system

Abstract

The system according to this embodiment aims to propose and execute tasks that can be automated based on the user's operation patterns. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a proposal unit, and an execution unit. The collection unit collects user operation patterns. The analysis unit analyzes the operation patterns collected by the collection unit. The proposal unit proposes tasks that can be automated based on the operation patterns analyzed by the analysis unit. The execution unit executes the tasks proposed by the proposal unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, the method including 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 in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, automation of tasks based on the operation pattern of a user has not been sufficiently performed, and there is room for improvement.

[0005] The system according to an embodiment aims to propose and execute tasks that can be automated based on the operation pattern of a user.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, and an execution unit. The collection unit collects user operation patterns. The analysis unit analyzes the operation patterns collected by the collection unit. The proposal unit proposes tasks that can be automated based on the operation patterns analyzed by the analysis unit. The execution unit executes the tasks proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can propose and execute tasks that can be automated based on the user's operation patterns. [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 manages communication between multiple computers. Examples of the 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 Daily Learner System, according to an embodiment of the present invention, is a business support tool equipped with generative AI that learns the user's PC operations daily and gradually automates specific business tasks. The Daily Learner System analyzes the user's operation patterns, identifies repetitive tasks, and proposes automation. Approved tasks can be executed autonomously by the AI. The Daily Learner System is particularly targeted at office workers in small and medium-sized enterprises (SMEs). Many office workers are bogged down in repetitive tasks such as data entry and report creation, preventing them from focusing on more creative and value-added work. Furthermore, the introduction of business automation tools often requires high costs and specialized knowledge, hindering their adoption in SMEs. The Daily Learner System achieves business automation without specialized settings or high implementation costs by learning the user's daily PC operations. Based on the operation patterns learned by the AI, it proposes automation and automatically executes tasks after obtaining user approval. As a result, users are gradually freed from routine tasks and can focus on more important work. Using generative AI, the system analyzes the user's daily PC operations and identifies repetitive task patterns. Based on the learned patterns, it proposes tasks that can be automated to the user. Suggestions are made in natural language and expressed in a way that is easy for users to understand. For approved tasks, the generative AI replicates the operation procedures and executes them automatically. During execution, it emulates screen operations and keyboard input, behaving as if a human were operating it. It learns from the results of task execution and new operation patterns on a daily basis, continuously proposing more advanced automation and efficiency improvements. In the event of unusual situations or unexpected errors, the generative AI analyzes the situation and proposes or implements appropriate countermeasures. It analyzes the execution status and degree of efficiency of automated tasks and automatically generates easy-to-understand reports. This idea leverages the learning and natural language processing capabilities of generative AI to gradually streamline users' daily work, lowering the barriers to business automation in small and medium-sized enterprises and improving productivity. As a result, the Daily Learner system can streamline users' work and allow them to focus on more important tasks.

[0029] The daily learner system according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, and an execution unit. The collection unit collects user operation patterns. The collection unit can collect, for example, the user's mouse clicks, keyboard input, and application usage history. The collection unit can also set the frequency of collection and the type of data to be collected. For example, the collection unit can collect operation patterns at a fixed time each day. The collection unit can also prioritize the collection of usage history for specific applications. The analysis unit analyzes the operation patterns collected by the collection unit. The analysis unit can analyze operation patterns using, for example, machine learning algorithms. The analysis unit can also evaluate the frequency and importance of operation patterns. For example, the analysis unit can identify frequently performed operation patterns and make automation suggestions based on them. The analysis unit can also evaluate the impact of specific operation patterns on business operations. The proposal unit proposes tasks that can be automated based on the operation patterns analyzed by the analysis unit. The proposal unit can propose tasks that can be automated using, for example, natural language processing technology. The proposal unit can also set the format and timing of the suggestions. For example, the proposal unit can make automation suggestions in a way that is easy for the user to understand. The proposal unit can also make suggestions at appropriate times depending on the user's work situation. The execution unit executes the tasks proposed by the proposal unit. The execution unit can execute tasks by, for example, emulating screen operations or keyboard input. The execution unit can also set the execution procedure for tasks. For example, the execution unit can execute tasks by reproducing specific operation procedures. Furthermore, the execution unit can monitor the task execution status and make corrections as needed. As a result, the daily learner system according to this embodiment can improve work efficiency by collecting and analyzing user operation patterns, proposing tasks that can be automated, and executing them.

[0030] The data collection unit collects user operation patterns. For example, it can collect data such as user mouse clicks, keyboard input, and application usage history. Specifically, it meticulously records details such as mouse click location and frequency, keyboard input content and speed, and even the startup and usage time of specific applications. This data can be customized according to the user's operating environment and usage, allowing for the setting of collection frequency and the types of data to collect. For example, the data collection unit can collect operation patterns at a fixed time each day. It can also prioritize the collection of usage history for specific applications. This allows the data collection unit to gain a detailed understanding of user operation patterns and efficiently collect data necessary for subsequent analysis and recommendations. Furthermore, the data collection unit can transmit the collected data to a central database in real time, enabling centralized data management in collaboration with other departments. This allows the data collection unit to continuously monitor user operation patterns and adjust the data collection method and frequency as needed. For example, during peak seasons for specific tasks, the collection frequency can be increased to collect more detailed data, while during off-peak seasons, the collection frequency can be reduced to lessen the system load. This allows the data collection unit to collect data efficiently and flexibly, improving the overall performance of the system.

[0031] The analysis department analyzes the operation patterns collected by the data collection department. For example, the analysis department can analyze operation patterns using machine learning algorithms. Specifically, it clusters user operation patterns based on the collected data and evaluates their frequency and importance. For instance, it can identify frequently performed operation patterns and use this information to suggest automation. It can also evaluate the impact of specific operation patterns on business operations. This allows the analysis department to analyze user operation patterns in detail and identify important patterns that contribute to business efficiency. Furthermore, the analysis department can utilize historical data and statistical information to analyze long-term trends and pattern fluctuations. For example, it can predict fluctuations in operation patterns at specific times or situations based on past operation data and formulate measures for future business efficiency improvements. Additionally, the analysis department can use anomaly detection algorithms to detect unusual operation patterns and abnormal data, issuing early warnings. This enables the analysis department to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the overall reliability and security of the system.

[0032] The Proposal Department proposes tasks that can be automated based on the operation patterns analyzed by the Analysis Department. For example, the Proposal Department can propose tasks that can be automated using natural language processing technology. Specifically, it analyzes user operation patterns, identifies frequently performed operations and time-consuming tasks, and proposes ways to automate them. The Proposal Department can set the format and timing of the proposals. For example, the Proposal Department can propose automation in a way that is easy for users to understand. Specifically, it can propose automation to users through pop-up notifications or email notifications. The Proposal Department can also make proposals at appropriate times depending on the user's work situation. For example, by making a proposal during times when the user frequently performs a particular task, the acceptance rate of the proposal can be increased. Furthermore, the Proposal Department can collect user feedback and continuously improve the accuracy and effectiveness of the proposals. For example, it can review and improve the proposal content based on feedback from users who have accepted the proposals. In this way, the Proposal Department can make appropriate automation proposals to users and support the efficiency of their work.

[0033] The execution unit executes tasks proposed by the proposal unit. The execution unit can, for example, emulate screen operations or keyboard input to execute tasks. Specifically, it can automatically reproduce operations that a user typically performs, efficiently executing tasks. For example, it can automate tasks such as launching specific applications, changing settings, and data entry. The execution unit can also configure the task execution procedure. For example, it can execute tasks by reproducing specific operation procedures. This allows the execution unit to accurately reproduce user operations and execute tasks efficiently. Furthermore, the execution unit can monitor the task execution status and make corrections as needed. For example, if an error occurs during task execution, the execution unit automatically detects the error and makes appropriate corrections. This ensures task execution and improves operational efficiency. Additionally, the execution unit can record the task execution results for subsequent analysis and improvement. For example, it can record the success rate and execution time of executed tasks and use this data to improve tasks. This allows the execution unit to execute tasks efficiently and reliably, improving the overall system performance.

