system

Abstract

An object of the system according to the embodiment is to efficiently identify a repetitive routine task of an employee and propose automation.SOLUTION: A system according to an embodiment includes an operation monitoring unit, a task identification unit, an automatic proposing unit, a generation AI learning unit, and a privacy protection unit. The operation monitoring unit monitors an operation from a PC monitor of an employee. The task identification unit identifies a repetitive routine task from the operation log acquired by the operation monitoring unit. The automation proposal unit proposes automation for the routine task identified by the task identification unit. A generation AI learning part learns the generation AI on the basis of the operation log, and creates the generation AI detailed in the business. The privacy protection unit protects the operation log and the training data of the generation AI based on strict privacy and security measures.SELECTED DRAWING: Figure 1

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 technologies have had the problem of making it difficult to efficiently identify and automate repetitive routine tasks performed by employees, limiting the extent to which they can improve work efficiency.

[0005] The system according to the embodiment aims to efficiently identify repetitive routine tasks of employees and suggest automation. [Means for solving the problem]

[0006] The system according to the embodiment comprises an operation monitoring unit, a task identification unit, an automation suggestion unit, a generation AI learning unit, and a privacy protection unit. The operation monitoring unit monitors operations performed on employees' PC monitors. The task identification unit identifies repetitive routine tasks from the operation logs acquired by the operation monitoring unit. The automation suggestion unit proposes automation for the routine tasks identified by the task identification unit. The generation AI learning unit trains the generation AI based on the operation logs, creating a generation AI that is knowledgeable about the work. The privacy protection unit protects the operation logs and the generation AI learning data based on strict privacy and security measures. [Effects of the Invention]

[0007] An embodiment of the system can efficiently identify repetitive and routine tasks of employees and suggest automation. [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) SmartOps Insight, an embodiment of the present invention, is a system that monitors operations on employees' PC monitors, automatically identifies repetitive routine tasks, and suggests automating them. This enables SmartOps Insight to improve business efficiency and support organizational productivity.

[0029] SmartOps Insight according to an embodiment includes an operation monitoring unit, a task identification unit, an automation suggestion unit, a generative AI learning unit, and a privacy protection unit. The operation monitoring unit monitors operations performed on employees' PC monitors. For example, it monitors keyboard input, mouse operations, and application usage. The operation monitoring unit also records and saves operation logs. For example, the operation logs are saved as log files for later analysis. The task identification unit identifies repetitive routine tasks from the operation logs acquired by the operation monitoring unit. For example, it identifies data entry tasks performed at the same time every day and file organization tasks performed periodically. The task identification unit also analyzes patterns in the operation logs to identify routine tasks. For example, it identifies routine tasks based on the frequency and timing of specific operations. The automation suggestion unit proposes automation for the routine tasks identified by the task identification unit. For example, it generates a script for automating data entry tasks. The automation suggestion unit also proposes tools for automating file organization tasks. For example, it proposes a tool for automatically moving files to a specific folder. The generative AI learning unit trains the generative AI based on the operation logs, resulting in a generative AI that is knowledgeable about that business. For example, the generative AI analyzes the operation logs to accumulate knowledge about a specific business. The generative AI can also answer questions about that business and provide advice. The privacy protection unit protects the operation logs and the generative AI's learning data based on strict privacy and security measures. For example, the operation logs are encrypted so that only authorized personnel can view them. The generative AI's learning data is also similarly protected to prevent unauthorized access and data leaks. This enables SmartOps Insight to improve business efficiency and organizational productivity. For example, automating repetitive routine tasks allows employees to focus on more creative work. Furthermore, with the support of the generative AI, which is knowledgeable about the business, employees can perform their work quickly and accurately. Furthermore, strict privacy and security measures allow users to use the platform with peace of mind.

[0030] In addition to operation logs, the operation monitoring unit can also monitor voice input and gesture operations, allowing it to identify a wider variety of routine tasks. For example, the operation monitoring unit may introduce a system that monitors voice input and gesture operations in addition to operations performed by employees on their PCs. For example, it may capture voice instructions and hand movements and analyze the data to identify routine tasks. The operation monitoring unit may also use voice recognition technology to analyze voice instructions given by employees and identify repetitive voice commands as routine tasks. For example, it may identify voice instructions given at the same time every day. The operation monitoring unit may also use gesture recognition technology to analyze hand movements made by employees and identify repetitive gesture operations as routine tasks. For example, it may identify hand movements used to open a specific file. This allows it to identify a wider variety of routine tasks by monitoring a wider variety of input methods.

[0031] When analyzing operation logs, the task identification unit also takes into account the context before and after the appearance of a specific operation pattern, allowing for more accurate task identification. For example, when analyzing operation logs, the task identification unit develops an algorithm that takes into account the operations before and after the appearance of a specific operation pattern. For example, it analyzes a series of operations performed before opening a specific file. The task identification unit also performs context analysis of the operation log to identify routine tasks by considering the time period and operation content before and after a specific operation. For example, it identifies operation patterns performed in specific time periods. The task identification unit also analyzes the context of the operation log to build a system that identifies routine tasks based on operation patterns before and after a specific operation. For example, it identifies operations that are always performed before a specific operation. In this way, the accuracy of task identification is improved by taking into account the context of the operation pattern.

[0032] The operation monitoring unit can monitor not only PC monitors but also other devices such as smartphones and tablets to identify routine tasks. For example, the operation monitoring unit builds a system that expands the scope of operation monitoring to include not only PC monitors but also other devices such as smartphones and tablets. For example, it collects smartphone operation logs. The operation monitoring unit also analyzes smartphone and tablet operation logs to develop an algorithm to identify repetitive routine tasks. For example, it identifies regular operations performed on smartphones. The operation monitoring unit also integrates operation logs from other devices and builds a system to identify routine tasks in the same way as PC monitors. For example, it integrates and analyzes operations performed on tablets with operations on PC monitors. This makes it possible to identify more routine tasks by monitoring multiple devices.

