system

Abstract

The system according to the embodiment aims to automate repetitive routine tasks performed by employees and improve work efficiency. [Solution] A system according to an embodiment includes a monitoring unit, an identification unit, a proposal unit, an acquisition unit, a learning unit, a generation unit, and a security unit. The monitoring unit monitors the operation of employees' PC monitors. The identification unit identifies repetitive routine tasks based on the operations monitored by the monitoring unit. The proposal unit proposes automation based on the repetitive routine tasks identified by the identification unit. The acquisition unit acquires logs for the automation proposed by the proposal unit. The learning unit trains a generation AI based on the logs acquired by the acquisition unit. The generation unit trains the generation AI generated by the learning unit to become more familiar with the business. The security unit implements specific privacy and security measures.

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has had the challenge of making it difficult to efficiently automate employees' repetitive routine tasks, limiting the extent to which it can improve work efficiency.

[0005] The system according to the embodiment aims to automate repetitive routine tasks performed by employees and improve work efficiency. [Means for solving the problem]

[0006] The system according to the embodiment includes a monitoring unit, an identification unit, a proposal unit, an acquisition unit, a learning unit, a generation unit, and a security unit. The monitoring unit monitors the operation of employees' PC monitors. The identification unit identifies repetitive routine tasks based on the operations monitored by the monitoring unit. The proposal unit proposes automation based on the repetitive routine tasks identified by the identification unit. The acquisition unit acquires logs for the automation proposed by the proposal unit. The learning unit trains the generation AI based on the logs acquired by the acquisition unit. The generation unit trains the generation AI generated by the learning unit to become more familiar with the business. The security unit implements specific privacy and security measures. [Effects of the Invention]

[0007] The system according to the embodiment can automate repetitive routine tasks performed by employees and improve work efficiency. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

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

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

[0028] (Example 1) A business efficiency system according to an embodiment of the present invention monitors the operations of employees' PC monitors, automatically identifies repetitive routine tasks, and proposes automation for them. This business efficiency system monitors the operations of employees' PC monitors, identifies repetitive routine tasks, and proposes automation for them. Furthermore, a generating AI learns from the acquired logs, resulting in a generating AI that is familiar with the tasks. The system supports organizational productivity improvement while maintaining strict privacy and security measures. For example, the business efficiency system includes a monitoring unit that monitors the operations of employees' PC monitors. The monitoring unit monitors specific operations and identifies which operations are repetitive routine tasks. For example, it monitors operations that involve a lot of routine work, such as data entry and standard report creation. Next, an identification unit identifies the repetitive routine tasks. The identification unit analyzes the operations monitored by the monitoring unit and identifies which operations are repetitive routine tasks. For example, it identifies operations that involve entering the same data every day or creating the same report periodically. Furthermore, a suggestion unit proposes automation. The suggestion unit proposes automation based on the repetitive routine tasks identified by the identification unit. For example, it proposes tools for automating data entry and software for automating report creation. Next, the acquisition department acquires logs. The acquisition department collects employee operation logs and provides data for the generation AI to learn. For example, it records what operations employees performed and which operations are repetitive routine tasks. Next, the learning department trains the generation AI. The learning department trains the generation AI based on the logs acquired by the acquisition department, creating a generation AI that is familiar with the work. For example, the generation AI learns data entry methods and report creation procedures, becoming familiar with the work. Finally, the security department implements strict privacy and security measures. The security department protects employee operation logs and the generation AI's learning data to ensure privacy and security. For example, it encrypts data and restricts access to prevent data leaks. This allows the business efficiency system to monitor employees' PC monitors, automatically identify repetitive routine tasks, and suggest automation for them.In addition, the generation AI learns based on the acquired logs, creating a generation AI that is familiar with the business, helping to improve organizational productivity.

[0029] A business efficiency improvement system according to an embodiment includes a monitoring unit, an identification unit, a proposal unit, an acquisition unit, a learning unit, a generation unit, and a security unit. The monitoring unit monitors the operations of employees' PC monitors. For example, the monitoring unit monitors specific operations and identifies which operations are repetitive routine tasks. For example, the monitoring unit monitors operations that involve a lot of routine work, such as data entry and standard report creation. The monitoring unit can also monitor the launch of specific applications and operations on specific files. The identification unit identifies repetitive routine tasks based on the operations monitored by the monitoring unit. For example, the identification unit identifies operations that input the same data every day or operations that periodically create the same report. The identification unit analyzes the operations monitored by the monitoring unit and identifies which operations are repetitive routine tasks. The proposal unit proposes automation based on the repetitive routine tasks identified by the identification unit. For example, the proposal unit proposes tools for automating data entry or software for automating report creation. The proposal unit proposes automation based on the repetitive routine tasks identified by the identification unit. The acquisition unit acquires employee operation logs. The acquisition unit records, for example, what operations the employee performed and which operations are repetitive routine tasks. The acquisition unit collects employee operation logs and provides data for the generation AI to learn. The learning unit causes the generation AI to learn based on the logs acquired by the acquisition unit. For example, the learning unit causes the generation AI to learn data entry methods and report creation procedures so as to become familiar with the work. The generation unit causes the generation AI generated by the learning unit to learn data entry methods and report creation procedures so as to become familiar with the work. For example, the generation unit causes the generation AI to learn data entry methods and report creation procedures so as to become familiar with the work. The security unit implements strict privacy and security measures. For example, the security unit protects employee operation logs and the generation AI's learning data to ensure privacy and security. The security unit encrypts data and restricts access to prevent data leaks. As a result, the business efficiency improvement system according to the embodiment can monitor employee PC monitor operations, automatically identify repetitive routine tasks, and propose automation for them.In addition, the generation AI learns based on the acquired logs, creating a generation AI that is familiar with the business, helping to improve organizational productivity.

[0030] The monitoring unit can monitor specific operations. For example, the monitoring unit monitors the launch of a specific application or operations on a specific file. For example, the monitoring unit records the launch and shutdown times of a specific application. The monitoring unit can also record operations on a specific file. For example, the monitoring unit records operations such as creating, editing, and deleting a specific file. The monitoring unit can also record the usage status of a specific application in detail. For example, the monitoring unit records the usage time and frequency of a specific application. By monitoring specific operations, repetitive routine tasks can be more easily identified. Some or all of the above-described processing in the monitoring unit may be performed using, or without, AI. For example, the monitoring unit can input the launch and shutdown times of a specific application into AI, which can then analyze the data to identify specific operations.

[0031] The identification unit can identify specific routine tasks. For example, the identification unit can identify operations such as entering the same data every day or creating the same report on a regular basis. The identification unit analyzes the operations monitored by the monitoring unit and identifies which operations are repetitive routine tasks. For example, the identification unit can analyze data entry operations to identify operations such as entering the same data every day. The identification unit can also analyze report creation operations to identify operations such as creating the same report on a regular basis. For example, the identification unit can analyze the frequency and content of report creation operations to identify repetitive routine tasks. The identification unit can also analyze the usage of specific applications to identify repetitive routine tasks. For example, the identification unit can analyze the frequency and usage time of specific applications to identify repetitive routine tasks. This identification of specific routine tasks makes it possible to suggest automation. Some or all of the above-described processing in the identification unit may be performed using, or without, AI. For example, the identification unit can input data acquired by the monitoring unit into AI, and the AI ​​can analyze the data to identify specific routine tasks.

[0032] The suggestion unit can make specific automation suggestions. For example, the suggestion unit can suggest tools for automating data entry or software for automating report creation. The suggestion unit can make automation suggestions based on the repetitive routine tasks identified by the identification unit. For example, the suggestion unit can suggest tools for automating data entry. The suggestion unit can also suggest software for automating data entry. For example, the suggestion unit can suggest how to use tools or software for automating data entry. The suggestion unit can also suggest software for automating report creation. For example, the suggestion unit can suggest how to use software for automating report creation. By making specific automation suggestions, business efficiency can be improved. Some or all of the above-mentioned processing in the suggestion unit can be performed using, for example, AI, or can be performed without using AI. For example, the suggestion unit can input data acquired by the identification unit into AI, and the AI ​​can analyze the data and make automation suggestions.

[0033] The acquisition unit can acquire employee operation logs. For example, the acquisition unit records what operations the employee performed and which operations are repetitive routine tasks. The acquisition unit collects employee operation logs and provides data for the generation AI to learn from. For example, the acquisition unit records the employee operation logs with a timestamp. The acquisition unit can also record the employee's operation details in detail. For example, the acquisition unit records the operation details of applications and files used by the employee. The acquisition unit can also filter the employee operation logs to record only specific operations. For example, the acquisition unit can record only data entry operations. The acquisition unit can also record only report creation operations. In this way, acquiring employee operation logs can provide data for the generation AI to learn from. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the employee's operation logs into AI, and the AI ​​can analyze the data to acquire the logs.

[0034] The learning unit can cause the generation AI to learn. For example, the learning unit causes the generation AI to learn based on the logs acquired by the acquisition unit. For example, the learning unit causes the generation AI to learn data entry methods and report creation procedures, thereby becoming more familiar with the business. The learning unit causes the generation AI to learn based on the logs acquired by the acquisition unit. For example, the learning unit causes the generation AI to learn data entry methods and report creation procedures, thereby becoming more familiar with the business. The learning unit provides the generation AI with data and documents related to the business and causes the generation AI to learn based on the data. For example, the learning unit provides the generation AI with data for learning data entry procedures and report creation methods. The learning unit can also provide the generation AI with business process data and cause the generation AI to learn based on the data. For example, the learning unit provides the generation AI with business process data and causes the generation AI to learn based on the data. As a result, the generation AI becomes more familiar with the business, enabling more accurate automation suggestions. Some or all of the above-mentioned processing in the learning unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the learning unit can input the log acquired by the acquisition unit into the generation AI, and the generation AI can analyze the data and learn.