[0034] The execution unit can automatically execute approved tasks. For example, the execution unit can automatically execute tasks approved by the user. For example, the execution unit can execute user-approved tasks according to a schedule. The execution unit can also execute user-approved tasks in real time. For example, the execution unit can execute user-approved tasks immediately. This reduces the user's effort by automatically executing approved tasks. Some or all of the above processes in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input user-approved tasks into an AI model and have the AI ​​execute the tasks.

[0035] The suggestion unit can propose tasks that can be automated using natural language. For example, the suggestion unit can propose tasks that can be automated using natural language processing technology. For example, the suggestion unit can analyze user operation patterns using text analysis technology and propose tasks that can be automated. The suggestion unit can also make suggestions in a way that is easy for the user to understand using natural language generation technology. For example, the suggestion unit can make a suggestion to the user in the form of "Would you like to automate this task?" By proposing tasks in natural language, it becomes easier for the user to understand. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input user operation patterns into an AI model and have the AI ​​execute suggestions in natural language.

[0036] The data collection unit can collect the user's daily PC operations. For example, the data collection unit can collect information such as the user's application launches and file operations. For instance, the data collection unit can collect the launch history of applications that the user uses on a daily basis. The data collection unit can also collect information on the types of files the user manipulates and how frequently they are manipulated. For example, the data collection unit can identify the types of files that the user frequently manipulates and collect operation patterns based on that. This allows for a more accurate understanding of operation patterns by collecting daily PC operations. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's daily PC operations into an AI model and have the AI ​​perform the collection of operation patterns.

[0037] The analysis unit can analyze collected operation patterns and identify repetitive tasks. For example, the analysis unit can analyze operation patterns using machine learning algorithms. For instance, it can identify frequently performed operations from the collected operation patterns and identify them as repetitive tasks. The analysis unit can also evaluate the impact of specific operation patterns on business operations. For example, it can identify repetitive tasks such as periodic data entry or periodic report creation. This allows for the clarification of tasks that can be automated by identifying repetitive tasks. Some or all of the above processing in the analysis unit may be performed using AI, or not. For example, the analysis unit can input collected operation patterns into an AI model and have the AI ​​perform the identification of repetitive tasks.

[0038] The execution unit can emulate screen operations and keyboard input. For example, the execution unit can emulate screen operations performed by a user. For example, the execution unit can emulate window operations and button clicks. The execution unit can also emulate keyboard input performed by a user. For example, the execution unit can emulate text input and the use of shortcut keys. In this way, user operations can be reproduced by emulating screen operations and keyboard input. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input the user's screen operations and keyboard input into an AI model and have the AI ​​perform the emulation.

[0039] The analysis unit can learn from the results of task execution and new operating patterns on a daily basis. For example, the analysis unit can learn from the results of task execution using machine learning algorithms. For example, the analysis unit can identify and learn new operating patterns based on the results of task execution. The analysis unit can also evaluate the results of task execution and analyze the degree of efficiency improvement. For example, the analysis unit can evaluate the degree of efficiency improvement based on the results of task execution and reflect it in the next proposal. In this way, the accuracy of the system improves through daily learning. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the results of task execution into an AI model and have the AI ​​perform the learning.

[0040] The proposal unit can analyze the execution status and efficiency improvements of automated tasks and automatically generate reports. For example, the proposal unit can monitor the execution status of tasks and evaluate the degree of efficiency improvements based on that data. For example, the proposal unit can analyze the execution time and error frequency of tasks and evaluate the degree of efficiency improvements. The proposal unit can also automatically generate reports based on the results of the efficiency improvement evaluation. For example, the proposal unit can generate reports that visually display the degree of efficiency improvements as graphs or charts. This makes it easier for users to understand the degree of efficiency improvements by automatically generating reports. Some or all of the above processes in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input task execution status data into an AI model and have the AI ​​perform the efficiency improvement evaluation and report generation.

[0041] The data collection unit can analyze the user's past operation history and select the optimal data collection method. For example, the data collection unit can analyze past operation history data and select the optimal data collection method. For example, the data collection unit can prioritize collecting operations that the user has frequently performed in the past. The data collection unit can also predict operations that will be performed during specific time periods and concentrate data collection during those times. For example, the data collection unit can predict operations that will be performed during specific time periods based on the user's operation history and collect data during those times. This allows for the selection of an efficient data collection method 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 the user's past operation history data into an AI model and have the AI ​​select the optimal data collection method.

[0042] The data collection unit can filter operation patterns based on the user's current work situation and areas of interest when collecting them. For example, the data collection unit can monitor the user's work situation and filter operation patterns based on that. For example, if the user is focused on a specific project, the data collection unit can prioritize collecting operation patterns related to that project. The data collection unit can also filter relevant operation patterns based on the user's areas of interest. For example, the data collection unit can prioritize collecting operation patterns related to the user's areas of interest. This allows for the priority collection of important operation patterns by filtering based on work situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's work situation data into an AI model and have the AI ​​perform the filtering.

[0043] The data collection unit can prioritize the collection of highly relevant patterns by considering the user's geographical location information when collecting operation patterns. For example, the data collection unit can acquire the user's geographical location information using GPS data. For example, the data collection unit can prioritize the collection of operation patterns performed by the user at a specific location. The data collection unit can also acquire the user's geographical location information using an IP address. For example, the data collection unit can identify the geographical location from the user's IP address and prioritize the collection of operation patterns performed at that location. This allows for the priority collection of highly relevant operation patterns by considering 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 the user's geographical location data into an AI model and have the AI ​​perform the collection of operation patterns.

[0044] The data collection unit can analyze the user's social media activity and collect relevant patterns when collecting operation patterns. For example, the data collection unit can analyze the user's social media activity data and collect relevant operation patterns. For example, the data collection unit can prioritize collecting operation patterns that the user frequently performs on social media. The data collection unit can also filter relevant operation patterns based on the user's social media activity. For example, the data collection unit can prioritize collecting operation patterns related to the user's social media activity. This allows for the efficient collection of relevant operation patterns by analyzing 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 the user's social media activity data into an AI model and have the AI ​​perform the operation pattern collection.

[0045] The analysis unit can adjust the level of detail of the analysis based on the importance of the operation patterns during the analysis. For example, the analysis unit can evaluate the importance of operation patterns and adjust the level of detail of the analysis based on that evaluation. For example, the analysis unit can perform a detailed analysis for highly important operation patterns and a concise analysis for less important operation patterns. The analysis unit can also dynamically adjust the level of detail of the analysis according to the importance of the operation patterns. For example, the analysis unit can adjust the level of detail of the analysis in real time based on the importance of the operation patterns. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the operation patterns. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the importance data of operation patterns into an AI model and have the AI ​​perform the adjustment of the level of detail of the analysis.

[0046] The analysis unit can apply different analysis algorithms depending on the category of the operation pattern during analysis. For example, the analysis unit can identify the category of the operation pattern and apply the most suitable analysis algorithm accordingly. For example, the analysis unit can apply a specific algorithm to data entry operations and a different algorithm to report creation operations. The analysis unit can also select the most suitable analysis algorithm depending on the category of the operation pattern. For example, the analysis unit can dynamically select the most suitable analysis algorithm based on the category of the operation pattern. This improves the accuracy of the analysis by applying the most suitable analysis algorithm according to the category of the operation pattern. Some or all of the above processes 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 pattern into an AI model and have the AI ​​perform the application of the analysis algorithm.