[0033] The task identification unit can use task identification algorithms customized for different business departments to make automation suggestions tailored to the characteristics of each department. For example, the task identification unit develops task identification algorithms customized for each business department and builds a system that makes automation suggestions tailored to the characteristics of each department. For example, different algorithms are used for the sales department and the accounting department. The task identification unit also develops task identification algorithms that take into account the characteristics of each department and makes automation suggestions that are optimal for each department. For example, the sales department proposes automation for customer support. The task identification unit also analyzes operation logs from different business departments and develops task identification algorithms tailored to the characteristics of each department. For example, the accounting department proposes automation for data entry tasks. This makes it possible to make automation suggestions tailored to the characteristics of each department, thereby enabling more effective business efficiency improvements.

[0034] The automation suggestion unit can refer to past success stories when proposing automation and propose the optimal automation method. For example, when proposing automation, the automation suggestion unit references past successful automation cases from a database and builds a system that proposes the optimal automation method. For example, it generates a script based on past success stories. The automation suggestion unit also analyzes past successful automation cases and develops an algorithm that proposes the optimal automation method based on that knowledge. For example, it analyzes patterns of success stories and reflects them in the proposal. Also, when proposing automation, the automation suggestion unit references past success stories and builds a system that proposes the optimal automation method for similar tasks. For example, it proposes methods that have been successful for the same type of task. This makes it possible to propose the optimal automation method based on past success stories.

[0035] The automation suggestion unit can collect employee feedback and continuously improve the suggestion algorithm in order to improve the accuracy of the automation suggestions. For example, the automation suggestion unit collects feedback from employees in order to improve the accuracy of the automation suggestions, and builds a system that improves the suggestion algorithm based on that data. For example, the algorithm is adjusted based on the feedback. The automation suggestion unit also collects employee feedback and develops an algorithm to improve the accuracy of the automation suggestions. For example, the feedback data is analyzed and reflected in the suggestions. The automation suggestion unit also collects employee feedback when making automation suggestions, and builds a system that continuously improves the suggestion algorithm based on that data. For example, the content of the suggestions is adjusted based on the feedback. This makes it possible to improve the accuracy of the automation suggestions based on employee feedback.

[0036] When making automation proposals, the automation proposal unit can refer to best practices from different industries and apply successful cases from those industries. For example, when making automation proposals, the automation proposal unit references best practices from different industries from a database and builds a system that applies successful cases from those industries. For example, proposals are made based on successful cases from other industries. The automation proposal unit also analyzes best practices from different industries and develops an algorithm that makes automation proposals based on that knowledge. For example, it analyzes successful patterns from different industries and reflects them in proposals. Furthermore, when making automation proposals, the automation proposal unit references successful cases from different industries and builds a system that proposes optimal automation methods for similar tasks. For example, it makes proposals based on successful cases from other industries. In this way, applying successful cases from other industries enables more effective automation proposals.

[0037] When making automation suggestions, the automation suggestion unit can make customization suggestions based on the employee's skill level and support skill improvement. For example, when making automation suggestions, the automation suggestion unit considers the employee's skill level and builds a system that makes customization suggestions to support skill improvement. For example, it proposes automation methods based on skill level. The automation suggestion unit also analyzes the employee's skill level and develops an algorithm that makes automation suggestions based on that level. For example, it adjusts the content of the suggestion based on skill level. The automation suggestion unit also considers the employee's skill level when making automation suggestions and builds a system that makes customization suggestions to support skill improvement. For example, it proposes training based on skill level. In this way, it is possible to support skill improvement by making suggestions based on the employee's skill level.

[0038] The generative AI learning unit incorporates external data related to the business when the generative AI learns, thereby cultivating a generative AI with deeper knowledge. For example, the generative AI learning unit automatically collects external data related to the business (e.g., industry news and technical documents) when the generative AI learns, and builds a system that learns based on that data. For example, it incorporates the latest industry news. The generative AI learning unit also analyzes external data related to the business and develops an algorithm that the generative AI uses to learn based on that knowledge. For example, it analyzes technical documents to deepen business knowledge. The generative AI learning unit also incorporates external data related to the business when the generative AI learns, and builds a system that cultivates a generative AI with deeper knowledge. For example, it learns based on industry news and technical documents. In this way, by incorporating external data, it is possible to cultivate a generative AI with deeper knowledge.

[0039] The generative AI learning unit can incorporate employee feedback as the generative AI learns, accumulating knowledge relevant to practical work. For example, the generative AI learning unit collects feedback from employees as the generative AI learns, and builds a system that accumulates knowledge relevant to practical work based on that data. For example, it adjusts the learning content based on the feedback. The generative AI learning unit also collects employee feedback and develops an algorithm that incorporates that data as the generative AI learns. For example, it analyzes the feedback data and reflects it in the learning. The generative AI learning unit also incorporates employee feedback as the generative AI learns, and builds a system that accumulates knowledge relevant to practical work. For example, it adjusts the learning content based on the feedback. In this way, by incorporating employee feedback, it is possible to accumulate knowledge relevant to practical work.

[0040] The generative AI learning unit integrates knowledge from different business departments as the generative AI learns, enabling it to develop a generative AI with cross-departmental knowledge. For example, the generative AI learning unit integrates operation logs from different business departments as the generative AI learns, building a system that develops a generative AI with cross-departmental knowledge. For example, it integrates knowledge from the sales department and the accounting department. The generative AI learning unit also analyzes the knowledge of different business departments and develops an algorithm for the generative AI to learn based on that knowledge. For example, it analyzes operation logs for each department and integrates knowledge. The generative AI learning unit also integrates knowledge from different business departments as the generative AI learns, enabling it to develop a system that develops a generative AI with cross-departmental knowledge. For example, it integrates knowledge from the sales department and the accounting department. In this way, by integrating knowledge from different business departments, it is possible to develop a generative AI with cross-departmental knowledge.

[0041] The generative AI learning unit incorporates business knowledge from different languages ​​and cultural spheres as the generative AI learns, enabling it to develop a generative AI with a global perspective. For example, the generative AI learning unit automatically collects business knowledge from different languages ​​and cultural spheres as the generative AI learns, and builds a system that learns based on that data. For example, it incorporates business manuals in different languages. The generative AI learning unit also analyzes business knowledge from different languages ​​and cultural spheres, and develops an algorithm that the generative AI uses to learn based on that knowledge. For example, it analyzes business processes from different cultural spheres and integrates the knowledge. The generative AI learning unit also incorporates business knowledge from different languages ​​and cultural spheres as the generative AI learns, enabling it to develop a system that develops a generative AI with a global perspective. For example, it incorporates business manuals in different languages. In this way, by incorporating business knowledge from different languages ​​and cultural spheres, it is possible to develop a generative AI with a global perspective.