[0035] The generation unit can train the generation AI to become more familiar with the business. For example, the generation unit causes the generation AI to learn data entry methods and report creation procedures, thereby becoming more familiar with the business. The generation unit causes the generation AI generated by the learning unit to learn to become more familiar with the business. For example, the generation unit provides the generation AI with data and documents related to the business and causes it to learn based on the data. For example, the generation unit provides the generation AI with data for learning data entry procedures and report creation methods. The generation unit can also provide the generation AI with business process data and cause it to learn based on the data. For example, the generation unit provides the generation AI with business process data and causes it to learn based on the data. This allows the generation AI to become more familiar with the business, thereby enabling more accurate automation suggestions. Some or all of the above-mentioned processing in the generation unit may be performed using the generation AI, for example, or may be performed without using the generation AI. For example, the generation unit can input data acquired by the learning unit into the generation AI, and the generation AI can analyze the data and learn.

[0036] The security unit can encrypt data or restrict access. The security unit can protect, for example, employee operation logs and the generation AI's learning data to ensure privacy and security. The security unit can encrypt data and restrict access to prevent data leakage. For example, the security unit can use AES encryption or RSA encryption as a data encryption method. The security unit can also perform user authentication and set access permissions as a method of access restriction. For example, the security unit can encrypt employee operation logs and set access permissions to prevent data leakage. The security unit can also encrypt the generation AI's learning data and set access permissions to prevent data leakage. In this way, data encryption and access restrictions can ensure privacy and security. Some or all of the above-mentioned processing in the security unit can be performed using, or without, AI. For example, the security unit can input data encryption and access restrictions into an AI, which can then analyze the data and implement security measures.

[0037] The monitoring unit can record the usage status of specific applications and software in detail. The monitoring unit, for example, records the start and end times of applications used by the user. The monitoring unit records the usage status of specific applications and software in detail. For example, the monitoring unit records the type and frequency of operations performed by the user on specific software. In addition, if a user uses multiple applications simultaneously, the monitoring unit can also record the frequency of switching between them and the usage time. This allows for detailed recording of the usage status of specific applications and software, thereby improving business efficiency. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the usage status of specific applications and software into AI, which then analyzes the data and records the usage status.

[0038] The monitoring unit can analyze the user's operation speed and pattern to detect abnormal operations. For example, if the user's operation speed changes suddenly, the monitoring unit detects the operation as abnormal. The monitoring unit analyzes the user's operation speed and pattern to detect abnormal operations. For example, the monitoring unit detects an operation pattern that the user does not normally perform and reports it as abnormal. The monitoring unit can also analyze operations that the user performs only during specific time periods and identify abnormal operations. In this way, by analyzing the user's operation speed and pattern, abnormal operations can be detected early. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's operation speed and pattern into AI, and the AI ​​can analyze the data to detect abnormal operations.

[0039] The monitoring unit can select the optimal monitoring method by taking into account the user's device information. For example, if the user is using a desktop PC, the monitoring unit performs full-screen monitoring. The monitoring unit selects the optimal monitoring method by taking into account the user's device information. For example, if the user is using a laptop PC, the monitoring unit selects a monitoring method that reduces battery consumption. Furthermore, if the user is using a tablet, the monitoring unit can also select a monitoring method that supports touch operations. In this way, the optimal monitoring method can be selected by taking into account the user's device information. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's device information into AI, which then analyzes the data and selects the optimal monitoring method.

[0040] The monitoring unit can improve the accuracy of monitoring by referring to the user's past operation history. The monitoring unit, for example, analyzes the user's past operation history and focuses on monitoring frequently performed operations. The monitoring unit improves the accuracy of monitoring by referring to the user's past operation history. For example, the monitoring unit detects abnormal operations early based on the user's past operation patterns. The monitoring unit can also predict operations performed during specific time periods from the user's past operation history and improve the accuracy of monitoring. In this way, the accuracy of monitoring is improved by referring to the user's past operation history. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's past operation history into AI, and the AI ​​can analyze the data to improve the accuracy of monitoring.

[0041] The identification unit can identify routine tasks by taking into account the frequency of operations and time periods. For example, the identification unit identifies operations that are performed at the same time period every day as routine tasks. The identification unit identifies routine tasks by taking into account the frequency of operations and time periods. For example, the identification unit identifies operations that are performed several times a week as routine tasks. The identification unit can also identify operations that are performed once a month as routine tasks. This makes it possible to accurately identify routine tasks by taking into account the frequency of operations and time periods. Some or all of the above-described processing in the identification unit may be performed using, or without, AI, for example. For example, the identification unit can input data on the frequency of operations and time periods into AI, and the AI ​​can analyze the data to identify routine tasks.

[0042] The identification unit can apply different identification algorithms depending on the type and content of the operation. For example, the identification unit applies a pattern recognition algorithm to a data entry operation. The identification unit applies different identification algorithms depending on the type and content of the operation. For example, the identification unit applies a natural language processing algorithm to a report creation operation. The identification unit can also apply a clustering algorithm to a file management operation. In this way, by applying different identification algorithms depending on the type and content of the operation, the accuracy of identification is improved. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input data on the type and content of the operation into AI, which analyzes the data and applies different identification algorithms.

[0043] The identification unit can determine the identification priority based on the user's job content and job title. For example, for a managerial user, the identification unit prioritizes identifying operations with high importance. The identification unit determines the identification priority based on the user's job content and job title. For example, for a general employee user, the identification unit prioritizes identifying everyday operations. The identification unit can also prioritize identifying basic operations for a new employee user. This enables efficient identification by determining the identification priority based on the user's job content and job title. Some or all of the above-described processing in the identification unit may be performed using, or without, AI, for example. For example, the identification unit can input data on the user's job content and job title into AI, and the AI ​​can analyze the data to determine the identification priority.

[0044] The identification unit can improve the accuracy of identification by referring to the user's operation history. For example, the identification unit analyzes the user's past operation history and focuses on identifying frequently performed operations. The identification unit improves the accuracy of identification by referring to the user's operation history. For example, the identification unit detects abnormal operations early based on the user's past operation patterns. The identification unit can also predict operations performed during specific time periods from the user's past operation history and improve the accuracy of identification. In this way, the accuracy of identification is improved by referring to the user's operation history. Some or all of the above-described processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input the user's operation history into AI, and the AI ​​can analyze the data to improve the accuracy of identification.

[0045] The suggestion unit can adjust the level of detail of the proposal based on the importance of the task. For example, the suggestion unit makes a detailed proposal for a task of high importance. The suggestion unit adjusts the level of detail of the proposal based on the importance of the task. For example, the suggestion unit makes a brief proposal for a task of low importance. The suggestion unit can also make a proposal with an appropriate level of detail for a task of medium importance. This enables efficient suggestions by adjusting the level of detail of the proposal based on the importance of the task. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input data on the importance of the task into AI, and the AI ​​can analyze the data and adjust the level of detail of the proposal.

[0046] The suggestion unit can apply different suggestion algorithms depending on the task category. For example, the suggestion unit applies a pattern recognition algorithm to a data entry task. The suggestion unit can apply different suggestion algorithms depending on the task category. For example, the suggestion unit can apply a natural language processing algorithm to a report creation task. The suggestion unit can also apply a clustering algorithm to a file management task. By applying different suggestion algorithms depending on the task category, the accuracy of suggestions is improved. Some or all of the above-mentioned processing in the suggestion unit can be performed using, or without, AI, for example. For example, the suggestion unit can input data of the task category into AI, which can analyze the data and apply different suggestion algorithms.

[0047] The suggestion unit can determine the priority of proposals based on the execution time of the tasks. For example, the suggestion unit prioritizes proposals for urgent tasks. The suggestion unit determines the priority of proposals based on the execution time of the tasks. For example, the suggestion unit makes an early proposal for a task with an approaching deadline. The suggestion unit can also postpone a proposal for a task with a distant deadline. In this way, efficient proposals can be made by determining the priority of proposals based on the execution time of the tasks. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input data on the execution time of tasks into AI, and the AI ​​can analyze the data to determine the priority of proposals.

[0048] The suggestion unit can adjust the order of suggestions based on the relevance of the tasks. For example, the suggestion unit prioritizes suggesting highly relevant tasks. The suggestion unit adjusts the order of suggestions based on the relevance of the tasks. For example, the suggestion unit puts less relevant tasks on hold before suggesting them. Furthermore, when multiple tasks are related, the suggestion unit can optimize the order of the tasks and suggest them. This enables efficient suggestions by adjusting the order of suggestions based on the relevance of the tasks. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input data on task relevance to AI, and the AI ​​can analyze the data and adjust the order of suggestions.

[0049] The acquisition unit can filter logs based on specific operations or events. For example, the acquisition unit acquires logs by filtering only data entry operations. The acquisition unit filters logs based on specific operations or events. For example, the acquisition unit acquires logs by filtering only report creation operations. The acquisition unit can also acquire logs by filtering only file management operations. In this way, by filtering logs based on specific operations or events, it is possible to acquire only necessary information. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input data on specific operations or events into AI, and the AI ​​can analyze the data and filter the logs.