[0047] The analysis unit can determine the priority of analysis based on the submission timing of operation patterns during analysis. For example, the analysis unit can evaluate the submission timing of operation patterns and determine the priority of analysis based on that. For example, the analysis unit can prioritize the analysis of recently submitted operation patterns and postpone the analysis of older operation patterns. The analysis unit can also dynamically adjust the priority of analysis based on the submission timing. For example, the analysis unit can adjust the priority of analysis in real time based on the submission timing. This enables efficient analysis by determining the priority of analysis based on the submission timing. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input operation pattern submission timing data into an AI model and have the AI ​​perform the determination of analysis priority.

[0048] The analysis unit can adjust the order of analysis based on the relevance of the operation patterns during analysis. For example, the analysis unit can evaluate the relevance of the operation patterns and adjust the order of analysis based on that evaluation. For example, the analysis unit can prioritize the analysis of highly relevant operation patterns and postpone the analysis of less relevant operation patterns. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the operation patterns. For example, the analysis unit can adjust the order of analysis in real time based on the relevance of the operation patterns. This enables efficient analysis by adjusting the order of analysis based on the relevance of the operation patterns. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the relevance data of the operation patterns into an AI model and have the AI ​​perform the adjustment of the order of analysis.

[0049] The proposal unit can adjust the level of detail of a proposal based on the importance of the task. For example, the proposal unit can evaluate the importance of the task and adjust the level of detail of the proposal accordingly. For example, the proposal unit can provide detailed proposals for high-importance tasks and concise proposals for low-importance tasks. The proposal unit can also dynamically adjust the level of detail of a proposal according to the importance of the task. For example, the proposal unit can adjust the level of detail of a proposal in real time based on the importance of the task. This allows for efficient proposals by adjusting the level of detail of a proposal based on the importance of the task. Some or all of the above processes in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input task importance data into an AI model and have the AI ​​perform the adjustment of the level of detail of the proposal.

[0050] The proposal unit can apply different proposal algorithms depending on the task category when making a proposal. For example, the proposal unit can identify the task category and apply the most suitable proposal algorithm accordingly. For example, the proposal unit can apply a specific algorithm to data entry tasks and a different algorithm to report creation tasks. The proposal unit can also select the most suitable proposal algorithm depending on the task category. For example, the proposal unit can dynamically select the most suitable proposal algorithm based on the task category. This improves the accuracy of the proposal by applying the most suitable proposal algorithm according to the task category. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input task category data into an AI model and have the AI ​​perform the application of the proposal algorithm.

[0051] The proposal department can determine the priority of proposals based on the task submission timing when submitting a proposal. For example, the proposal department can evaluate the task submission timing and determine the priority of proposals based on that. For example, the proposal department can prioritize recently submitted tasks and postpone older tasks. The proposal department can also dynamically adjust the priority of proposals based on submission timing. For example, the proposal department can adjust the priority of proposals in real time based on submission timing. This enables efficient proposals by determining the priority of proposals based on submission timing. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input task submission timing data into an AI model and have the AI ​​perform the determination of proposal priority.

[0052] The proposal unit can adjust the order of proposals based on the relevance of the tasks during the proposal process. For example, the proposal unit can evaluate the relevance of tasks and adjust the order of proposals accordingly. For example, the proposal unit can prioritize highly relevant tasks and postpone less relevant tasks. The proposal unit can also dynamically adjust the order of proposals based on the relevance of tasks. For example, the proposal unit can adjust the order of proposals in real time based on the relevance of tasks. This allows for efficient proposals by adjusting the order of proposals based on the relevance of tasks. Some or all of the above processes in the proposal unit may be performed using AI, for example, or not. For example, the proposal unit can input task relevance data into an AI model and have the AI ​​perform the adjustment of the order of proposals.

[0053] The execution unit can analyze the user's past operation history during execution to select the optimal execution method. For example, the execution unit can analyze past operation history data and select the optimal execution method. For example, the execution unit can select the optimal execution method based on operations performed by the user in the past. The execution unit can also propose an efficient execution method from the user's operation history. For example, the execution unit analyzes the user's past operation history and selects the most effective execution method. In this way, an efficient execution method can be selected by analyzing past operation history. Some or all of the above processing in the execution unit may be performed using AI, for example, or without using AI. For example, the execution unit can input the user's past operation history data into an AI model and have the AI ​​select the optimal execution method.

[0054] The execution unit can customize the execution methods at runtime based on the user's current work situation. For example, the execution unit can monitor the user's work situation and customize the execution methods accordingly. For example, if the user is focused on a specific project, the execution unit can prioritize tasks related to that project. The execution unit can also prioritize important tasks, taking into account the user's current work situation. For example, the execution unit can dynamically customize the execution methods based on the user's work situation. This enables efficient execution by customizing the execution methods based on the work situation. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input user work situation data into an AI model and have the AI ​​perform the customization of the execution methods.

[0055] The execution unit can select the optimal execution method at runtime, taking into account the user's geographical location information. For example, the execution unit can obtain the user's geographical location information using GPS data. For example, the execution unit can prioritize the execution of tasks performed by the user at a specific location. The execution unit can also obtain the user's geographical location information using an IP address. For example, the execution unit can identify the geographical location from the user's IP address and prioritize the execution of tasks performed at that location. This allows for the priority execution of highly relevant tasks by considering geographical location information. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input the user's geographical location data into an AI model and have the AI ​​select the optimal execution method.

[0056] The execution unit can analyze the user's social media activity at runtime and propose means of execution. For example, the execution unit can analyze the user's social media activity data and execute related tasks. For example, the execution unit can prioritize the execution of tasks that the user frequently performs on social media. The execution unit can also filter related tasks based on the user's social media activity. For example, the execution unit can prioritize the execution of tasks related to the user's social media activity. This allows for the efficient execution of related tasks by analyzing social media activity. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input the user's social media activity data into an AI model and have the AI ​​propose means of execution.

[0057] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0058] The Daily Learner system can also be equipped with a function to analyze the user's past activity history and select the optimal method for suggesting tasks. For example, it can analyze patterns of tasks the user has previously approved and suggest similar tasks in a format the user prefers. It can also predict the user's activities during specific time periods and suggest tasks suitable for those times. Furthermore, it can analyze the reasons why the user has previously rejected tasks and take those reasons into consideration when suggesting similar tasks. This enables optimal suggestions based on the user's past activity history, thereby improving the acceptance rate of suggestions.

[0059] The Daily Learner system can also incorporate a function to suggest tasks that take into account the user's geographical location. For example, if a user is in a specific location, tasks related to that location can be prioritized. If a user is on the move, tasks that can be performed while traveling can be suggested. Furthermore, the system can predict the time of day when a user will be in a specific location and suggest tasks suitable for that time. This enables optimal task suggestions based on the user's geographical location, thereby improving the user's work efficiency.

[0060] The Daily Learner system can further incorporate features that analyze users' social media activity and suggest relevant tasks. For example, it can analyze the user's frequent social media activity patterns and suggest relevant tasks based on that. It can also suggest tasks related to specific topics if the user shows interest in them on social media. Furthermore, it can suggest new tasks that the user might be interested in based on their social media activity. This enables optimal task suggestions based on the user's social media activity, thereby improving the user's work efficiency.