[0042] The privacy protection unit encrypts the access log in addition to the operation log, and is able to strictly manage who accessed the log and when. For example, the privacy protection unit encrypts the access log in addition to the operation log, and builds a system that strictly manages who accessed the log and when. For example, the access log is encrypted and saved. The privacy protection unit also encrypts the access log and develops an algorithm to track who accessed the operation log and when. For example, the access log is analyzed to detect unauthorized access. The privacy protection unit also integrates the encryption of the operation log and the access log, and builds a system that strictly manages who accessed the log and when. For example, the access log is encrypted and saved. In this way, security can be strengthened by encrypting the operation log and the access log.

[0043] The privacy protection unit can introduce a function to limit the storage period of operation logs and automatically delete them after a certain period as a security measure. The privacy protection unit, for example, builds a system that introduces a function to limit the storage period of operation logs and automatically delete them after a certain period as a security measure. For example, a storage period is set and automatic deletion is performed. The privacy protection unit also develops an algorithm that limits the storage period of operation logs and automatically deletes them after a certain period as a security measure. For example, a storage period is set and automatic deletion is performed. The privacy protection unit also builds a system that introduces a function to limit the storage period of operation logs and automatically delete them after a certain period as a security measure. For example, a storage period is set and automatic deletion is performed. In this way, by limiting the storage period of operation logs and automatically deleting them, the risk of data leakage can be reduced.

[0044] The privacy protection department can provide a customization function for adapting privacy and security measures to the regulations and standards of different industries. The privacy protection department, for example, builds a system that provides a customization function for adapting privacy and security measures to the regulations and standards of different industries. For example, it corresponds to regulations for each industry. The privacy protection department also analyzes regulations and standards of different industries and develops algorithms that customize privacy and security measures based on that knowledge. For example, it corresponds to regulations for each industry. The privacy protection department also builds a system that provides a customization function for adapting privacy and security measures to the regulations and standards of different industries. For example, it corresponds to regulations for each industry. This makes it possible to adapt to the regulations and standards of different industries and use the system in a wide range of industries.

[0045] The privacy protection unit can decentralize the storage locations of operation logs as a security measure, thereby reducing the risk of data leakage. For example, the privacy protection unit decentralizes the storage locations of operation logs as a security measure, thereby building a system that reduces the risk of data leakage. For example, the data is stored in a distributed manner on multiple servers. The privacy protection unit also develops an algorithm that decentralizes the storage locations of operation logs and reduces the risk of data leakage. For example, data is stored in different geographical locations. The privacy protection unit also decentralizes the storage locations of operation logs as a security measure, thereby building a system that reduces the risk of data leakage. For example, data is stored in a distributed manner using cloud storage. In this way, the risk of data leakage can be reduced by decentralizing the storage locations of operation logs.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The operation monitoring unit not only monitors operations performed by employees on their PC monitors, but also acquires employees' biometric information and monitors their health. For example, sensors built into the keyboard and mouse can be used to measure heart rate and skin temperature. The operation monitoring unit then analyzes this biometric information to build a system that monitors employees' health in real time. For example, it can analyze heart rate fluctuations to evaluate stress levels. The operation monitoring unit can also make suggestions to optimize employees' work environments based on health data. For example, if stress levels are high, it can send a notification encouraging employees to take a break. This allows for monitoring employees' health and optimizing their work environments, thereby improving work efficiency.

[0048] When analyzing operation logs, the task identification unit can take into account the skill level of employees and identify tasks according to their skills. For example, it can distinguish between the operation patterns of beginners and experts and identify tasks appropriate for each. The task identification unit can also develop task identification algorithms according to skill level and build a system that supports employee skill improvement. For example, it can identify basic operations for beginners and advanced operations for experts. The task identification unit can also identify tasks according to skill level and provide feedback to promote employee skill improvement. For example, it can evaluate the efficiency of operations and suggest areas for improvement. In this way, by identifying tasks according to the skill level of employees, it is possible to support skill improvement and improve work efficiency.

[0049] When proposing automation, the automation suggestion unit can refer to past success stories and propose the optimal automation method. For example, when proposing automation, a system can be built that references past successful automation cases from a database and proposes the optimal automation method. For example, a script can be generated based on past success stories. The automation suggestion unit can also analyze past successful automation cases and develop an algorithm that proposes the optimal automation method based on that knowledge. For example, it can analyze the patterns of successful cases and reflect them in the proposal. The automation suggestion unit can also refer to past success stories and propose the optimal automation method for similar tasks when proposing automation. For example, it can propose methods that have been successful for the same type of task. This makes it possible to propose the optimal automation method based on past success stories.

[0050] The automation suggestion unit can collect employee feedback and continuously improve the suggestion algorithm in order to improve the accuracy of automation suggestions. For example, in order to improve the accuracy of automation suggestions, a system is built that collects feedback from employees and improves the suggestion algorithm based on that data. For example, the algorithm is adjusted based on the feedback. The automation suggestion unit also collects employee feedback and develops an algorithm to improve the accuracy of automation suggestions. For example, the feedback data is analyzed and reflected in the suggestions. The automation suggestion unit also collects employee feedback when making automation suggestions and builds a system that continuously improves the suggestion algorithm based on that data. For example, the content of the suggestions is adjusted based on the feedback. This makes it possible to improve the accuracy of automation suggestions based on employee feedback.