[0050] The acquisition unit can adjust the level of detail of the log to acquire only necessary information. For example, the acquisition unit records only important operations in detail and records other operations in a simplified manner. The acquisition unit adjusts the level of detail of the log to acquire only necessary information. For example, the acquisition unit records all operations in detail and filters them later. The acquisition unit can also record only information about specific operations in detail and record other operations in a simplified manner. In this way, by adjusting the level of detail of the log, only necessary information can be acquired. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input data on the level of detail of the log into AI, and the AI ​​can analyze the data to acquire only necessary information.

[0051] The acquisition unit can select the optimal log acquisition method by taking into account the user's device information. For example, if the user is using a desktop PC, the acquisition unit acquires a full-screen log. The acquisition unit selects the optimal log acquisition method by taking into account the user's device information. For example, if the user is using a laptop PC, the acquisition unit selects a log acquisition method that reduces battery consumption. Furthermore, if the user is using a tablet, the acquisition unit can also select a log acquisition method that supports touch operations. In this way, the optimal log acquisition method can be selected by taking into account the user's device information. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's device information into AI, and the AI ​​can analyze the data to select the optimal log acquisition method.

[0052] The acquisition unit can improve the accuracy of the log by referring to the user's operation history. For example, the acquisition unit analyzes the user's past operation history and focuses on acquiring logs of frequently performed operations. The acquisition unit improves the accuracy of the log by referring to the user's operation history. For example, the acquisition unit acquires logs of abnormal operations early based on the user's past operation patterns. The acquisition unit can also predict logs of operations performed in specific time periods from the user's past operation history and improve accuracy. In this way, the accuracy of the log is improved by referring to the user's operation history. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's operation history into AI, and the AI ​​can analyze the data to improve the accuracy of the log.

[0053] The learning unit can optimize the learning algorithm by referring to past learning data. For example, the learning unit analyzes past learning data and selects an optimal algorithm. The learning unit optimizes the learning algorithm by referring to past learning data. For example, the learning unit identifies and applies an effective algorithm from past learning data. The learning unit can also adjust parameters of the algorithm based on past learning data. This makes it possible to optimize the learning algorithm by referring to past learning data. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the learning unit can input past learning data into a generation AI, which then analyzes the data to optimize the learning algorithm.

[0054] The learning unit can construct a learning model specialized for specific business content. For example, the learning unit constructs a learning model specialized for data entry work. The learning unit constructs a learning model specialized for specific business content. For example, the learning unit constructs a learning model specialized for report writing work. The learning unit can also construct a learning model specialized for file management work. This enables efficient learning by constructing a learning model specialized for specific business content. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the learning unit can input data specialized for specific business content into the generation AI, and the generation AI can analyze the data to construct a learning model.

[0055] The learning unit can weight the learning data based on the time when the log was acquired. For example, the learning unit weights recently acquired log data more heavily during learning. The learning unit weights the learning data based on the time when the log was acquired. For example, the learning unit weights older log data less heavily during learning. The learning unit can also weight log data acquired within a specific period of time during learning. This enables efficient learning by weighting the learning data based on the time when the log was acquired. Some or all of the above-described processing in the learning unit may be performed using, or without, a generation AI. For example, the learning unit can input data from the time when the log was acquired into the generation AI, and the generation AI can analyze the data and weight the learning data.

[0056] The learning unit can improve the accuracy of learning by referring to the user's operation history. For example, the learning unit analyzes the user's past operation history and focuses on learning frequently performed operations. The learning unit improves the accuracy of learning by referring to the user's operation history. For example, the learning unit early learns abnormal operations based on the user's past operation patterns. The learning unit can also predict operations performed during specific time periods from the user's past operation history and improve the accuracy of learning. In this way, the accuracy of learning is improved by referring to the user's operation history. Some or all of the above-described processing in the learning unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the learning unit can input the user's operation history into the generation AI, and the generation AI can analyze the data to improve the accuracy of learning.

[0057] The generation unit can construct a generation AI specialized for specific business content. The generation unit, for example, constructs a generation AI specialized for data entry work. The generation unit constructs a generation AI specialized for specific business content. For example, the generation unit constructs a generation AI specialized for report creation work. The generation unit can also construct a generation AI specialized for file management work. This enables efficient learning by constructing a generation AI specialized for specific business content. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input data specialized for specific business content into the generation AI, and the generation AI can analyze the data to construct the generation AI.

[0058] The generation unit can optimize the generation algorithm by referring to the past performance of the generation AI. For example, the generation unit analyzes the past performance of the generation AI and selects the optimal algorithm. The generation unit optimizes the generation algorithm by referring to the past performance of the generation AI. For example, the generation unit identifies and applies an effective algorithm from the past performance of the generation AI. The generation unit can also adjust the parameters of the algorithm based on the past performance of the generation AI. This makes it possible to optimize the generation algorithm by referring to the past performance of the generation AI. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, the generation AI, for example. For example, the generation unit can input the past performance of the generation AI into the generation AI, and the generation AI can analyze the data to optimize the generation algorithm.

[0059] The generation unit can customize the learning content of the generation AI based on the user's work content and job title. For example, the generation unit has a managerial user learn important work content. The generation unit customizes the learning content of the generation AI based on the user's work content and job title. For example, the generation unit has a general employee user learn everyday work content. The generation unit can also have a new employee user learn basic work content. This enables efficient learning by customizing the learning content of the generation AI based on the user's work content and job title. Some or all of the above-mentioned processing in the generation unit may be performed using the generation AI, for example, or may be performed without using the generation AI. For example, the generation unit can input data on the user's work content and job title into the generation AI, and the generation AI can analyze the data to customize the learning content of the generation AI.

[0060] The generation unit can improve the accuracy of the generation AI by referring to the user's operation history. For example, the generation unit analyzes the user's past operation history and focuses on learning frequently performed operations. The generation unit improves the accuracy of the generation AI by referring to the user's operation history. For example, the generation unit early learns abnormal operations based on the user's past operation patterns. The generation unit can also predict operations that will be performed during specific time periods from the user's past operation history and improve the accuracy of the generation AI. In this way, the accuracy of the generation AI is improved by referring to the user's operation history. Some or all of the above-mentioned processing in the generation unit may be performed using the generation AI, for example, or may be performed without using the generation AI. For example, the generation unit can input the user's operation history into the generation AI, and the generation AI can analyze the data to improve the accuracy of the generation AI.

[0061] The security unit can apply different encryption methods to specific data or operations. For example, the security unit applies a strong encryption method to important data. The security unit can apply different encryption methods to specific data or operations. For example, the security unit applies a standard encryption method to general data. The security unit can also apply an appropriate encryption method to specific operations. This enables efficient security measures by applying different encryption methods to specific data or operations. Some or all of the above-mentioned processing in the security unit may be performed using, or without, AI, for example. For example, the security unit can input data for specific data or operations into AI, which then analyzes the data and applies different encryption methods.

[0062] The security department can optimize countermeasures by referring to past security incidents. For example, the security department analyzes past security incidents and selects optimal countermeasures. The security department optimizes countermeasures by referring to past security incidents. For example, the security department identifies and applies effective countermeasures from past security incidents. The security department can also adjust parameters of countermeasures based on past security incidents. This makes it possible to optimize countermeasures by referring to past security incidents. Some or all of the above-mentioned processing in the security department may be performed using, for example, AI, or may be performed without using AI. For example, the security department can input data on past security incidents into AI, which then analyzes the data and optimizes countermeasures.

[0063] The security unit can select optimal countermeasures by taking into account the user's device information. For example, if the user is using a desktop PC, the security unit implements full-screen security countermeasures. The security unit selects optimal countermeasures by taking into account the user's device information. For example, if the user is using a laptop PC, the security unit selects security countermeasures that reduce battery consumption. Furthermore, if the user is using a tablet, the security unit can also select security countermeasures that are compatible with touch operations. This allows optimal security countermeasures to be selected by taking into account the user's device information. Some or all of the above-described processing in the security unit may be performed using, for example, AI, or may be performed without using AI. For example, the security unit can input the user's device information into AI, which then analyzes the data to select optimal countermeasures.

[0064] The security unit can improve the accuracy of countermeasures by referring to the user's operation history. For example, the security unit analyzes the user's past operation history and implements security countermeasures focusing on frequently performed operations. The security unit improves the accuracy of countermeasures by referring to the user's operation history. For example, the security unit implements security countermeasures for abnormal operations early based on the user's past operation patterns. The security unit can also predict security countermeasures for operations performed during specific time periods from the user's past operation history and improve accuracy. In this way, the accuracy of countermeasures is improved by referring to the user's operation history. Some or all of the above-described processing in the security unit may be performed using, for example, AI, or may be performed without using AI. For example, the security unit can input the user's operation history into AI, and the AI ​​can analyze the data to improve the accuracy of countermeasures.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] The monitoring unit can analyze the speed and patterns of user operations to detect abnormal operations. For example, if the speed of a user's operations suddenly changes, the operation is detected as abnormal. It can also detect operation patterns that are not normally performed by the user and report them as abnormal. It can also analyze operations that are only performed during specific time periods and identify abnormal operations. By analyzing the speed and patterns of a user's operations, abnormal operations can be detected early.