[0061] The Daily Learner system can further incorporate a feature that customizes task suggestions based on the user's current work status. For example, if a user is focused on a specific project, tasks related to that project can be prioritized. If a user is working on multiple projects simultaneously, appropriate tasks can be suggested according to the progress of each project. Furthermore, the system can monitor the user's work status in real time and dynamically adjust task suggestions accordingly. This enables optimal task suggestions based on the user's current work situation, thereby improving the user's work efficiency.

[0062] The Daily Learner system can further incorporate features that customize how tasks are executed based on the user's current work status. For example, if a user is focused on a specific project, tasks related to that project can be prioritized. Similarly, if a user is working on multiple projects simultaneously, the system can execute appropriate tasks according to the progress of each project. Furthermore, it can monitor the user's work status in real time and dynamically adjust task execution methods accordingly. This enables optimal task execution based on the user's current work status, thereby improving the user's work efficiency.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The data collection unit collects user operation patterns. For example, it can collect the user's mouse clicks, keyboard input, and application usage history. The data collection unit can also configure the frequency of collection and the types of data to be collected. For example, it can collect operation patterns at a certain time each day, or prioritize the collection of usage history for specific applications. Step 2: The analysis unit analyzes the operation patterns collected by the data collection unit. For example, machine learning algorithms can be used to analyze operation patterns and evaluate their frequency and importance. Frequently occurring operation patterns can be identified, and automation suggestions can be made based on these. It is also possible to evaluate the impact that specific operation patterns have on business operations. Step 3: The proposal department proposes tasks that can be automated based on the operation patterns analyzed by the analysis department. For example, it can propose tasks that can be automated using natural language processing technology and set the format and timing of the proposals. Automation proposals can be made in a way that is easy for users to understand and at an appropriate time according to the user's work situation. Step 4: The execution unit executes the tasks proposed by the proposal unit. For example, it can execute tasks by emulating screen operations or keyboard input, and it can set the procedure for executing the tasks. It can also execute tasks by reproducing specific operation procedures, monitor the task execution status, and make corrections as needed.

[0065] (Example of form 2) The Daily Learner System, according to an embodiment of the present invention, is a business support tool equipped with generative AI that learns the user's PC operations daily and gradually automates specific business tasks. The Daily Learner System analyzes the user's operation patterns, identifies repetitive tasks, and proposes automation. Approved tasks can be executed autonomously by the AI. The Daily Learner System is particularly targeted at office workers in small and medium-sized enterprises (SMEs). Many office workers are bogged down in repetitive tasks such as data entry and report creation, preventing them from focusing on more creative and value-added work. Furthermore, the introduction of business automation tools often requires high costs and specialized knowledge, hindering their adoption in SMEs. The Daily Learner System achieves business automation without specialized settings or high implementation costs by learning the user's daily PC operations. Based on the operation patterns learned by the AI, it proposes automation and automatically executes tasks after obtaining user approval. As a result, users are gradually freed from routine tasks and can focus on more important work. Using generative AI, the system analyzes the user's daily PC operations and identifies repetitive task patterns. Based on the learned patterns, it proposes tasks that can be automated to the user. Suggestions are made in natural language and expressed in a way that is easy for users to understand. For approved tasks, the generative AI replicates the operation procedures and executes them automatically. During execution, it emulates screen operations and keyboard input, behaving as if a human were operating it. It learns from the results of task execution and new operation patterns on a daily basis, continuously proposing more advanced automation and efficiency improvements. In the event of unusual situations or unexpected errors, the generative AI analyzes the situation and proposes or implements appropriate countermeasures. It analyzes the execution status and degree of efficiency of automated tasks and automatically generates easy-to-understand reports. This idea leverages the learning and natural language processing capabilities of generative AI to gradually streamline users' daily work, lowering the barriers to business automation in small and medium-sized enterprises and improving productivity. As a result, the Daily Learner system can streamline users' work and allow them to focus on more important tasks.

[0066] The daily learner system according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, and an execution unit. The collection unit collects user operation patterns. The collection unit can collect, for example, the user's mouse clicks, keyboard input, and application usage history. The collection unit can also set the frequency of collection and the type of data to be collected. For example, the collection unit can collect operation patterns at a fixed time each day. The collection unit can also prioritize the collection of usage history for specific applications. The analysis unit analyzes the operation patterns collected by the collection unit. The analysis unit can analyze operation patterns using, for example, machine learning algorithms. The analysis unit can also evaluate the frequency and importance of operation patterns. For example, the analysis unit can identify frequently performed operation patterns and make automation suggestions based on them. The analysis unit can also evaluate the impact of specific operation patterns on business operations. The proposal unit proposes tasks that can be automated based on the operation patterns analyzed by the analysis unit. The proposal unit can propose tasks that can be automated using, for example, natural language processing technology. The proposal unit can also set the format and timing of the suggestions. For example, the proposal unit can make automation suggestions in a way that is easy for the user to understand. The proposal unit can also make suggestions at appropriate times depending on the user's work situation. The execution unit executes the tasks proposed by the proposal unit. The execution unit can execute tasks by, for example, emulating screen operations or keyboard input. The execution unit can also set the execution procedure for tasks. For example, the execution unit can execute tasks by reproducing specific operation procedures. Furthermore, the execution unit can monitor the task execution status and make corrections as needed. As a result, the daily learner system according to this embodiment can improve work efficiency by collecting and analyzing user operation patterns, proposing tasks that can be automated, and executing them.

[0067] The data collection unit collects user operation patterns. For example, it can collect data such as user mouse clicks, keyboard input, and application usage history. Specifically, it meticulously records details such as mouse click location and frequency, keyboard input content and speed, and even the startup and usage time of specific applications. This data can be customized according to the user's operating environment and usage, allowing for the setting of collection frequency and the types of data to collect. For example, the data collection unit can collect operation patterns at a fixed time each day. It can also prioritize the collection of usage history for specific applications. This allows the data collection unit to gain a detailed understanding of user operation patterns and efficiently collect data necessary for subsequent analysis and recommendations. Furthermore, the data collection unit can transmit the collected data to a central database in real time, enabling centralized data management in collaboration with other departments. This allows the data collection unit to continuously monitor user operation patterns and adjust the data collection method and frequency as needed. For example, during peak seasons for specific tasks, the collection frequency can be increased to collect more detailed data, while during off-peak seasons, the collection frequency can be reduced to lessen the system load. This allows the data collection unit to collect data efficiently and flexibly, improving the overall performance of the system.

[0068] The analysis department analyzes the operation patterns collected by the data collection department. For example, the analysis department can analyze operation patterns using machine learning algorithms. Specifically, it clusters user operation patterns based on the collected data and evaluates their frequency and importance. For instance, it can identify frequently performed operation patterns and use this information to suggest automation. It can also evaluate the impact of specific operation patterns on business operations. This allows the analysis department to analyze user operation patterns in detail and identify important patterns that contribute to business efficiency. Furthermore, the analysis department can utilize historical data and statistical information to analyze long-term trends and pattern fluctuations. For example, it can predict fluctuations in operation patterns at specific times or situations based on past operation data and formulate measures for future business efficiency improvements. Additionally, the analysis department can use anomaly detection algorithms to detect unusual operation patterns and abnormal data, issuing early warnings. This enables the analysis department to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the overall reliability and security of the system.

[0069] The Proposal Department proposes tasks that can be automated based on the operation patterns analyzed by the Analysis Department. For example, the Proposal Department can propose tasks that can be automated using natural language processing technology. Specifically, it analyzes user operation patterns, identifies frequently performed operations and time-consuming tasks, and proposes ways to automate them. The Proposal Department can set the format and timing of the proposals. For example, the Proposal Department can propose automation in a way that is easy for users to understand. Specifically, it can propose automation to users through pop-up notifications or email notifications. The Proposal Department can also make proposals at appropriate times depending on the user's work situation. For example, by making a proposal during times when the user frequently performs a particular task, the acceptance rate of the proposal can be increased. Furthermore, the Proposal Department can collect user feedback and continuously improve the accuracy and effectiveness of the proposals. For example, it can review and improve the proposal content based on feedback from users who have accepted the proposals. In this way, the Proposal Department can make appropriate automation proposals to users and support the efficiency of their work.