[0051] The generative AI learning unit incorporates external data related to the business as the generative AI learns, enabling it to develop a generative AI with deeper knowledge. For example, when the generative AI learns, it automatically collects external data related to the business (e.g., industry news and technical documents) and builds a system that learns based on that data. For example, it incorporates the latest industry news. The generative AI learning unit also analyzes external data related to the business and develops an algorithm that the generative AI uses to learn based on that knowledge. For example, it analyzes technical documents to deepen business knowledge. The generative AI learning unit also incorporates external data related to the business as the generative AI learns, enabling it to develop a system that develops a generative AI with deeper knowledge. For example, it learns based on industry news and technical documents. In this way, by incorporating external data, it is possible to develop a generative AI with deeper knowledge.

[0052] The generative AI learning department can incorporate employee feedback as the generative AI learns, accumulating knowledge relevant to practical work. For example, as the generative AI learns, it collects feedback from employees and builds a system that accumulates knowledge relevant to practical work based on that data. For example, it adjusts the learning content based on the feedback. The generative AI learning department also collects employee feedback and develops an algorithm that incorporates that data as the generative AI learns. For example, it analyzes the feedback data and reflects it in the learning. The generative AI learning department can also incorporate employee feedback as the generative AI learns, accumulating knowledge relevant to practical work. For example, it adjusts the learning content based on the feedback. In this way, by incorporating employee feedback, it is possible to accumulate knowledge relevant to practical work.

[0053] The Privacy Protection Department encrypts the access logs in addition to encrypting the operation logs, enabling strict management of who accessed the system and when. For example, a system is constructed in which the access logs are encrypted in addition to encrypting the operation logs, and who accessed the system and when. For example, the access logs are encrypted and stored. The Privacy Protection Department also develops an algorithm to encrypt the access logs and track who accessed the operation logs and when. For example, it analyzes the access logs to detect unauthorized access. The Privacy Protection Department also integrates the encryption of the operation logs and access logs, and constructs a system in which who accessed the system and when. For example, the access logs are encrypted and stored. In this way, security can be strengthened by encrypting the operation logs and access logs.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The operation monitoring unit monitors operations performed by employees on their PC monitors. For example, it monitors keyboard input, mouse operation, and application usage, and records and saves the operation log. The operation log is saved as a log file so that it can be analyzed later. Step 2: The task identification unit identifies repetitive routine tasks from the operation logs acquired by the operation monitoring unit. For example, it identifies data entry tasks that are performed at the same time every day or file organization tasks that are performed periodically, and identifies routine tasks by analyzing patterns in the operation logs. Step 3: The automation suggestion unit makes automation suggestions for the routine tasks identified by the task identification unit. For example, it generates scripts to automate data entry tasks and suggests tools to automate file organization tasks. Step 4: The Generative AI Learning Unit trains the Generative AI based on the operation log, creating a Generative AI that is knowledgeable about that task. For example, the Generative AI can analyze the operation log, accumulate knowledge about a specific task, and answer questions about that task or provide advice. Step 5: The Privacy Protection Department will protect the operation logs and the generation AI's training data based on strict privacy and security measures. For example, the operation logs will be encrypted and can only be viewed by authorized personnel, and the generation AI's training data will be similarly protected to prevent unauthorized access and data leaks.

[0056] (Example 2) SmartOps Insight, an embodiment of the present invention, is a system that monitors operations on employees' PC monitors, automatically identifies repetitive routine tasks, and suggests automating them. This enables SmartOps Insight to improve business efficiency and support organizational productivity.

[0057] SmartOps Insight according to an embodiment includes an operation monitoring unit, a task identification unit, an automation suggestion unit, a generative AI learning unit, and a privacy protection unit. The operation monitoring unit monitors operations performed on employees' PC monitors. For example, it monitors keyboard input, mouse operations, and application usage. The operation monitoring unit also records and saves operation logs. For example, the operation logs are saved as log files for later analysis. The task identification unit identifies repetitive routine tasks from the operation logs acquired by the operation monitoring unit. For example, it identifies data entry tasks performed at the same time every day and file organization tasks performed periodically. The task identification unit also analyzes patterns in the operation logs to identify routine tasks. For example, it identifies routine tasks based on the frequency and timing of specific operations. The automation suggestion unit proposes automation for the routine tasks identified by the task identification unit. For example, it generates a script for automating data entry tasks. The automation suggestion unit also proposes tools for automating file organization tasks. For example, it proposes a tool for automatically moving files to a specific folder. The generative AI learning unit trains the generative AI based on the operation logs, resulting in a generative AI that is knowledgeable about that business. For example, the generative AI analyzes the operation logs to accumulate knowledge about a specific business. The generative AI can also answer questions about that business and provide advice. The privacy protection unit protects the operation logs and the generative AI's learning data based on strict privacy and security measures. For example, the operation logs are encrypted so that only authorized personnel can view them. The generative AI's learning data is also similarly protected to prevent unauthorized access and data leaks. This enables SmartOps Insight to improve business efficiency and organizational productivity. For example, automating repetitive routine tasks allows employees to focus on more creative work. Furthermore, with the support of the generative AI, which is knowledgeable about the business, employees can perform their work quickly and accurately. Furthermore, strict privacy and security measures allow users to use the platform with peace of mind.

[0058] In addition to operation logs, the operation monitoring unit can also monitor voice input and gesture operations, allowing it to identify a wider variety of routine tasks. For example, the operation monitoring unit may introduce a system that monitors voice input and gesture operations in addition to operations performed by employees on their PCs. For example, it may capture voice instructions and hand movements and analyze the data to identify routine tasks. The operation monitoring unit may also use voice recognition technology to analyze voice instructions given by employees and identify repetitive voice commands as routine tasks. For example, it may identify voice instructions given at the same time every day. The operation monitoring unit may also use gesture recognition technology to analyze hand movements made by employees and identify repetitive gesture operations as routine tasks. For example, it may identify hand movements used to open a specific file. This allows it to identify a wider variety of routine tasks by monitoring a wider variety of input methods.

[0059] When analyzing operation logs, the task identification unit also takes into account the context before and after the appearance of a specific operation pattern, allowing for more accurate task identification. For example, when analyzing operation logs, the task identification unit develops an algorithm that takes into account the operations before and after the appearance of a specific operation pattern. For example, it analyzes a series of operations performed before opening a specific file. The task identification unit also performs context analysis of the operation log to identify routine tasks by considering the time period and operation content before and after a specific operation. For example, it identifies operation patterns performed in specific time periods. The task identification unit also analyzes the context of the operation log to build a system that identifies routine tasks based on operation patterns before and after a specific operation. For example, it identifies operations that are always performed before a specific operation. In this way, the accuracy of task identification is improved by taking into account the context of the operation pattern.