[0067] The identification unit can apply different identification algorithms depending on the type and content of the operation. For example, a pattern recognition algorithm can be applied to data entry operations, and a natural language processing algorithm can be applied to report creation operations. It can also apply a clustering algorithm to file management operations. This allows the application of different identification algorithms depending on the type and content of the operation, thereby improving the accuracy of identification.

[0068] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the task. For example, detailed suggestions are made for tasks with high importance, and brief suggestions are made for tasks with low importance. Also, the suggestion unit can make suggestions with an appropriate level of detail for tasks with medium importance. This allows for efficient suggestions by adjusting the level of detail of the suggestion based on the importance of the task.

[0069] The acquisition unit can filter logs based on specific operations or events. For example, it can filter logs for only data entry operations, or for only report creation operations. It can also filter logs for only file management operations. This allows you to obtain only the information you need by filtering logs based on specific operations or events.

[0070] The learning unit can optimize the learning algorithm by referencing past learning data. For example, it can analyze past learning data and select the optimal algorithm. It can also identify and apply an effective algorithm from past learning data. It can also adjust the parameters of the algorithm based on past learning data. This makes it possible to optimize the learning algorithm by referring to past learning data.

[0071] The processing flow of the first embodiment will be briefly explained below.

[0072] Step 1: The monitoring department monitors the operation of employees' PC monitors. For example, it monitors specific operations and identifies which operations are repetitive routine tasks. The monitoring department monitors operations that involve a lot of routine work, such as data entry and standard report creation. It can also monitor the launch of specific applications and operations on specific files. Step 2: The identification unit identifies repetitive routine tasks based on the operations monitored by the monitoring unit. For example, it identifies operations such as entering the same data every day or creating the same report periodically. The identification unit analyzes the operations monitored by the monitoring unit and identifies which operations are repetitive routine tasks. Step 3: The suggestion module makes automation suggestions based on the repetitive and routine tasks identified by the identification module, for example, proposing tools to automate data entry or software to automate report generation. Step 4: The acquisition unit acquires the employee's operation logs. For example, it records what operations the employee performed and which operations were repetitive routine tasks. The acquisition unit collects the employee's operation logs and provides the data for the generation AI to learn. Step 5: The learning unit trains the generating AI based on the logs acquired by the acquisition unit. For example, the generating AI learns how to enter data and how to create reports, and becomes more familiar with those tasks. Step 6: The generation unit trains the generated AI generated by the learning unit so that it becomes familiar with the business. For example, the generation AI learns how to enter data and the procedures for creating reports, and trains it to become familiar with that business. Step 7: The security department implements strict privacy and security measures. For example, they protect employee operation logs and the AI's learning data to ensure privacy and security. They also encrypt data and restrict access to prevent data leaks.

[0073] (Example 2) A business efficiency system according to an embodiment of the present invention monitors the operations of employees' PC monitors, automatically identifies repetitive routine tasks, and proposes automation for them. This business efficiency system monitors the operations of employees' PC monitors, identifies repetitive routine tasks, and proposes automation for them. Furthermore, a generating AI learns from the acquired logs, resulting in a generating AI that is familiar with the tasks. The system supports organizational productivity improvement while maintaining strict privacy and security measures. For example, the business efficiency system includes a monitoring unit that monitors the operations of employees' PC monitors. The monitoring unit monitors specific operations and identifies which operations are repetitive routine tasks. For example, it monitors operations that involve a lot of routine work, such as data entry and standard report creation. Next, an identification unit identifies the repetitive routine tasks. The identification unit analyzes the operations monitored by the monitoring unit and identifies which operations are repetitive routine tasks. For example, it identifies operations that involve entering the same data every day or creating the same report periodically. Furthermore, a suggestion unit proposes automation. The suggestion unit proposes automation based on the repetitive routine tasks identified by the identification unit. For example, it proposes tools for automating data entry and software for automating report creation. Next, the acquisition department acquires logs. The acquisition department collects employee operation logs and provides data for the generation AI to learn. For example, it records what operations employees performed and which operations are repetitive routine tasks. Next, the learning department trains the generation AI. The learning department trains the generation AI based on the logs acquired by the acquisition department, creating a generation AI that is familiar with the work. For example, the generation AI learns data entry methods and report creation procedures, becoming familiar with the work. Finally, the security department implements strict privacy and security measures. The security department protects employee operation logs and the generation AI's learning data to ensure privacy and security. For example, it encrypts data and restricts access to prevent data leaks. This allows the business efficiency system to monitor employees' PC monitors, automatically identify repetitive routine tasks, and suggest automation for them.In addition, the generation AI learns based on the acquired logs, creating a generation AI that is familiar with the business, helping to improve organizational productivity.

[0074] A business efficiency improvement system according to an embodiment includes a monitoring unit, an identification unit, a proposal unit, an acquisition unit, a learning unit, a generation unit, and a security unit. The monitoring unit monitors the operations of employees' PC monitors. For example, the monitoring unit monitors specific operations and identifies which operations are repetitive routine tasks. For example, the monitoring unit monitors operations that involve a lot of routine work, such as data entry and standard report creation. The monitoring unit can also monitor the launch of specific applications and operations on specific files. The identification unit identifies repetitive routine tasks based on the operations monitored by the monitoring unit. For example, the identification unit identifies operations that input the same data every day or operations that periodically create the same report. The identification unit analyzes the operations monitored by the monitoring unit and identifies which operations are repetitive routine tasks. The proposal unit proposes automation based on the repetitive routine tasks identified by the identification unit. For example, the proposal unit proposes tools for automating data entry or software for automating report creation. The proposal unit proposes automation based on the repetitive routine tasks identified by the identification unit. The acquisition unit acquires employee operation logs. The acquisition unit records, for example, what operations the employee performed and which operations are repetitive routine tasks. The acquisition unit collects employee operation logs and provides data for the generation AI to learn. The learning unit causes the generation AI to learn based on the logs acquired by the acquisition unit. For example, the learning unit causes the generation AI to learn data entry methods and report creation procedures so as to become familiar with the work. The generation unit causes the generation AI generated by the learning unit to learn data entry methods and report creation procedures so as to become familiar with the work. For example, the generation unit causes the generation AI to learn data entry methods and report creation procedures so as to become familiar with the work. The security unit implements strict privacy and security measures. For example, the security unit protects employee operation logs and the generation AI's learning data to ensure privacy and security. The security unit encrypts data and restricts access to prevent data leaks. As a result, the business efficiency improvement system according to the embodiment can monitor employee PC monitor operations, automatically identify repetitive routine tasks, and propose automation for them.In addition, the generation AI learns based on the acquired logs, creating a generation AI that is familiar with the business, helping to improve organizational productivity.

[0075] The monitoring unit can monitor specific operations. For example, the monitoring unit monitors the launch of a specific application or operations on a specific file. For example, the monitoring unit records the launch and shutdown times of a specific application. The monitoring unit can also record operations on a specific file. For example, the monitoring unit records operations such as creating, editing, and deleting a specific file. The monitoring unit can also record the usage status of a specific application in detail. For example, the monitoring unit records the usage time and frequency of a specific application. By monitoring specific operations, repetitive routine tasks can be more easily identified. Some or all of the above-described processing in the monitoring unit may be performed using, or without, AI. For example, the monitoring unit can input the launch and shutdown times of a specific application into AI, which can then analyze the data to identify specific operations.

[0076] The identification unit can identify specific routine tasks. For example, the identification unit can identify operations such as entering the same data every day or creating the same report on a regular basis. The identification unit analyzes the operations monitored by the monitoring unit and identifies which operations are repetitive routine tasks. For example, the identification unit can analyze data entry operations to identify operations such as entering the same data every day. The identification unit can also analyze report creation operations to identify operations such as creating the same report on a regular basis. For example, the identification unit can analyze the frequency and content of report creation operations to identify repetitive routine tasks. The identification unit can also analyze the usage of specific applications to identify repetitive routine tasks. For example, the identification unit can analyze the frequency and usage time of specific applications to identify repetitive routine tasks. This identification of specific routine tasks makes it possible to suggest automation. Some or all of the above-described processing in the identification unit may be performed using, or without, AI. For example, the identification unit can input data acquired by the monitoring unit into AI, and the AI ​​can analyze the data to identify specific routine tasks.

[0077] The suggestion unit can make specific automation suggestions. For example, the suggestion unit can suggest tools for automating data entry or software for automating report creation. The suggestion unit can make automation suggestions based on the repetitive routine tasks identified by the identification unit. For example, the suggestion unit can suggest tools for automating data entry. The suggestion unit can also suggest software for automating data entry. For example, the suggestion unit can suggest how to use tools or software for automating data entry. The suggestion unit can also suggest software for automating report creation. For example, the suggestion unit can suggest how to use software for automating report creation. By making specific automation suggestions, business efficiency can be improved. Some or all of the above-mentioned processing in the suggestion unit can be performed using, for example, AI, or can be performed without using AI. For example, the suggestion unit can input data acquired by the identification unit into AI, and the AI ​​can analyze the data and make automation suggestions.

[0078] The acquisition unit can acquire employee operation logs. For example, the acquisition unit records what operations the employee performed and which operations are repetitive routine tasks. The acquisition unit collects employee operation logs and provides data for the generation AI to learn from. For example, the acquisition unit records the employee operation logs with a timestamp. The acquisition unit can also record the employee's operation details in detail. For example, the acquisition unit records the operation details of applications and files used by the employee. The acquisition unit can also filter the employee operation logs to record only specific operations. For example, the acquisition unit can record only data entry operations. The acquisition unit can also record only report creation operations. In this way, acquiring employee operation logs can provide data for the generation AI to learn from. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the employee's operation logs into AI, and the AI ​​can analyze the data to acquire the logs.