[0070] The execution unit executes tasks proposed by the proposal unit. The execution unit can, for example, emulate screen operations or keyboard input to execute tasks. Specifically, it can automatically reproduce operations that a user typically performs, efficiently executing tasks. For example, it can automate tasks such as launching specific applications, changing settings, and data entry. The execution unit can also configure the task execution procedure. For example, it can execute tasks by reproducing specific operation procedures. This allows the execution unit to accurately reproduce user operations and execute tasks efficiently. Furthermore, the execution unit can monitor the task execution status and make corrections as needed. For example, if an error occurs during task execution, the execution unit automatically detects the error and makes appropriate corrections. This ensures task execution and improves operational efficiency. Additionally, the execution unit can record the task execution results for subsequent analysis and improvement. For example, it can record the success rate and execution time of executed tasks and use this data to improve tasks. This allows the execution unit to execute tasks efficiently and reliably, improving the overall system performance.

[0071] The execution unit can automatically execute approved tasks. For example, the execution unit can automatically execute tasks approved by the user. For example, the execution unit can execute user-approved tasks according to a schedule. The execution unit can also execute user-approved tasks in real time. For example, the execution unit can execute user-approved tasks immediately. This reduces the user's effort by automatically executing approved tasks. Some or all of the above processes in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input user-approved tasks into an AI model and have the AI ​​execute the tasks.

[0072] The suggestion unit can propose tasks that can be automated using natural language. For example, the suggestion unit can propose tasks that can be automated using natural language processing technology. For example, the suggestion unit can analyze user operation patterns using text analysis technology and propose tasks that can be automated. The suggestion unit can also make suggestions in a way that is easy for the user to understand using natural language generation technology. For example, the suggestion unit can make a suggestion to the user in the form of "Would you like to automate this task?" By proposing tasks in natural language, it becomes easier for the user to understand. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input user operation patterns into an AI model and have the AI ​​execute suggestions in natural language.

[0073] The data collection unit can collect the user's daily PC operations. For example, the data collection unit can collect information such as the user's application launches and file operations. For instance, the data collection unit can collect the launch history of applications that the user uses on a daily basis. The data collection unit can also collect information on the types of files the user manipulates and how frequently they are manipulated. For example, the data collection unit can identify the types of files that the user frequently manipulates and collect operation patterns based on that. This allows for a more accurate understanding of operation patterns by collecting daily PC operations. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's daily PC operations into an AI model and have the AI ​​perform the collection of operation patterns.

[0074] The analysis unit can analyze collected operation patterns and identify repetitive tasks. For example, the analysis unit can analyze operation patterns using machine learning algorithms. For instance, it can identify frequently performed operations from the collected operation patterns and identify them as repetitive tasks. The analysis unit can also evaluate the impact of specific operation patterns on business operations. For example, it can identify repetitive tasks such as periodic data entry or periodic report creation. This allows for the clarification of tasks that can be automated by identifying repetitive tasks. Some or all of the above processing in the analysis unit may be performed using AI, or not. For example, the analysis unit can input collected operation patterns into an AI model and have the AI ​​perform the identification of repetitive tasks.

[0075] The execution unit can emulate screen operations and keyboard input. For example, the execution unit can emulate screen operations performed by a user. For example, the execution unit can emulate window operations and button clicks. The execution unit can also emulate keyboard input performed by a user. For example, the execution unit can emulate text input and the use of shortcut keys. In this way, user operations can be reproduced by emulating screen operations and keyboard input. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input the user's screen operations and keyboard input into an AI model and have the AI ​​perform the emulation.

[0076] The analysis unit can learn from the results of task execution and new operating patterns on a daily basis. For example, the analysis unit can learn from the results of task execution using machine learning algorithms. For example, the analysis unit can identify and learn new operating patterns based on the results of task execution. The analysis unit can also evaluate the results of task execution and analyze the degree of efficiency improvement. For example, the analysis unit can evaluate the degree of efficiency improvement based on the results of task execution and reflect it in the next proposal. In this way, the accuracy of the system improves through daily learning. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the results of task execution into an AI model and have the AI ​​perform the learning.

[0077] The suggestion unit can propose or implement appropriate countermeasures when unusual situations or unexpected errors occur. For example, the suggestion unit can detect unusual situations using an anomaly detection algorithm. For example, the suggestion unit can analyze system operation logs and detect abnormal patterns. The suggestion unit can also propose appropriate countermeasures when unexpected errors occur. For example, the suggestion unit can analyze error messages and propose appropriate countermeasures to the user. This improves the reliability of the system by proposing or implementing countermeasures in the event of anomalies. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input an anomaly detection algorithm into an AI model and have the AI ​​perform anomaly detection and propose countermeasures. Sentiment estimation is implemented using sentiment estimation functions, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0078] The proposal unit can analyze the execution status and efficiency improvements of automated tasks and automatically generate reports. For example, the proposal unit can monitor the execution status of tasks and evaluate the degree of efficiency improvements based on that data. For example, the proposal unit can analyze the execution time and error frequency of tasks and evaluate the degree of efficiency improvements. The proposal unit can also automatically generate reports based on the results of the efficiency improvement evaluation. For example, the proposal unit can generate reports that visually display the degree of efficiency improvements as graphs or charts. This makes it easier for users to understand the degree of efficiency improvements by automatically generating reports. Some or all of the above processes in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input task execution status data into an AI model and have the AI ​​perform the efficiency improvement evaluation and report generation.

[0079] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. The data collection unit can estimate the user's emotions using, for example, facial recognition technology. For example, the data collection unit can analyze the user's facial expression data captured by a camera and estimate the emotions. The data collection unit can also estimate the user's emotions using voice analysis technology. For example, the data collection unit can analyze the tone and speed of the user's voice and estimate the emotions. This reduces the user's burden by adjusting the data collection timing 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 is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input the user's emotion data into an AI model and have the AI ​​adjust the data collection timing.

[0080] The data collection unit can analyze the user's past operation history and select the optimal data collection method. For example, the data collection unit can analyze past operation history data and select the optimal data collection method. For example, the data collection unit can prioritize collecting operations that the user has frequently performed in the past. The data collection unit can also predict operations that will be performed during specific time periods and concentrate data collection during those times. For example, the data collection unit can predict operations that will be performed during specific time periods based on the user's operation history and collect data during those times. This allows for the selection of an efficient data collection method 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 the user's past operation history data into an AI model and have the AI ​​select the optimal data collection method.

[0081] The data collection unit can filter operation patterns based on the user's current work situation and areas of interest when collecting them. For example, the data collection unit can monitor the user's work situation and filter operation patterns based on that. For example, if the user is focused on a specific project, the data collection unit can prioritize collecting operation patterns related to that project. The data collection unit can also filter relevant operation patterns based on the user's areas of interest. For example, the data collection unit can prioritize collecting operation patterns related to the user's areas of interest. This allows for the priority collection of important operation patterns by filtering based on work situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's work situation data into an AI model and have the AI ​​perform the filtering.