[0060] The task identification unit can use the emotion estimation function to identify operations that cause stress to employees and make suggestions to prioritize automating those operations. The task identification unit, for example, uses the emotion estimation function to analyze in real time the stress felt by employees during operations and identify operations that cause stress. For example, it analyzes facial expressions and voice tone. The task identification unit also builds a system that identifies operations that cause stress and makes suggestions to prioritize automating those operations. For example, it generates a script for automating operations with a high stress score. The task identification unit also identifies operations that cause stress to employees based on the emotion estimation data and suggests tools for automating those operations. For example, it develops a tool for automating operations that cause stress. In this way, employee stress can be reduced by prioritizing automation of operations that cause stress.

[0061] The operation monitoring unit can monitor not only PC monitors but also other devices such as smartphones and tablets to identify routine tasks. For example, the operation monitoring unit builds a system that expands the scope of operation monitoring to include not only PC monitors but also other devices such as smartphones and tablets. For example, it collects smartphone operation logs. The operation monitoring unit also analyzes smartphone and tablet operation logs to develop an algorithm to identify repetitive routine tasks. For example, it identifies regular operations performed on smartphones. The operation monitoring unit also integrates operation logs from other devices and builds a system to identify routine tasks in the same way as PC monitors. For example, it integrates and analyzes operations performed on tablets with operations on PC monitors. This makes it possible to identify more routine tasks by monitoring multiple devices.

[0062] The task identification unit can use task identification algorithms customized for different business departments to make automation suggestions tailored to the characteristics of each department. For example, the task identification unit develops task identification algorithms customized for each business department and builds a system that makes automation suggestions tailored to the characteristics of each department. For example, different algorithms are used for the sales department and the accounting department. The task identification unit also develops task identification algorithms that take into account the characteristics of each department and makes automation suggestions that are optimal for each department. For example, the sales department proposes automation for customer support. The task identification unit also analyzes operation logs from different business departments and develops task identification algorithms tailored to the characteristics of each department. For example, the accounting department proposes automation for data entry tasks. This makes it possible to make automation suggestions tailored to the characteristics of each department, thereby enabling more effective business efficiency improvements.

[0063] The task identification unit can use the emotion estimation function to identify the operation that gives employees the most satisfaction and generate guidelines that recommend that operation. The task identification unit, for example, uses the emotion estimation function to analyze in real time the satisfaction felt by employees during an operation and identify operations that give high satisfaction. For example, it analyzes facial expressions and voice tone. The task identification unit also builds a system that identifies operations that give high satisfaction and generates guidelines that recommend those operations. For example, it creates guidelines that recommend operations with high satisfaction scores. The task identification unit also identifies operations that give employees the most satisfaction based on the emotion estimation data and generates guidelines for recommending those operations. For example, it develops a tool for recommending operations that give high satisfaction. This improves work satisfaction by recommending operations that give employees satisfaction.

[0064] The automation suggestion unit can refer to past success stories when proposing automation and propose the optimal automation method. For example, when proposing automation, the automation suggestion unit references past successful automation cases from a database and builds a system that proposes the optimal automation method. For example, it generates a script based on past success stories. The automation suggestion unit also analyzes past successful automation cases and develops an algorithm that proposes the optimal automation method based on that knowledge. For example, it analyzes patterns of success stories and reflects them in the proposal. Also, when proposing automation, the automation suggestion unit references past success stories and builds a system that proposes the optimal automation method for similar tasks. For example, it proposes methods that have been successful for the same type of task. This makes it possible to propose the optimal automation method based on past success stories.

[0065] The automation suggestion unit can collect employee feedback and continuously improve the suggestion algorithm in order to improve the accuracy of the automation suggestions. For example, the automation suggestion unit collects feedback from employees in order to improve the accuracy of the automation suggestions, and builds a system that improves the suggestion algorithm based on that data. For example, the algorithm is adjusted based on the feedback. The automation suggestion unit also collects employee feedback and develops an algorithm to improve the accuracy of the automation suggestions. For example, the feedback data is analyzed and reflected in the suggestions. The automation suggestion unit also collects employee feedback when making automation suggestions, and builds a system that continuously improves the suggestion algorithm based on that data. For example, the content of the suggestions is adjusted based on the feedback. This makes it possible to improve the accuracy of the automation suggestions based on employee feedback.

[0066] The automation suggestion unit can use the emotion estimation function to preferentially suggest automation measures that evoke the most positive emotions in employees. The automation suggestion unit, for example, uses the emotion estimation function to analyze the emotions employees have toward automation suggestions in real time, and builds a system that preferentially suggests automation measures that evoke positive emotions. For example, the automation suggestion unit adjusts the suggestions based on the emotion score. The automation suggestion unit also develops an algorithm that identifies automation measures that evoke the most positive emotions based on employees' emotional responses, and preferentially suggests those measures. For example, it proposes measures with a high positive emotion score. The automation suggestion unit also builds a system that identifies automation measures that evoke the most positive emotions in employees based on the emotion estimation data, and preferentially suggests those measures. For example, it proposes measures that elicit positive emotions. This allows for preferential suggestion of automation measures that elicit positive emotions, thereby improving employee satisfaction.

[0067] When making automation proposals, the automation proposal unit can refer to best practices from different industries and apply successful cases from those industries. For example, when making automation proposals, the automation proposal unit references best practices from different industries from a database and builds a system that applies successful cases from those industries. For example, proposals are made based on successful cases from other industries. The automation proposal unit also analyzes best practices from different industries and develops an algorithm that makes automation proposals based on that knowledge. For example, it analyzes successful patterns from different industries and reflects them in proposals. Furthermore, when making automation proposals, the automation proposal unit references successful cases from different industries and builds a system that proposes optimal automation methods for similar tasks. For example, it makes proposals based on successful cases from other industries. In this way, applying successful cases from other industries enables more effective automation proposals.