[0079] The learning unit can cause the generation AI to learn. For example, the learning unit causes the generation AI to learn based on the logs acquired by the acquisition unit. For example, the learning unit causes the generation AI to learn data entry methods and report creation procedures, thereby becoming more familiar with the business. The learning unit causes the generation AI to learn based on the logs acquired by the acquisition unit. For example, the learning unit causes the generation AI to learn data entry methods and report creation procedures, thereby becoming more familiar with the business. The learning unit provides the generation AI with data and documents related to the business and causes the generation AI to learn based on the data. For example, the learning unit provides the generation AI with data for learning data entry procedures and report creation methods. The learning unit can also provide the generation AI with business process data and cause the generation AI to learn based on the data. For example, the learning unit provides the generation AI with business process data and causes the generation AI to learn based on the data. As a result, the generation AI becomes more familiar with the business, enabling more accurate automation suggestions. Some or all of the above-mentioned processing in the learning unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the learning unit can input the log acquired by the acquisition unit into the generation AI, and the generation AI can analyze the data and learn.

[0080] The generation unit can train the generation AI to become more familiar with the business. For example, the generation unit causes the generation AI to learn data entry methods and report creation procedures, thereby becoming more familiar with the business. The generation unit causes the generation AI generated by the learning unit to learn to become more familiar with the business. For example, the generation unit provides the generation AI with data and documents related to the business and causes it to learn based on the data. For example, the generation unit provides the generation AI with data for learning data entry procedures and report creation methods. The generation unit can also provide the generation AI with business process data and cause it to learn based on the data. For example, the generation unit provides the generation AI with business process data and causes it to learn based on the data. This allows the generation AI to become more familiar with the business, thereby enabling more accurate automation suggestions. Some or all of the above-mentioned processing in the generation unit may be performed using the generation AI, for example, or may be performed without using the generation AI. For example, the generation unit can input data acquired by the learning unit into the generation AI, and the generation AI can analyze the data and learn.

[0081] The security unit can encrypt data or restrict access. The security unit can protect, for example, employee operation logs and the generation AI's learning data to ensure privacy and security. The security unit can encrypt data and restrict access to prevent data leakage. For example, the security unit can use AES encryption or RSA encryption as a data encryption method. The security unit can also perform user authentication and set access permissions as a method of access restriction. For example, the security unit can encrypt employee operation logs and set access permissions to prevent data leakage. The security unit can also encrypt the generation AI's learning data and set access permissions to prevent data leakage. In this way, data encryption and access restrictions can ensure privacy and security. Some or all of the above-mentioned processing in the security unit can be performed using, or without, AI. For example, the security unit can input data encryption and access restrictions into an AI, which can then analyze the data and implement security measures.

[0082] The monitoring unit can estimate the user's emotions and adjust the monitoring frequency based on the estimated user emotions. For example, if the user is feeling stressed, the monitoring unit can set the monitoring frequency low to reduce the burden on the user. The monitoring unit can estimate the user's emotions and adjust the monitoring frequency based on the estimated user emotions. For example, if the user is relaxed, the monitoring unit can set the monitoring frequency high to collect detailed data. Furthermore, if the user is in a hurry, the monitoring unit can set the monitoring frequency to medium to efficiently collect data. This can reduce the burden on the user by adjusting the monitoring frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the monitoring unit can be performed using, for example, an AI, or without an AI. For example, the monitoring unit can input the user's emotion data into an AI, which can analyze the data and adjust the monitoring frequency.

[0083] The monitoring unit can record the usage status of specific applications and software in detail. The monitoring unit, for example, records the start and end times of applications used by the user. The monitoring unit records the usage status of specific applications and software in detail. For example, the monitoring unit records the type and frequency of operations performed by the user on specific software. In addition, if a user uses multiple applications simultaneously, the monitoring unit can also record the frequency of switching between them and the usage time. This allows for detailed recording of the usage status of specific applications and software, thereby improving business efficiency. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the usage status of specific applications and software into AI, which then analyzes the data and records the usage status.

[0084] The monitoring unit can analyze the user's operation speed and pattern to detect abnormal operations. For example, if the user's operation speed changes suddenly, the monitoring unit detects the operation as abnormal. The monitoring unit analyzes the user's operation speed and pattern to detect abnormal operations. For example, the monitoring unit detects an operation pattern that the user does not normally perform and reports it as abnormal. The monitoring unit can also analyze operations that the user performs only during specific time periods and identify abnormal operations. In this way, by analyzing the user's operation speed and pattern, abnormal operations can be detected early. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's operation speed and pattern into AI, and the AI ​​can analyze the data to detect abnormal operations.

[0085] The monitoring unit can estimate the user's emotions and determine the priority of operations to monitor based on the estimated user emotions. For example, if the user is feeling stressed, the monitoring unit prioritizes monitoring of operations with low importance. The monitoring unit can estimate the user's emotions and determine the priority of operations to monitor based on the estimated user emotions. For example, if the user is relaxed, the monitoring unit prioritizes monitoring of operations with high importance. Furthermore, if the user is in a hurry, the monitoring unit can prioritize monitoring of efficient operations. This enables efficient monitoring by determining the priority of operations to monitor based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the monitoring unit can be performed using, for example, an AI, or without an AI. For example, the monitoring unit can input user emotion data into an AI, which analyzes the data and determines the priority of operations to monitor.

[0086] The monitoring unit can select the optimal monitoring method by taking into account the user's device information. For example, if the user is using a desktop PC, the monitoring unit performs full-screen monitoring. The monitoring unit selects the optimal monitoring method by taking into account the user's device information. For example, if the user is using a laptop PC, the monitoring unit selects a monitoring method that reduces battery consumption. Furthermore, if the user is using a tablet, the monitoring unit can also select a monitoring method that supports touch operations. In this way, the optimal monitoring method can be selected by taking into account the user's device information. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's device information into AI, which then analyzes the data and selects the optimal monitoring method.

[0087] The monitoring unit can improve the accuracy of monitoring by referring to the user's past operation history. The monitoring unit, for example, analyzes the user's past operation history and focuses on monitoring frequently performed operations. The monitoring unit improves the accuracy of monitoring by referring to the user's past operation history. For example, the monitoring unit detects abnormal operations early based on the user's past operation patterns. The monitoring unit can also predict operations performed during specific time periods from the user's past operation history and improve the accuracy of monitoring. In this way, the accuracy of monitoring is improved by referring to the user's past operation history. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's past operation history into AI, and the AI ​​can analyze the data to improve the accuracy of monitoring.

[0088] The identification unit can estimate the user's emotions and adjust the identification criteria based on the estimated user emotions. For example, if the user is feeling stressed, the identification unit relaxes the identification criteria to reduce false positives. The identification unit can estimate the user's emotions and adjust the identification criteria based on the estimated user emotions. For example, if the user is relaxed, the identification unit tightens the identification criteria to improve accuracy. Alternatively, if the user is in a hurry, the identification unit can set the identification criteria to a moderate level to perform efficient identification. This can reduce false positives and improve identification accuracy by adjusting the identification criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the identification unit can be performed using, for example, an AI. For example, the identification unit can input user emotion data into an AI, which then analyzes the data and adjusts the identification criteria.

[0089] The identification unit can identify routine tasks by taking into account the frequency of operations and time periods. For example, the identification unit identifies operations that are performed at the same time period every day as routine tasks. The identification unit identifies routine tasks by taking into account the frequency of operations and time periods. For example, the identification unit identifies operations that are performed several times a week as routine tasks. The identification unit can also identify operations that are performed once a month as routine tasks. This makes it possible to accurately identify routine tasks by taking into account the frequency of operations and time periods. Some or all of the above-described processing in the identification unit may be performed using, or without, AI, for example. For example, the identification unit can input data on the frequency of operations and time periods into AI, and the AI ​​can analyze the data to identify routine tasks.

[0090] The identification unit can apply different identification algorithms depending on the type and content of the operation. For example, the identification unit applies a pattern recognition algorithm to a data entry operation. The identification unit applies different identification algorithms depending on the type and content of the operation. For example, the identification unit applies a natural language processing algorithm to a report creation operation. The identification unit can also apply a clustering algorithm to a file management operation. In this way, by applying different identification algorithms depending on the type and content of the operation, the accuracy of identification is improved. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input data on the type and content of the operation into AI, which analyzes the data and applies different identification algorithms.

[0091] The identification unit can estimate the user's emotion and adjust the display method of the identification result based on the estimated user emotion. For example, if the user is feeling stressed, the identification unit provides a simple, highly visible display method. The identification unit can estimate the user's emotion and adjust the display method of the identification result based on the estimated user emotion. For example, if the user is relaxed, the identification unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the identification unit can also provide a display method that focuses on the main points. This improves visibility by adjusting the display method of the identification result according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the identification unit can be performed using, for example, an AI, or without an AI. For example, the identification unit can input user emotion data into an AI, which can analyze the data and adjust the display method of the identification result.