[0082] The data collection unit can estimate the user's emotions and determine the priority of operation patterns to collect based on the estimated user emotions. The data collection unit can estimate the user's emotions using, for example, facial recognition technology. For example, the data collection unit can analyze the user's facial expression data captured by a camera and estimate emotions. The data collection unit can also estimate the user's emotions using voice analysis technology. For example, the data collection unit can analyze the tone and speed of the user's voice and estimate emotions. This enables efficient data collection by determining the priority of operation patterns 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 is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input user emotion data into an AI model and have the AI ​​determine the priority of operation patterns.

[0083] The data collection unit can prioritize the collection of highly relevant patterns by considering the user's geographical location information when collecting operation patterns. For example, the data collection unit can acquire the user's geographical location information using GPS data. For example, the data collection unit can prioritize the collection of operation patterns performed by the user at a specific location. The data collection unit can also acquire the user's geographical location information using an IP address. For example, the data collection unit can identify the geographical location from the user's IP address and prioritize the collection of operation patterns performed at that location. This allows for the priority collection of highly relevant operation patterns by considering 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 the user's geographical location data into an AI model and have the AI ​​perform the collection of operation patterns.

[0084] The data collection unit can analyze the user's social media activity and collect relevant patterns when collecting operation patterns. For example, the data collection unit can analyze the user's social media activity data and collect relevant operation patterns. For example, the data collection unit can prioritize collecting operation patterns that the user frequently performs on social media. The data collection unit can also filter relevant operation patterns based on the user's social media activity. For example, the data collection unit can prioritize collecting operation patterns related to the user's social media activity. This allows for the efficient collection of relevant operation patterns by analyzing 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 the user's social media activity data into an AI model and have the AI ​​perform the operation pattern collection.

[0085] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, the analysis unit can estimate the user's emotions using facial recognition technology. For example, the analysis unit can analyze facial expression data of the user captured by a camera and estimate the emotions. The analysis unit can also estimate the user's emotions using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the user's voice and estimate the emotions. This makes it easier for the user to understand 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 is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into an AI model and have the AI ​​adjust the presentation of the analysis.

[0086] The analysis unit can adjust the level of detail of the analysis based on the importance of the operation patterns during the analysis. For example, the analysis unit can evaluate the importance of operation patterns and adjust the level of detail of the analysis based on that evaluation. For example, the analysis unit can perform a detailed analysis for highly important operation patterns and a concise analysis for less important operation patterns. The analysis unit can also dynamically adjust the level of detail of the analysis according to the importance of the operation patterns. For example, the analysis unit can adjust the level of detail of the analysis in real time based on the importance of the operation patterns. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the operation patterns. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the importance data of operation patterns into an AI model and have the AI ​​perform the adjustment of the level of detail of the analysis.

[0087] The analysis unit can apply different analysis algorithms depending on the category of the operation pattern during analysis. For example, the analysis unit can identify the category of the operation pattern and apply the most suitable analysis algorithm accordingly. For example, the analysis unit can apply a specific algorithm to data entry operations and a different algorithm to report creation operations. The analysis unit can also select the most suitable analysis algorithm depending on the category of the operation pattern. For example, the analysis unit can dynamically select the most suitable analysis algorithm based on the category of the operation pattern. This improves the accuracy of the analysis by applying the most suitable analysis algorithm according to the category of the operation pattern. Some or all of the above processes 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 pattern into an AI model and have the AI ​​perform the application of the analysis algorithm.

[0088] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, the analysis unit can estimate the user's emotions using facial recognition technology. For example, the analysis unit can analyze the user's facial expression data captured by a camera and estimate the emotions. The analysis unit can also estimate the user's emotions using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the user's voice and estimate the emotions. By adjusting the length of the analysis according to the user's emotions, the analysis results can be provided that meet the user's needs. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's emotion data into an AI model and have the AI ​​adjust the length of the analysis.

[0089] The analysis unit can determine the priority of analysis based on the submission timing of operation patterns during analysis. For example, the analysis unit can evaluate the submission timing of operation patterns and determine the priority of analysis based on that. For example, the analysis unit can prioritize the analysis of recently submitted operation patterns and postpone the analysis of older operation patterns. The analysis unit can also dynamically adjust the priority of analysis based on the submission timing. For example, the analysis unit can adjust the priority of analysis in real time based on the submission timing. This enables efficient analysis by determining the priority of analysis based on the submission timing. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input operation pattern submission timing data into an AI model and have the AI ​​perform the determination of analysis priority.

[0090] The analysis unit can adjust the order of analysis based on the relevance of the operation patterns during analysis. For example, the analysis unit can evaluate the relevance of the operation patterns and adjust the order of analysis based on that evaluation. For example, the analysis unit can prioritize the analysis of highly relevant operation patterns and postpone the analysis of less relevant operation patterns. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the operation patterns. For example, the analysis unit can adjust the order of analysis in real time based on the relevance of the operation patterns. This enables efficient analysis by adjusting the order of analysis based on the relevance of the operation patterns. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the relevance data of the operation patterns into an AI model and have the AI ​​perform the adjustment of the order of analysis.

[0091] The proposal unit can estimate the user's emotions and adjust the way the proposal is presented based on the estimated emotions. For example, the proposal unit can estimate the user's emotions using facial recognition technology. For example, the proposal unit can analyze the user's facial expression data captured by a camera and estimate the emotions. The proposal unit can also estimate the user's emotions using voice analysis technology. For example, the proposal unit can analyze the tone and speed of the user's voice and estimate the emotions. By adjusting the way the proposal is presented according to the user's emotions, it becomes easier for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input user emotion data into an AI model and have the AI ​​adjust the way the proposal is presented.

[0092] The proposal unit can adjust the level of detail of a proposal based on the importance of the task. For example, the proposal unit can evaluate the importance of the task and adjust the level of detail of the proposal accordingly. For example, the proposal unit can provide detailed proposals for high-importance tasks and concise proposals for low-importance tasks. The proposal unit can also dynamically adjust the level of detail of a proposal according to the importance of the task. For example, the proposal unit can adjust the level of detail of a proposal in real time based on the importance of the task. This allows for efficient proposals by adjusting the level of detail of a proposal based on the importance of the task. Some or all of the above processes in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input task importance data into an AI model and have the AI ​​perform the adjustment of the level of detail of the proposal.

[0093] The proposal unit can apply different proposal algorithms depending on the task category when making a proposal. For example, the proposal unit can identify the task category and apply the most suitable proposal algorithm accordingly. For example, the proposal unit can apply a specific algorithm to data entry tasks and a different algorithm to report creation tasks. The proposal unit can also select the most suitable proposal algorithm depending on the task category. For example, the proposal unit can dynamically select the most suitable proposal algorithm based on the task category. This improves the accuracy of the proposal by applying the most suitable proposal algorithm according to the task category. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input task category data into an AI model and have the AI ​​perform the application of the proposal algorithm.

[0094] The suggestion unit can estimate the user's emotions and adjust the length of the suggestion based on the estimated emotions. For example, the suggestion unit can estimate the user's emotions using facial recognition technology. For example, it can analyze facial expression data of the user captured by a camera and estimate the emotions. The suggestion unit can also estimate the user's emotions using voice analysis technology. For example, it can analyze the tone and speed of the user's voice and estimate the emotions. This allows for suggestions tailored to the user's needs by adjusting the length of the suggestion 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 is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI, or not using AI. For example, the suggestion unit can input user emotion data into an AI model and have the AI ​​adjust the length of the suggestion.

[0095] The proposal department can determine the priority of proposals based on the task submission timing when submitting a proposal. For example, the proposal department can evaluate the task submission timing and determine the priority of proposals based on that. For example, the proposal department can prioritize recently submitted tasks and postpone older tasks. The proposal department can also dynamically adjust the priority of proposals based on submission timing. For example, the proposal department can adjust the priority of proposals in real time based on submission timing. This enables efficient proposals by determining the priority of proposals based on submission timing. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input task submission timing data into an AI model and have the AI ​​perform the determination of proposal priority.