[0068] When making automation suggestions, the automation suggestion unit can make customization suggestions based on the employee's skill level and support skill improvement. For example, when making automation suggestions, the automation suggestion unit considers the employee's skill level and builds a system that makes customization suggestions to support skill improvement. For example, it proposes automation methods based on skill level. The automation suggestion unit also analyzes the employee's skill level and develops an algorithm that makes automation suggestions based on that level. For example, it adjusts the content of the suggestion based on skill level. The automation suggestion unit also considers the employee's skill level when making automation suggestions and builds a system that makes customization suggestions to support skill improvement. For example, it proposes training based on skill level. In this way, it is possible to support skill improvement by making suggestions based on the employee's skill level.

[0069] The automation suggestion unit can use the emotion estimation function to identify tasks that employees find most stressful and prioritize the automation of those tasks. The automation suggestion unit, for example, uses the emotion estimation function to analyze the stress employees feel during tasks in real time and identify tasks that cause stress. For example, it analyzes facial expressions and voice tone. The automation suggestion unit also identifies tasks that cause stress and builds a system that prioritizes the automation of those tasks. For example, it generates a script for automating tasks with high stress scores. The automation suggestion unit also identifies tasks that employees find most stressful based on the emotion estimation data and proposes a tool for automating those tasks. For example, it develops a tool for automating stressful tasks. In this way, employee stress can be reduced by prioritizing the automation of stressful tasks.

[0070] The generative AI learning unit incorporates external data related to the business when the generative AI learns, thereby cultivating a generative AI with deeper knowledge. For example, the generative AI learning unit automatically collects external data related to the business (e.g., industry news and technical documents) when the generative AI learns, and builds a system that learns based on that data. For example, it incorporates the latest industry news. The generative AI learning unit also analyzes external data related to the business and develops an algorithm that the generative AI uses to learn based on that knowledge. For example, it analyzes technical documents to deepen business knowledge. The generative AI learning unit also incorporates external data related to the business when the generative AI learns, and builds a system that cultivates a generative AI with deeper knowledge. For example, it learns based on industry news and technical documents. In this way, by incorporating external data, it is possible to cultivate a generative AI with deeper knowledge.

[0071] The generative AI learning unit can incorporate employee feedback as the generative AI learns, accumulating knowledge relevant to practical work. For example, the generative AI learning unit collects feedback from employees as the generative AI learns, and builds a system that accumulates knowledge relevant to practical work based on that data. For example, it adjusts the learning content based on the feedback. The generative AI learning unit also collects employee feedback and develops an algorithm that incorporates that data as the generative AI learns. For example, it analyzes the feedback data and reflects it in the learning. The generative AI learning unit also incorporates employee feedback as the generative AI learns, and builds a system that accumulates knowledge relevant to practical work. For example, it adjusts the learning content based on the feedback. In this way, by incorporating employee feedback, it is possible to accumulate knowledge relevant to practical work.

[0072] The generative AI learning unit can use the emotion estimation function to train a generative AI that prioritizes learning business knowledge that evokes the most positive emotions from employees. For example, the generative AI learning unit uses the emotion estimation function to analyze the emotions employees have toward business knowledge in real time, and builds a system to train a generative AI that prioritizes learning business knowledge that evokes positive emotions. For example, the learning content is adjusted based on the emotion score. The generative AI learning unit also identifies business knowledge that evokes the most positive emotions based on employees' emotional reactions, and develops an algorithm to train a generative AI that prioritizes learning that knowledge. For example, it learns knowledge with a high positive emotion score. The generative AI learning unit also identifies business knowledge that evokes the most positive emotions from employees based on the emotion estimation data, and builds a system to train a generative AI that prioritizes learning that knowledge. For example, it learns knowledge that elicits positive emotions. In this way, by prioritizing learning business knowledge that elicits positive emotions, the usefulness of the generative AI is improved.

[0073] The generative AI learning unit integrates knowledge from different business departments as the generative AI learns, enabling it to develop a generative AI with cross-departmental knowledge. For example, the generative AI learning unit integrates operation logs from different business departments as the generative AI learns, building a system that develops a generative AI with cross-departmental knowledge. For example, it integrates knowledge from the sales department and the accounting department. The generative AI learning unit also analyzes the knowledge of different business departments and develops an algorithm for the generative AI to learn based on that knowledge. For example, it analyzes operation logs for each department and integrates knowledge. The generative AI learning unit also integrates knowledge from different business departments as the generative AI learns, enabling it to develop a system that develops a generative AI with cross-departmental knowledge. For example, it integrates knowledge from the sales department and the accounting department. In this way, by integrating knowledge from different business departments, it is possible to develop a generative AI with cross-departmental knowledge.

[0074] The generative AI learning unit incorporates business knowledge from different languages ​​and cultural spheres as the generative AI learns, enabling it to develop a generative AI with a global perspective. For example, the generative AI learning unit automatically collects business knowledge from different languages ​​and cultural spheres as the generative AI learns, and builds a system that learns based on that data. For example, it incorporates business manuals in different languages. The generative AI learning unit also analyzes business knowledge from different languages ​​and cultural spheres, and develops an algorithm that the generative AI uses to learn based on that knowledge. For example, it analyzes business processes from different cultural spheres and integrates the knowledge. The generative AI learning unit also incorporates business knowledge from different languages ​​and cultural spheres as the generative AI learns, enabling it to develop a system that develops a generative AI with a global perspective. For example, it incorporates business manuals in different languages. In this way, by incorporating business knowledge from different languages ​​and cultural spheres, it is possible to develop a generative AI with a global perspective.

[0075] The generative AI learning unit can use the emotion estimation function to identify the work knowledge that employees find most stressful, and train a generative AI that prioritizes learning that work knowledge. The generative AI learning unit, for example, uses the emotion estimation function to analyze in real time the stress that employees feel toward work knowledge and identify the work knowledge that causes stress. For example, it analyzes facial expressions and tone of voice. The generative AI learning unit also identifies work knowledge that causes stress and builds a system that trains a generative AI that prioritizes learning that knowledge. For example, it learns knowledge with a high stress score. The generative AI learning unit also identifies work knowledge that employees find most stressful based on the emotion estimation data, and builds a system that trains a generative AI that prioritizes learning that knowledge. For example, it learns knowledge that causes stress. In this way, by prioritizing learning work knowledge that causes stress, the usefulness of the generative AI is improved.