[0092] The identification unit can determine the identification priority based on the user's job content and job title. For example, for a managerial user, the identification unit prioritizes identifying operations with high importance. The identification unit determines the identification priority based on the user's job content and job title. For example, for a general employee user, the identification unit prioritizes identifying everyday operations. The identification unit can also prioritize identifying basic operations for a new employee user. This enables efficient identification by determining the identification priority based on the user's job content and job title. Some or all of the above-described processing in the identification unit may be performed using, or without, AI, for example. For example, the identification unit can input data on the user's job content and job title into AI, and the AI ​​can analyze the data to determine the identification priority.

[0093] The identification unit can improve the accuracy of identification by referring to the user's operation history. For example, the identification unit analyzes the user's past operation history and focuses on identifying frequently performed operations. The identification unit improves the accuracy of identification by referring to the user's operation history. For example, the identification unit detects abnormal operations early based on the user's past operation patterns. The identification unit can also predict operations performed during specific time periods from the user's past operation history and improve the accuracy of identification. In this way, the accuracy of identification is improved by referring to the user's operation history. Some or all of the above-described processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input the user's operation history into AI, and the AI ​​can analyze the data to improve the accuracy of identification.

[0094] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on the estimated user emotions. For example, if the user is feeling stressed, the suggestion unit provides simple, highly visible suggestions. The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on the estimated user emotions. For example, if the user is relaxed, the suggestion unit can provide suggestions that include detailed information. Furthermore, if the user is in a hurry, the suggestion unit can also provide suggestions that focus on the main points. This improves visibility by adjusting the way suggestions are presented based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit can be performed using, for example, an AI, or without an AI. For example, the suggestion unit can input the user's emotion data into an AI, which can analyze the data and adjust the way suggestions are presented.

[0095] The suggestion unit can adjust the level of detail of the proposal based on the importance of the task. For example, the suggestion unit makes a detailed proposal for a task of high importance. The suggestion unit adjusts the level of detail of the proposal based on the importance of the task. For example, the suggestion unit makes a brief proposal for a task of low importance. The suggestion unit can also make a proposal with an appropriate level of detail for a task of medium importance. This enables efficient suggestions by adjusting the level of detail of the proposal based on the importance of the task. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input data on the importance of the task into AI, and the AI ​​can analyze the data and adjust the level of detail of the proposal.

[0096] The suggestion unit can apply different suggestion algorithms depending on the task category. For example, the suggestion unit applies a pattern recognition algorithm to a data entry task. The suggestion unit can apply different suggestion algorithms depending on the task category. For example, the suggestion unit can apply a natural language processing algorithm to a report creation task. The suggestion unit can also apply a clustering algorithm to a file management task. By applying different suggestion algorithms depending on the task category, the accuracy of suggestions is improved. Some or all of the above-mentioned processing in the suggestion unit can be performed using, or without, AI, for example. For example, the suggestion unit can input data of the task category into AI, which can analyze the data and apply different suggestion algorithms.

[0097] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user emotions. For example, if the user is feeling stressed, the suggestion unit can provide short, concise suggestions. The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user emotions. For example, if the user is relaxed, the suggestion unit can provide longer suggestions with detailed explanations. Also, if the user is in a hurry, the suggestion unit can provide quick, concise suggestions. This improves readability by adjusting the length of the suggestions according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit can be performed using, for example, an AI, or without an AI. For example, the suggestion unit can input the user's emotion data into an AI, and the AI ​​can analyze the data and adjust the length of the suggestions.

[0098] The suggestion unit can determine the priority of proposals based on the execution time of the tasks. For example, the suggestion unit prioritizes proposals for urgent tasks. The suggestion unit determines the priority of proposals based on the execution time of the tasks. For example, the suggestion unit makes an early proposal for a task with an approaching deadline. The suggestion unit can also postpone a proposal for a task with a distant deadline. In this way, efficient proposals can be made by determining the priority of proposals based on the execution time of the tasks. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input data on the execution time of tasks into AI, and the AI ​​can analyze the data to determine the priority of proposals.

[0099] The suggestion unit can adjust the order of suggestions based on the relevance of the tasks. For example, the suggestion unit prioritizes suggesting highly relevant tasks. The suggestion unit adjusts the order of suggestions based on the relevance of the tasks. For example, the suggestion unit puts less relevant tasks on hold before suggesting them. Furthermore, when multiple tasks are related, the suggestion unit can optimize the order of the tasks and suggest them. This enables efficient suggestions by adjusting the order of suggestions based on the relevance of the tasks. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input data on task relevance to AI, and the AI ​​can analyze the data and adjust the order of suggestions.

[0100] The acquisition unit can estimate the user's emotions and adjust the timing of log acquisition based on the estimated user emotions. For example, if the user is feeling stressed, the acquisition unit sets the log acquisition frequency low. The acquisition unit can estimate the user's emotions and adjust the timing of log acquisition based on the estimated user emotions. For example, if the user is relaxed, the acquisition unit sets the log acquisition frequency high. Furthermore, if the user is in a hurry, the acquisition unit can also set the log acquisition frequency to medium. This enables efficient log acquisition by adjusting the timing of log acquisition according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the acquisition unit can be performed using an AI, for example, or without an AI. For example, the acquisition unit can input the user's emotion data into an AI, and the AI ​​can analyze the data and adjust the timing of log acquisition.

[0101] The acquisition unit can filter logs based on specific operations or events. For example, the acquisition unit acquires logs by filtering only data entry operations. The acquisition unit filters logs based on specific operations or events. For example, the acquisition unit acquires logs by filtering only report creation operations. The acquisition unit can also acquire logs by filtering only file management operations. In this way, by filtering logs based on specific operations or events, it is possible to acquire only necessary information. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input data on specific operations or events into AI, and the AI ​​can analyze the data and filter the logs.

[0102] The acquisition unit can adjust the level of detail of the log to acquire only necessary information. For example, the acquisition unit records only important operations in detail and records other operations in a simplified manner. The acquisition unit adjusts the level of detail of the log to acquire only necessary information. For example, the acquisition unit records all operations in detail and filters them later. The acquisition unit can also record only information about specific operations in detail and record other operations in a simplified manner. In this way, by adjusting the level of detail of the log, only necessary information can be acquired. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input data on the level of detail of the log into AI, and the AI ​​can analyze the data to acquire only necessary information.

[0103] The acquisition unit can estimate the user's emotions and determine the priority of logs to be acquired based on the estimated user emotions. For example, when the user is feeling stressed, the acquisition unit prioritizes acquiring logs with lower importance. The acquisition unit can estimate the user's emotions and determine the priority of logs to be acquired based on the estimated user emotions. For example, when the user is relaxed, the acquisition unit prioritizes acquiring logs with higher importance. Furthermore, when the user is in a hurry, the acquisition unit can prioritize efficient log acquisition. This enables efficient log acquisition by determining the priority of logs to be acquired according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the acquisition unit can be performed using, for example, an AI, or without an AI. For example, the acquisition unit can input user emotion data into an AI, and the AI ​​can analyze the data to determine the priority of logs to be acquired.

[0104] The acquisition unit can select the optimal log acquisition method by taking into account the user's device information. For example, if the user is using a desktop PC, the acquisition unit acquires a full-screen log. The acquisition unit selects the optimal log acquisition method by taking into account the user's device information. For example, if the user is using a laptop PC, the acquisition unit selects a log acquisition method that reduces battery consumption. Furthermore, if the user is using a tablet, the acquisition unit can also select a log acquisition method that supports touch operations. In this way, the optimal log acquisition method can be selected by taking into account the user's device information. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's device information into AI, and the AI ​​can analyze the data to select the optimal log acquisition method.

[0105] The acquisition unit can improve the accuracy of the log by referring to the user's operation history. For example, the acquisition unit analyzes the user's past operation history and focuses on acquiring logs of frequently performed operations. The acquisition unit improves the accuracy of the log by referring to the user's operation history. For example, the acquisition unit acquires logs of abnormal operations early based on the user's past operation patterns. The acquisition unit can also predict logs of operations performed in specific time periods from the user's past operation history and improve accuracy. In this way, the accuracy of the log is improved by referring to the user's operation history. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's operation history into AI, and the AI ​​can analyze the data to improve the accuracy of the log.

[0106] The learning unit can estimate the user's emotions and select training data based on the estimated user emotions. For example, if the user is feeling stressed, the learning unit prioritizes learning simple data. The learning unit can estimate the user's emotions and select training data based on the estimated user emotions. For example, if the user is relaxed, the learning unit can learn detailed data. Furthermore, if the user is in a hurry, the learning unit can also learn efficient data. This enables efficient learning by selecting training data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the learning unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the learning unit can input user emotion data into the generation AI, which can then analyze the data and select training data.

[0107] The learning unit can optimize the learning algorithm by referring to past learning data. For example, the learning unit analyzes past learning data and selects an optimal algorithm. The learning unit optimizes the learning algorithm by referring to past learning data. For example, the learning unit identifies and applies an effective algorithm from past learning data. The learning unit can also adjust parameters of the algorithm based on past learning data. This makes it possible to optimize the learning algorithm by referring to past learning data. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the learning unit can input past learning data into a generation AI, which then analyzes the data to optimize the learning algorithm.

[0108] The learning unit can construct a learning model specialized for specific business content. For example, the learning unit constructs a learning model specialized for data entry work. The learning unit constructs a learning model specialized for specific business content. For example, the learning unit constructs a learning model specialized for report writing work. The learning unit can also construct a learning model specialized for file management work. This enables efficient learning by constructing a learning model specialized for specific business content. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the learning unit can input data specialized for specific business content into the generation AI, and the generation AI can analyze the data to construct a learning model.