[0096] The proposal unit can adjust the order of proposals based on the relevance of the tasks during the proposal process. For example, the proposal unit can evaluate the relevance of tasks and adjust the order of proposals accordingly. For example, the proposal unit can prioritize highly relevant tasks and postpone less relevant tasks. The proposal unit can also dynamically adjust the order of proposals based on the relevance of tasks. For example, the proposal unit can adjust the order of proposals in real time based on the relevance of tasks. This allows for efficient proposals by adjusting the order of proposals based on the relevance of tasks. Some or all of the above processes in the proposal unit may be performed using AI, for example, or not. For example, the proposal unit can input task relevance data into an AI model and have the AI ​​perform the adjustment of the order of proposals.

[0097] The execution unit can estimate the user's emotions and adjust the execution method based on the estimated user emotions. For example, the execution unit can estimate the user's emotions using facial recognition technology. For example, the execution unit can analyze the user's facial expression data captured by a camera and estimate the emotions. The execution unit can also estimate the user's emotions using voice analysis technology. For example, the execution unit can analyze the tone and speed of the user's voice and estimate the emotions. This makes it easier for the user to understand by adjusting the execution 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 is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input user emotion data into an AI model and have the AI ​​adjust the execution method.

[0098] The execution unit can analyze the user's past operation history during execution to select the optimal execution method. For example, the execution unit can analyze past operation history data and select the optimal execution method. For example, the execution unit can select the optimal execution method based on operations performed by the user in the past. The execution unit can also propose an efficient execution method from the user's operation history. For example, the execution unit analyzes the user's past operation history and selects the most effective execution method. In this way, an efficient execution method can be selected by analyzing past operation history. Some or all of the above processing in the execution unit may be performed using AI, for example, or without using AI. For example, the execution unit can input the user's past operation history data into an AI model and have the AI ​​select the optimal execution method.

[0099] The execution unit can customize the execution methods at runtime based on the user's current work situation. For example, the execution unit can monitor the user's work situation and customize the execution methods accordingly. For example, if the user is focused on a specific project, the execution unit can prioritize tasks related to that project. The execution unit can also prioritize important tasks, taking into account the user's current work situation. For example, the execution unit can dynamically customize the execution methods based on the user's work situation. This enables efficient execution by customizing the execution methods based on the work situation. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input user work situation data into an AI model and have the AI ​​perform the customization of the execution methods.

[0100] The execution unit can estimate the user's emotions and determine the execution priority based on the estimated user emotions. For example, the execution unit can estimate the user's emotions using facial recognition technology. For example, the execution unit can analyze the user's facial expression data captured by a camera and estimate the emotions. The execution unit can also estimate the user's emotions using voice analysis technology. For example, the execution unit can analyze the tone and speed of the user's voice and estimate the emotions. This enables efficient execution by determining the execution priority 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 is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input user emotion data into an AI model and have the AI ​​determine the execution priority.

[0101] The execution unit can select the optimal execution method at runtime, taking into account the user's geographical location information. For example, the execution unit can obtain the user's geographical location information using GPS data. For example, the execution unit can prioritize the execution of tasks performed by the user at a specific location. The execution unit can also obtain the user's geographical location information using an IP address. For example, the execution unit can identify the geographical location from the user's IP address and prioritize the execution of tasks performed at that location. This allows for the priority execution of highly relevant tasks by considering geographical location information. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input the user's geographical location data into an AI model and have the AI ​​select the optimal execution method.

[0102] The execution unit can analyze the user's social media activity at runtime and propose means of execution. For example, the execution unit can analyze the user's social media activity data and execute related tasks. For example, the execution unit can prioritize the execution of tasks that the user frequently performs on social media. The execution unit can also filter related tasks based on the user's social media activity. For example, the execution unit can prioritize the execution of tasks related to the user's social media activity. This allows for the efficient execution of related tasks by analyzing social media activity. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input the user's social media activity data into an AI model and have the AI ​​propose means of execution.

[0103] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0104] The Daily Learner system can also be equipped with the ability to estimate the user's emotions and dynamically adjust task priorities based on those emotions. For example, if a user is feeling stressed, the system can detect this emotion and prioritize simpler tasks to alleviate it. Similarly, if the system estimates the user is focused, it can prioritize more complex tasks. Furthermore, if the system estimates the user is tired, it can suggest taking a break. This allows for task prioritization based on the user's emotions, thereby improving the user's work efficiency.

[0105] The Daily Learner system can also be equipped with a function to analyze the user's past activity history and select the optimal method for suggesting tasks. For example, it can analyze patterns of tasks the user has previously approved and suggest similar tasks in a format the user prefers. It can also predict the user's activities during specific time periods and suggest tasks suitable for those times. Furthermore, it can analyze the reasons why the user has previously rejected tasks and take those reasons into consideration when suggesting similar tasks. This enables optimal suggestions based on the user's past activity history, thereby improving the acceptance rate of suggestions.

[0106] The Daily Learner system can also incorporate a function to suggest tasks that take into account the user's geographical location. For example, if a user is in a specific location, tasks related to that location can be prioritized. If a user is on the move, tasks that can be performed while traveling can be suggested. Furthermore, the system can predict the time of day when a user will be in a specific location and suggest tasks suitable for that time. This enables optimal task suggestions based on the user's geographical location, thereby improving the user's work efficiency.

[0107] The Daily Learner system can further incorporate features that analyze users' social media activity and suggest relevant tasks. For example, it can analyze the user's frequent social media activity patterns and suggest relevant tasks based on that. It can also suggest tasks related to specific topics if the user shows interest in them on social media. Furthermore, it can suggest new tasks that the user might be interested in based on their social media activity. This enables optimal task suggestions based on the user's social media activity, thereby improving the user's work efficiency.

[0108] The Daily Learner system can also be equipped with the ability to estimate the user's emotions and adjust the task execution method based on those emotions. For example, if the user is feeling stressed, the system can detect that emotion and adjust the task execution speed to reduce stress. If the system estimates the user is focused, it can speed up the task execution. Furthermore, if the system estimates the user is tired, it can pause the task execution and suggest a break. This allows for adjustments to task execution methods in response to the user's emotions, thereby improving the user's work efficiency.

[0109] The Daily Learner system can further incorporate a feature that customizes task suggestions based on the user's current work status. For example, if a user is focused on a specific project, tasks related to that project can be prioritized. If a user is working on multiple projects simultaneously, appropriate tasks can be suggested according to the progress of each project. Furthermore, the system can monitor the user's work status in real time and dynamically adjust task suggestions accordingly. This enables optimal task suggestions based on the user's current work situation, thereby improving the user's work efficiency.

[0110] The Daily Learner system can also be equipped with the ability to estimate the user's emotions and adjust the way tasks are suggested based on those emotions. For example, if a user is feeling stressed, the system can detect that emotion and suggest tasks using gentle language to alleviate stress. If the system estimates that the user is focused, it can suggest tasks using concise and clear language. Furthermore, if the system estimates that the user is tired, it can suggest taking a break. This allows for the adjustment of task suggestions according to the user's emotions, thereby improving the user's work efficiency.

[0111] The Daily Learner system can also be equipped with the ability to estimate the user's emotions and adjust the timing of task execution based on those emotions. For example, if a user is feeling stressed, the system can detect this emotion and temporarily delay task execution to alleviate the stress. Conversely, if the system estimates that the user is focused, it can immediately execute the task. Furthermore, if the system estimates that the user is tired, it can pause task execution and suggest a break. This allows for adjustment of task execution timing according to the user's emotions, thereby improving the user's work efficiency.