[0076] The privacy protection unit encrypts the access log in addition to the operation log, and is able to strictly manage who accessed the log and when. For example, the privacy protection unit encrypts the access log in addition to the operation log, and builds a system that strictly manages who accessed the log and when. For example, the access log is encrypted and saved. The privacy protection unit also encrypts the access log and develops an algorithm to track who accessed the operation log and when. For example, the access log is analyzed to detect unauthorized access. The privacy protection unit also integrates the encryption of the operation log and the access log, and builds a system that strictly manages who accessed the log and when. For example, the access log is encrypted and saved. In this way, security can be strengthened by encrypting the operation log and the access log.

[0077] The privacy protection unit can introduce a function to limit the storage period of operation logs and automatically delete them after a certain period as a security measure. The privacy protection unit, for example, builds a system that introduces a function to limit the storage period of operation logs and automatically delete them after a certain period as a security measure. For example, a storage period is set and automatic deletion is performed. The privacy protection unit also develops an algorithm that limits the storage period of operation logs and automatically deletes them after a certain period as a security measure. For example, a storage period is set and automatic deletion is performed. The privacy protection unit also builds a system that introduces a function to limit the storage period of operation logs and automatically delete them after a certain period as a security measure. For example, a storage period is set and automatic deletion is performed. In this way, by limiting the storage period of operation logs and automatically deleting them, the risk of data leakage can be reduced.

[0078] The privacy protection department can use the emotion estimation function to identify security measures that give employees the most sense of security and prioritize their implementation. The privacy protection department, for example, uses the emotion estimation function to analyze in real time the sense of security employees have regarding security measures and identify security measures that give employees the sense of security. For example, it analyzes facial expressions and tone of voice. The privacy protection department also identifies security measures that give employees the sense of security and builds a system to implement those measures on a priority basis. For example, it implements security measures with a high sense of security score. The privacy protection department also identifies security measures that give employees the most sense of security based on the emotion estimation data and builds a system to implement those measures on a priority basis. For example, it implements security measures that elicit a sense of security. In this way, by prioritizing the implementation of security measures that give employees a sense of security, the reliability of security is improved.

[0079] The privacy protection department can provide a customization function for adapting privacy and security measures to the regulations and standards of different industries. The privacy protection department, for example, builds a system that provides a customization function for adapting privacy and security measures to the regulations and standards of different industries. For example, it corresponds to regulations for each industry. The privacy protection department also analyzes regulations and standards of different industries and develops algorithms that customize privacy and security measures based on that knowledge. For example, it corresponds to regulations for each industry. The privacy protection department also builds a system that provides a customization function for adapting privacy and security measures to the regulations and standards of different industries. For example, it corresponds to regulations for each industry. This makes it possible to adapt to the regulations and standards of different industries and use the system in a wide range of industries.

[0080] The privacy protection unit can decentralize the storage locations of operation logs as a security measure, thereby reducing the risk of data leakage. For example, the privacy protection unit decentralizes the storage locations of operation logs as a security measure, thereby building a system that reduces the risk of data leakage. For example, the data is stored in a distributed manner on multiple servers. The privacy protection unit also develops an algorithm that decentralizes the storage locations of operation logs and reduces the risk of data leakage. For example, data is stored in different geographical locations. The privacy protection unit also decentralizes the storage locations of operation logs as a security measure, thereby building a system that reduces the risk of data leakage. For example, data is stored in a distributed manner using cloud storage. In this way, the risk of data leakage can be reduced by decentralizing the storage locations of operation logs.

[0081] The privacy protection department can use the emotion estimation function to identify security risks that make employees feel the most anxious and prioritize countermeasures for those risks. For example, the privacy protection department uses the emotion estimation function to analyze employees' anxiety about security risks in real time and identify risks that make them anxious. For example, facial expressions and tone of voice are analyzed. The privacy protection department also identifies security risks that make employees anxious and builds a system to prioritize countermeasures for those risks. For example, risks with high anxiety scores are prioritized. The privacy protection department also uses the emotion estimation data to identify security risks that make employees feel the most anxious and builds a system to prioritize countermeasures for those risks. For example, risks that elicit anxiety are identified and countermeasures are implemented. In this way, priority is given to countering security risks that make employees anxious, thereby improving the reliability of security.

[0082] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0083] The operation monitoring unit not only monitors operations performed by employees on their PC monitors, but also acquires employees' biometric information and monitors their health. For example, sensors built into the keyboard and mouse can be used to measure heart rate and skin temperature. The operation monitoring unit then analyzes this biometric information to build a system that monitors employees' health in real time. For example, it can analyze heart rate fluctuations to evaluate stress levels. The operation monitoring unit can also make suggestions to optimize employees' work environments based on health data. For example, if stress levels are high, it can send a notification encouraging employees to take a break. This allows for monitoring employees' health and optimizing their work environments, thereby improving work efficiency.

[0084] When analyzing operation logs, the task identification unit can take into account the skill level of employees and identify tasks according to their skills. For example, it can distinguish between the operation patterns of beginners and experts and identify tasks appropriate for each. The task identification unit can also develop task identification algorithms according to skill level and build a system that supports employee skill improvement. For example, it can identify basic operations for beginners and advanced operations for experts. The task identification unit can also identify tasks according to skill level and provide feedback to promote employee skill improvement. For example, it can evaluate the efficiency of operations and suggest areas for improvement. In this way, by identifying tasks according to the skill level of employees, it is possible to support skill improvement and improve work efficiency.

[0085] The task identification unit can use the emotion estimation function to identify the operations that employees find most satisfying and generate guidelines to recommend those operations. For example, the emotion estimation function can be used to analyze in real time the satisfaction that employees feel during operations and identify operations that provide high satisfaction. For example, facial expressions and tone of voice can be analyzed. The task identification unit can also build a system that identifies operations that provide high satisfaction and generates guidelines to recommend those operations. For example, guidelines can be created that recommend operations with high satisfaction scores. The task identification unit can also identify operations that employees find most satisfying based on the emotion estimation data and generate guidelines to recommend those operations. For example, a tool can be developed to recommend operations that provide high satisfaction. This can improve work satisfaction by recommending operations that provide satisfaction to employees.