[0109] The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated user emotions. For example, if the user is feeling stressed, the learning unit sets the learning frequency low. The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated user emotions. For example, if the user is relaxed, the learning unit sets the learning frequency high. Also, if the user is in a hurry, the learning unit can set the learning frequency to medium. This enables efficient learning by adjusting the learning frequency according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the learning unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the learning unit can input the user's emotion data into the generation AI, which can then analyze the data and adjust the learning frequency.

[0110] The learning unit can weight the learning data based on the time when the log was acquired. For example, the learning unit weights recently acquired log data more heavily during learning. The learning unit weights the learning data based on the time when the log was acquired. For example, the learning unit weights older log data less heavily during learning. The learning unit can also weight log data acquired within a specific period of time during learning. This enables efficient learning by weighting the learning data based on the time when the log was acquired. Some or all of the above-described processing in the learning unit may be performed using, or without, a generation AI. For example, the learning unit can input data from the time when the log was acquired into the generation AI, and the generation AI can analyze the data and weight the learning data.

[0111] The learning unit can improve the accuracy of learning by referring to the user's operation history. For example, the learning unit analyzes the user's past operation history and focuses on learning frequently performed operations. The learning unit improves the accuracy of learning by referring to the user's operation history. For example, the learning unit early learns abnormal operations based on the user's past operation patterns. The learning unit can also predict operations performed during specific time periods from the user's past operation history and improve the accuracy of learning. In this way, the accuracy of learning is improved by referring to the user's operation history. Some or all of the above-described processing in the learning unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the learning unit can input the user's operation history into the generation AI, and the generation AI can analyze the data to improve the accuracy of learning.

[0112] The generation unit can estimate the user's emotions and adjust the learning content of the generation AI based on the estimated user emotions. For example, if the user is feeling stressed, the generation unit prioritizes learning simple content. The generation unit can estimate the user's emotions and adjust the learning content of the generation AI based on the estimated user emotions. For example, if the user is relaxed, the generation unit can cause the generation AI to learn detailed content. Furthermore, if the user is in a hurry, the generation unit can also cause the generation AI to learn efficient content. This enables efficient learning by adjusting the learning content of the generation AI according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit can input user emotion data into the generation AI, and the generation AI can analyze the data and adjust the learning content of the generation AI.

[0113] The generation unit can construct a generation AI specialized for specific business content. The generation unit, for example, constructs a generation AI specialized for data entry work. The generation unit constructs a generation AI specialized for specific business content. For example, the generation unit constructs a generation AI specialized for report creation work. The generation unit can also construct a generation AI specialized for file management work. This enables efficient learning by constructing a generation AI specialized for specific business content. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input data specialized for specific business content into the generation AI, and the generation AI can analyze the data to construct the generation AI.

[0114] The generation unit can optimize the generation algorithm by referring to the past performance of the generation AI. For example, the generation unit analyzes the past performance of the generation AI and selects the optimal algorithm. The generation unit optimizes the generation algorithm by referring to the past performance of the generation AI. For example, the generation unit identifies and applies an effective algorithm from the past performance of the generation AI. The generation unit can also adjust the parameters of the algorithm based on the past performance of the generation AI. This makes it possible to optimize the generation algorithm by referring to the past performance of the generation AI. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, the generation AI, for example. For example, the generation unit can input the past performance of the generation AI into the generation AI, and the generation AI can analyze the data to optimize the generation algorithm.

[0115] The generation unit can estimate the user's emotions and prioritize the generation AIs based on the estimated user emotions. For example, if the user is feeling stressed, the generation unit prioritizes the generation AIs with a lower importance. The generation unit can estimate the user's emotions and prioritize the generation AIs based on the estimated user emotions. For example, if the user is relaxed, the generation unit prioritizes the generation AIs with a higher importance. Furthermore, if the user is in a hurry, the generation unit can prioritize the generation AIs with an efficient importance. This enables efficient generation by prioritizing the generation AIs according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit can input user emotion data into the generation AI, which then analyzes the data to prioritize the generation AIs.

[0116] The generation unit can customize the learning content of the generation AI based on the user's work content and job title. For example, the generation unit has a managerial user learn important work content. The generation unit customizes the learning content of the generation AI based on the user's work content and job title. For example, the generation unit has a general employee user learn everyday work content. The generation unit can also have a new employee user learn basic work content. This enables efficient learning by customizing the learning content of the generation AI based on the user's work content and job title. Some or all of the above-mentioned processing in the generation unit may be performed using the generation AI, for example, or may be performed without using the generation AI. For example, the generation unit can input data on the user's work content and job title into the generation AI, and the generation AI can analyze the data to customize the learning content of the generation AI.

[0117] The generation unit can improve the accuracy of the generation AI by referring to the user's operation history. For example, the generation unit analyzes the user's past operation history and focuses on learning frequently performed operations. The generation unit improves the accuracy of the generation AI by referring to the user's operation history. For example, the generation unit early learns abnormal operations based on the user's past operation patterns. The generation unit can also predict operations that will be performed during specific time periods from the user's past operation history and improve the accuracy of the generation AI. In this way, the accuracy of the generation AI is improved by referring to the user's operation history. Some or all of the above-mentioned processing in the generation unit may be performed using the generation AI, for example, or may be performed without using the generation AI. For example, the generation unit can input the user's operation history into the generation AI, and the generation AI can analyze the data to improve the accuracy of the generation AI.

[0118] The security unit can estimate the user's emotions and adjust the strength of security measures based on the estimated user emotions. For example, if the user is feeling stressed, the security unit sets the strength of security measures to a low level. The security unit can estimate the user's emotions and adjust the strength of security measures based on the estimated user emotions. For example, if the user is relaxed, the security unit sets the strength of security measures to a high level. The security unit can also set the strength of security measures to a medium level if the user is in a hurry. This enables efficient security measures by adjusting the strength of security measures according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the security unit can be performed using, for example, an AI, or without an AI. For example, the security unit can input the user's emotion data into an AI, which then analyzes the data and adjusts the strength of security measures.

[0119] The security unit can apply different encryption methods to specific data or operations. For example, the security unit applies a strong encryption method to important data. The security unit can apply different encryption methods to specific data or operations. For example, the security unit applies a standard encryption method to general data. The security unit can also apply an appropriate encryption method to specific operations. This enables efficient security measures by applying different encryption methods to specific data or operations. Some or all of the above-mentioned processing in the security unit may be performed using, or without, AI, for example. For example, the security unit can input data for specific data or operations into AI, which then analyzes the data and applies different encryption methods.

[0120] The security department can optimize countermeasures by referring to past security incidents. For example, the security department analyzes past security incidents and selects optimal countermeasures. The security department optimizes countermeasures by referring to past security incidents. For example, the security department identifies and applies effective countermeasures from past security incidents. The security department can also adjust parameters of countermeasures based on past security incidents. This makes it possible to optimize countermeasures by referring to past security incidents. Some or all of the above-mentioned processing in the security department may be performed using, for example, AI, or may be performed without using AI. For example, the security department can input data on past security incidents into AI, which then analyzes the data and optimizes countermeasures.

[0121] The security unit can estimate the user's emotions and prioritize security measures based on the estimated user emotions. For example, if the user is feeling stressed, the security unit prioritizes implementing security measures with lower importance. The security unit can estimate the user's emotions and prioritize security measures based on the estimated user emotions. For example, if the user is relaxed, the security unit prioritizes implementing security measures with higher importance. Furthermore, if the user is in a hurry, the security unit can prioritize implementing efficient security measures. This enables efficient security measures by prioritizing security measures according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the security unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the security unit can input user emotion data into an AI, which then analyzes the data to prioritize security measures.

[0122] The security unit can select optimal countermeasures by taking into account the user's device information. For example, if the user is using a desktop PC, the security unit implements full-screen security countermeasures. The security unit selects optimal countermeasures by taking into account the user's device information. For example, if the user is using a laptop PC, the security unit selects security countermeasures that reduce battery consumption. Furthermore, if the user is using a tablet, the security unit can also select security countermeasures that are compatible with touch operations. This allows optimal security countermeasures to be selected by taking into account the user's device information. Some or all of the above-described processing in the security unit may be performed using, for example, AI, or may be performed without using AI. For example, the security unit can input the user's device information into AI, which then analyzes the data to select optimal countermeasures.