[0112] The Daily Learner system can also be equipped with the ability to estimate the user's emotions and customize how tasks are performed based on those emotions. For example, if a user is feeling stressed, the system can detect that emotion and change how tasks are performed to reduce stress. If the system estimates that the user is focused, it can make the task execution more efficient. Furthermore, if the system estimates that the user is tired, it can pause the task and suggest a break. This allows for the customization of task execution methods according to the user's emotions, thereby improving the user's work efficiency.

[0113] The Daily Learner system can further incorporate features that customize how tasks are executed based on the user's current work status. For example, if a user is focused on a specific project, tasks related to that project can be prioritized. Similarly, if a user is working on multiple projects simultaneously, the system can execute appropriate tasks according to the progress of each project. Furthermore, it can monitor the user's work status in real time and dynamically adjust task execution methods accordingly. This enables optimal task execution based on the user's current work status, thereby improving the user's work efficiency.

[0114] The following briefly describes the processing flow for example form 2.

[0115] Step 1: The data collection unit collects user operation patterns. For example, it can collect the user's mouse clicks, keyboard input, and application usage history. The data collection unit can also configure the frequency of collection and the types of data to be collected. For example, it can collect operation patterns at a certain time each day, or prioritize the collection of usage history for specific applications. Step 2: The analysis unit analyzes the operation patterns collected by the data collection unit. For example, machine learning algorithms can be used to analyze operation patterns and evaluate their frequency and importance. Frequently occurring operation patterns can be identified, and automation suggestions can be made based on these. It is also possible to evaluate the impact that specific operation patterns have on business operations. Step 3: The proposal department proposes tasks that can be automated based on the operation patterns analyzed by the analysis department. For example, it can propose tasks that can be automated using natural language processing technology and set the format and timing of the proposals. Automation proposals can be made in a way that is easy for users to understand and at an appropriate time according to the user's work situation. Step 4: The execution unit executes the tasks proposed by the proposal unit. For example, it can execute tasks by emulating screen operations or keyboard input, and it can set the procedure for executing the tasks. It can also execute tasks by reproducing specific operation procedures, monitor the task execution status, and make corrections as needed.

[0116] 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.

[0117] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0118] 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.

[0119] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and execution unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit can collect the user's mouse clicks and keyboard inputs using the control unit 46A of the smart device 14. The analysis unit analyzes the collected operation patterns using a machine learning algorithm, for example, using the specific processing unit 290 of the data processing unit 12. The proposal unit proposes tasks that can be automated in natural language, for example, using the specific processing unit 290 of the data processing unit 12. The execution unit executes the tasks by emulating screen operations and keyboard inputs using the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0120] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.).

[0132] 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.

[0133] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0134] 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.

[0135] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and execution unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit can collect the user's mouse clicks and keyboard inputs using the control unit 46A of the smart glasses 214. The analysis unit analyzes the collected operation patterns using a machine learning algorithm, for example, using the specific processing unit 290 of the data processing unit 12. The proposal unit proposes tasks that can be automated in natural language, for example, using the specific processing unit 290 of the data processing unit 12. The execution unit executes the tasks by emulating screen operations and keyboard inputs using the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0136] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.).

[0148] 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.

[0149] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0150] 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.

[0151] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and execution unit, is implemented in, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit can collect the user's mouse clicks and keyboard inputs using the control unit 46A of the headset terminal 314. The analysis unit analyzes the collected operation patterns using a machine learning algorithm, for example, the specific processing unit 290 of the data processing unit 12. The proposal unit proposes tasks that can be automated in natural language, for example, using the specific processing unit 290 of the data processing unit 12. The execution unit executes the tasks by emulating screen operations and keyboard inputs using, for example, the control unit 46A of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0152] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.).

[0165] 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.

[0166] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0167] 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.

[0168] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and execution unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit can collect the user's mouse clicks and keyboard inputs by the control unit 46A of the robot 414. The analysis unit analyzes the collected operation patterns using a machine learning algorithm by the specific processing unit 290 of the data processing unit 12. The proposal unit proposes tasks that can be automated in natural language by the specific processing unit 290 of the data processing unit 12. The execution unit executes tasks by emulating screen operations and keyboard inputs by the control unit 46A of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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."

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] (Note 1) A collection unit that collects user operation patterns, An analysis unit analyzes the operation patterns collected by the collection unit, A proposal unit proposes tasks that can be automated based on the operation patterns analyzed by the analysis unit, The system comprises an execution unit that performs the tasks proposed by the proposal unit. A system characterized by the following features. (Note 2) The execution unit is, Automatically execute approved tasks. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, Suggest tasks that can be automated using natural language. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is Collect data on users' daily PC usage. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit is Analyze collected operation patterns to identify repetitive tasks. The system described in Appendix 1, characterized by the features described herein. (Note 6) The execution unit is, Emulates screen operations and keyboard input. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit is Learn daily from the results of task execution and new operation patterns. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned proposal section is, In the event of unusual circumstances or unexpected errors, we will suggest or implement appropriate countermeasures. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned proposal section is, Analyze the performance and efficiency improvements of automated tasks, and automatically generate reports. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of collecting interaction patterns based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is Analyze the user's past operation history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting operation patterns, filtering is performed based on the user's current work situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is It estimates the user's emotions and determines the priority of the interaction patterns to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is When collecting operation patterns, the system prioritizes collecting patterns that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned collection unit is When collecting operational patterns, the system analyzes users' social media activity and collects relevant patterns. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is It estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During analysis, adjust the level of detail based on the importance of the operational patterns. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is During analysis, different analysis algorithms are applied depending on the category of the operation pattern. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit is It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit is During analysis, the priority of the analysis is determined based on when the operation patterns were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit is During analysis, adjust the order of analysis based on the relevance of the operation patterns. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the task. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making a proposal, apply a different proposal algorithm depending on the task category. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, When submitting a proposal, prioritize the proposals based on the task submission deadline. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the tasks. The system described in Appendix 1, characterized by the features described herein. (Note 28) The execution unit is, It estimates the user's emotions and adjusts the execution method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The execution unit is, During execution, the system analyzes the user's past operation history to select the optimal execution method. The system described in Appendix 1, characterized by the features described herein. (Note 30) The execution unit is, At runtime, the execution method is customized based on the user's current work situation. The system described in Appendix 1, characterized by the features described herein. (Note 31) The execution unit is, It estimates the user's emotions and determines the priority of actions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The execution unit is, During execution, the system selects the optimal execution method, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 33) The execution unit is, During execution, the system analyzes the user's social media activity and suggests implementation methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0188] 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 user operation patterns, An analysis unit analyzes the operation patterns collected by the collection unit, A proposal unit proposes tasks that can be automated based on the operation patterns analyzed by the analysis unit, The system comprises an execution unit that performs the tasks proposed by the proposal unit. A system characterized by the following features.

2. The execution unit is, Automatically execute approved tasks. The system according to feature 1.

3. The aforementioned proposal section is, Suggest tasks that can be automated using natural language. The system according to feature 1.

4. The aforementioned collection unit is Collect data on users' daily PC usage. The system according to feature 1.

5. The aforementioned analysis unit is Analyze collected operation patterns to identify repetitive tasks. The system according to feature 1.

6. The execution unit is, Emulates screen operations and keyboard input. The system according to feature 1.

7. The aforementioned analysis unit is Learn daily from the results of task execution and new operation patterns. The system according to feature 1.

8. The aforementioned proposal section is, In the event of unusual circumstances or unexpected errors, we will suggest or implement appropriate countermeasures. The system according to feature 1.

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