a pop-up notification and instruct the user to install the generated code. Alternatively, the notification unit can send an email notification and instruct the user to install the generated code. Furthermore, the notification unit can notify the user through a chat application and instruct the user to install the generated code. Step 6: The implementation unit installs the code notified by the notification unit onto the PC. The implementation unit completes the automation of routine tasks, for example, by having the user execute the code according to the instructions. For example, the implementation unit can execute a generated Python script to automate data entry operations. It can also execute a generated VBA macro to automate routine report creation operations. Furthermore, the implementation unit can execute a script related to a specific application to automate operations related to that application.

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

[0114] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, 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. 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 a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

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

[0116] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

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

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

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

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

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

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

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

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

[0125] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

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

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

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

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

[0131] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0132] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

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

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

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

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

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

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

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

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

[0141] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

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

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

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

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

[0147] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0148] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

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

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

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

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

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

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

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

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

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

[0158] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

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

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

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

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

[0164] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0165] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

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

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

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

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

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

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

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

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

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

[0175] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

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

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

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

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

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

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

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

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

[0184] [Explanation of symbols]

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

Claims

1. A collection unit that collects PC operation logs, An analysis unit analyzes the operation logs collected by the aforementioned collection unit and learns the content of the work, A discrimination unit that identifies tasks that can be automated based on the content learned by the aforementioned analysis unit, A generation unit generates code for automating the business identified by the discrimination unit, A notification unit that notifies the PC of the code generated by the generation unit, The system includes an introduction unit that introduces the code notified by the notification unit. A system characterized by the following features.

2. 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.

3. The aforementioned collection unit is Analyze the user's past activity history and select the appropriate data collection method. The system according to feature 1.

4. The aforementioned collection unit is When collecting operation logs, filtering is performed based on the user's current work status and areas of interest. The system according to feature 1.

5. 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 according to feature 1.

6. The aforementioned collection unit is When collecting operation logs, prioritize collecting logs that are highly relevant based on the user's geographical location. The system according to feature 1.

7. The aforementioned collection unit is When collecting operation logs, analyze the user's social media activity and collect relevant logs. The system according to feature 1.

8. 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 according to feature 1.

9. The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the operations. The system according to feature 1.

10. The aforementioned analysis unit, During analysis, apply the appropriate analysis algorithm according to the category of the operation. The system according to feature 1.

Images