[0123] The security unit can improve the accuracy of countermeasures by referring to the user's operation history. For example, the security unit analyzes the user's past operation history and implements security countermeasures focusing on frequently performed operations. The security unit improves the accuracy of countermeasures by referring to the user's operation history. For example, the security unit implements security countermeasures for abnormal operations early based on the user's past operation patterns. The security unit can also predict security countermeasures for operations performed during specific time periods from the user's past operation history and improve accuracy. In this way, the accuracy of countermeasures is improved by referring to the user's operation history. Some or all of the above-described processing in the security unit may be performed using, for example, AI, or may be performed without using AI. For example, the security unit can input the user's operation history into AI, and the AI ​​can analyze the data to improve the accuracy of countermeasures. === Hard Collateral 1-1 === Each of the multiple elements, including the monitoring unit, identification unit, suggestion unit, acquisition unit, learning unit, generation unit, and security unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the monitoring unit is realized by the control unit 46A of the smart device 14 and operates and monitors employees' PC monitors. The identification unit is realized by the specific processing unit 290 of the data processing device 12 and identifies repetitive routine tasks. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and makes automation suggestions. The acquisition unit is realized by the control unit 46A of the smart device 14 and acquires operation logs. The learning unit is realized by the specific processing unit 290 of the data processing device 12 and trains the generation AI. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates the generation AI. The security unit is realized by the specific processing unit 290 of the data processing device 12 and implements privacy and security measures. === Hard Collateral 1-2 === Each of the multiple elements, including the monitoring unit, identification unit, suggestion unit, acquisition unit, learning unit, generation unit, and security unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the monitoring unit is realized by the control unit 46A of the smart glasses 214 and operates and monitors the employee's PC monitor. The identification unit is realized by the specific processing unit 290 of the data processing device 12 and identifies repetitive routine tasks. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and makes automation suggestions. The acquisition unit is realized by the control unit 46A of the smart glasses 214 and acquires operation logs. The learning unit is realized by the specific processing unit 290 of the data processing device 12 and trains the generation AI. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates the generation AI. The security unit is realized by the specific processing unit 290 of the data processing device 12 and implements privacy and security measures. === Hard Collateral 1-3 === Each of the multiple elements, including the monitoring unit, identification unit, suggestion unit, acquisition unit, learning unit, generation unit, and security unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the monitoring unit is realized by the control unit 46A of the headset-type terminal 314 and monitors the operation of employees' PC monitors. The identification unit is realized by the specific processing unit 290 of the data processing device 12 and identifies repetitive routine tasks. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and makes automation suggestions. The acquisition unit is realized by the control unit 46A of the headset-type terminal 314 and acquires operation logs. The learning unit is realized by the specific processing unit 290 of the data processing device 12 and trains the generation AI. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates the generation AI. The security unit is realized by the specific processing unit 290 of the data processing device 12 and implements privacy and security measures. === Hard Collateral 1-4 === Each of the multiple elements, including the monitoring unit, identification unit, suggestion unit, acquisition unit, learning unit, generation unit, and security unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the monitoring unit is realized by the control unit 46A of the robot 414 and operates and monitors the employee's PC monitor. The identification unit is realized by the specific processing unit 290 of the data processing device 12 and identifies repetitive routine tasks. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and makes automation suggestions. The acquisition unit is realized by the control unit 46A of the robot 414 and acquires operation logs. The learning unit is realized by the specific processing unit 290 of the data processing device 12 and trains the generation AI. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates the generation AI. The security unit is realized by the specific processing unit 290 of the data processing device 12 and implements privacy and security measures.

[0124] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0125] The monitoring unit can analyze the speed and patterns of user operations to detect abnormal operations. For example, if the speed of a user's operations suddenly changes, the operation is detected as abnormal. It can also detect operation patterns that are not normally performed by the user and report them as abnormal. It can also analyze operations that are only performed during specific time periods and identify abnormal operations. By analyzing the speed and patterns of a user's operations, abnormal operations can be detected early.

[0126] The identification unit can apply different identification algorithms depending on the type and content of the operation. For example, a pattern recognition algorithm can be applied to data entry operations, and a natural language processing algorithm can be applied to report creation operations. It can also apply a clustering algorithm to file management operations. This allows the application of different identification algorithms depending on the type and content of the operation, thereby improving the accuracy of identification.

[0127] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the task. For example, detailed suggestions are made for tasks with high importance, and brief suggestions are made for tasks with low importance. Also, the suggestion unit can make suggestions with an appropriate level of detail for tasks with medium importance. This allows for efficient suggestions by adjusting the level of detail of the suggestion based on the importance of the task.

[0128] The acquisition unit can filter logs based on specific operations or events. For example, it can filter logs for only data entry operations, or for only report creation operations. It can also filter logs for only file management operations. This allows you to obtain only the information you need by filtering logs based on specific operations or events.

[0129] The learning unit can optimize the learning algorithm by referencing past learning data. For example, it can analyze past learning data and select the optimal algorithm. It can also identify and apply an effective algorithm from past learning data. It can also adjust the parameters of the algorithm based on past learning data. This makes it possible to optimize the learning algorithm by referring to past learning data.

[0130] The monitoring unit can estimate the user's emotions and adjust the monitoring frequency based on the estimated user's emotions. For example, if the user is feeling stressed, the monitoring frequency can be set low to reduce the burden on the user. Also, if the user is relaxed, the monitoring frequency can be set high to collect detailed data. Furthermore, if the user is in a hurry, the monitoring frequency can be set to a medium level to efficiently collect data. In this way, the burden on the user can be reduced by adjusting the monitoring frequency according to the user's emotions.

[0131] The identification unit can estimate the user's emotions and adjust the identification criteria based on the estimated user emotions. For example, if the user is feeling stressed, the identification criteria can be relaxed to reduce false positives. Alternatively, if the user is relaxed, the identification criteria can be tightened to improve accuracy. Furthermore, if the user is in a hurry, the identification criteria can be set to a moderate level to perform efficient identification. In this way, by adjusting the identification criteria according to the user's emotions, false positives can be reduced and the accuracy of identification can be improved.

[0132] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on the estimated user emotions. For example, if the user is feeling stressed, a simple, highly visible suggestion can be made. If the user is relaxed, a suggestion including detailed information can be made. Furthermore, if the user is in a hurry, a suggestion that focuses on the main points can be made. In this way, visibility can be improved by adjusting the way suggestions are presented according to the user's emotions.

[0133] The acquisition unit can estimate the user's emotions and adjust the timing of log acquisition based on the estimated user emotions. For example, if the user is feeling stressed, the log acquisition frequency can be set low. Also, if the user is relaxed, the log acquisition frequency can be set high. Furthermore, if the user is in a hurry, the log acquisition frequency can be set to medium. This allows for efficient log acquisition by adjusting the log acquisition timing according to the user's emotions.

[0134] The learning unit can estimate the user's emotions and select learning data based on the estimated user emotions. For example, if the user is feeling stressed, simple data can be prioritized for learning. Also, if the user is relaxed, detailed data can be learned. Furthermore, if the user is in a hurry, efficient data can be learned. This allows for efficient learning by selecting learning data according to the user's emotions.

[0135] The processing flow of the second embodiment will be briefly explained below.

[0136] Step 1: The monitoring department monitors the operation of employees' PC monitors. For example, it monitors specific operations and identifies which operations are repetitive routine tasks. The monitoring department monitors operations that involve a lot of routine work, such as data entry and standard report creation. It can also monitor the launch of specific applications and operations on specific files. Step 2: The identification unit identifies repetitive routine tasks based on the operations monitored by the monitoring unit. For example, it identifies operations such as entering the same data every day or creating the same report periodically. The identification unit analyzes the operations monitored by the monitoring unit and identifies which operations are repetitive routine tasks. Step 3: The suggestion module makes automation suggestions based on the repetitive and routine tasks identified by the identification module, for example, proposing tools to automate data entry or software to automate report generation. Step 4: The acquisition unit acquires the employee's operation logs. For example, it records what operations the employee performed and which operations were repetitive routine tasks. The acquisition unit collects the employee's operation logs and provides the data for the generation AI to learn. Step 5: The learning unit trains the generating AI based on the logs acquired by the acquisition unit. For example, the generating AI learns how to enter data and how to create reports, and becomes more familiar with those tasks. Step 6: The generation unit trains the generated AI generated by the learning unit so that it becomes familiar with the business. For example, the generation AI learns how to enter data and the procedures for creating reports, and trains it to become familiar with that business. Step 7: The security department implements strict privacy and security measures. For example, they protect employee operation logs and the AI's learning data to ensure privacy and security. They also encrypt data and restrict access to prevent data leaks.

[0137] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0138] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0139] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0142] 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.

[0143] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0144] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0145] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0147] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0148] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0149] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0150] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0151] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0152] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0153] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0154] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0155] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0158] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0159] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0160] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0161] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0162] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0163] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0164] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0165] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0166] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0167] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0168] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0169] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0170] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0171] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0172] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0173] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0174] 7, a 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.

[0175] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0176] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0177] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0178] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0179] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0180] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0181] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0182] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0183] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0184] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0185] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0186] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0187] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0188] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0189] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0190] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0191] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0192] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0193] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0194] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0195] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0196] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0197] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0198] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0199] 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.

[0200] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0201] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0202] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0203] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0204] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0205] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0206] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0207] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0208] [Explanation of symbols]

[0209] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A monitoring department that operates and monitors employees' PC monitors, an identification unit that identifies repetitive routine tasks based on the operations monitored by the monitoring unit; a suggestion unit that makes automation suggestions based on the repetitive routine tasks identified by the identification unit; an acquisition unit that acquires a log for automation proposed by the proposal unit; a learning unit that causes a generation AI to learn based on the log acquired by the acquisition unit; A generation unit that learns so that the generated AI generated by the learning unit becomes more familiar with the business; and a security department that implements specific privacy and security measures. A system characterized by:

2. The monitoring unit Monitor specific operations The system of claim 1 .

3. The identification unit Identifying specific routine tasks The system of claim 1 .

4. The proposal unit Make specific automation suggestions The system of claim 1 .

5. The acquisition unit Obtain employee operation logs The system of claim 1 .

6. The learning unit Let the generative AI learn The system of claim 1 .

7. The generation unit Generative AI learns to become familiar with the business The system of claim 1 .

8. The security unit Encrypting or restricting access to data The system of claim 1 .

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