[0086] When proposing automation, the automation suggestion unit can refer to past success stories and propose the optimal automation method. For example, when proposing automation, a system can be built that references past successful automation cases from a database and proposes the optimal automation method. For example, a script can be generated based on past success stories. The automation suggestion unit can also analyze past successful automation cases and develop an algorithm that proposes the optimal automation method based on that knowledge. For example, it can analyze the patterns of successful cases and reflect them in the proposal. The automation suggestion unit can also refer to past success stories and propose the optimal automation method for similar tasks when proposing automation. For example, it can propose methods that have been successful for the same type of task. This makes it possible to propose the optimal automation method based on past success stories.

[0087] The automation suggestion unit can collect employee feedback and continuously improve the suggestion algorithm in order to improve the accuracy of automation suggestions. For example, in order to improve the accuracy of automation suggestions, a system is built that collects feedback from employees and improves the suggestion algorithm based on that data. For example, the algorithm is adjusted based on the feedback. The automation suggestion unit also collects employee feedback and develops an algorithm to improve the accuracy of automation suggestions. For example, the feedback data is analyzed and reflected in the suggestions. The automation suggestion unit also collects employee feedback when making automation suggestions and builds a system that continuously improves the suggestion algorithm based on that data. For example, the content of the suggestions is adjusted based on the feedback. This makes it possible to improve the accuracy of automation suggestions based on employee feedback.

[0088] The automation suggestion unit can use the emotion estimation function to preferentially suggest automation measures that evoke the most positive emotions among employees. For example, the emotion estimation function can be used to analyze the emotions employees have toward automation suggestions in real time, and a system can be built that preferentially suggests automation measures that evoke positive emotions. For example, the proposals can be adjusted based on the emotion score. The automation suggestion unit can also identify automation measures that evoke the most positive emotions based on employees' emotional responses, and develop an algorithm that preferentially suggests those measures. For example, it can suggest measures with a high positive emotion score. The automation suggestion unit can also identify automation measures that evoke the most positive emotions among employees based on the emotion estimation data, and build a system that preferentially suggests those measures. For example, it can suggest measures that elicit positive emotions. This allows for preferential suggestion of automation measures that elicit positive emotions, thereby improving employee satisfaction.

[0089] The generative AI learning unit incorporates external data related to the business as the generative AI learns, enabling it to develop a generative AI with deeper knowledge. For example, when the generative AI learns, it automatically collects external data related to the business (e.g., industry news and technical documents) and builds a system that learns based on that data. For example, it incorporates the latest industry news. The generative AI learning unit also analyzes external data related to the business and develops an algorithm that the generative AI uses to learn based on that knowledge. For example, it analyzes technical documents to deepen business knowledge. The generative AI learning unit also incorporates external data related to the business as the generative AI learns, enabling it to develop a system that develops a generative AI with deeper knowledge. For example, it learns based on industry news and technical documents. In this way, by incorporating external data, it is possible to develop a generative AI with deeper knowledge.

[0090] The generative AI learning department can incorporate employee feedback as the generative AI learns, accumulating knowledge relevant to practical work. For example, as the generative AI learns, it collects feedback from employees and builds a system that accumulates knowledge relevant to practical work based on that data. For example, it adjusts the learning content based on the feedback. The generative AI learning department also collects employee feedback and develops an algorithm that incorporates that data as the generative AI learns. For example, it analyzes the feedback data and reflects it in the learning. The generative AI learning department can also incorporate employee feedback as the generative AI learns, accumulating knowledge relevant to practical work. For example, it adjusts the learning content based on the feedback. In this way, by incorporating employee feedback, it is possible to accumulate knowledge relevant to practical work.

[0091] The generative AI learning unit can use the emotion estimation function to train a generative AI that prioritizes learning business knowledge that evokes the most positive emotions from employees. For example, the emotion estimation function can be used to analyze the emotions employees have toward business knowledge in real time, and a system can be built to train a generative AI that prioritizes learning business knowledge that evokes positive emotions. For example, the learning content can be adjusted based on the emotion score. The generative AI learning unit can also identify business knowledge that evokes the most positive emotions based on employees' emotional reactions, and develop an algorithm to train a generative AI that prioritizes learning that knowledge. For example, it can learn knowledge with a high positive emotion score. The generative AI learning unit can also identify business knowledge that evokes the most positive emotions from employees based on emotion estimation data, and build a system to train a generative AI that prioritizes learning that knowledge. For example, it can learn knowledge that elicits positive emotions. This improves the usefulness of the generative AI by prioritizing learning business knowledge that elicits positive emotions.

[0092] The Privacy Protection Department encrypts the access logs in addition to encrypting the operation logs, enabling strict management of who accessed the system and when. For example, a system is constructed in which the access logs are encrypted in addition to encrypting the operation logs, and who accessed the system and when. For example, the access logs are encrypted and stored. The Privacy Protection Department also develops an algorithm to encrypt the access logs and track who accessed the operation logs and when. For example, it analyzes the access logs to detect unauthorized access. The Privacy Protection Department also integrates the encryption of the operation logs and access logs, and constructs a system in which who accessed the system and when. For example, the access logs are encrypted and stored. In this way, security can be strengthened by encrypting the operation logs and access logs.

[0093] The processing flow of the second embodiment will be briefly explained below.

[0094] Step 1: The operation monitoring unit monitors operations performed by employees on their PC monitors. For example, it monitors keyboard input, mouse operation, and application usage, and records and saves the operation log. The operation log is saved as a log file so that it can be analyzed later. Step 2: The task identification unit identifies repetitive routine tasks from the operation logs acquired by the operation monitoring unit. For example, it identifies data entry tasks that are performed at the same time every day or file organization tasks that are performed periodically, and identifies routine tasks by analyzing patterns in the operation logs. Step 3: The automation suggestion unit makes automation suggestions for the routine tasks identified by the task identification unit. For example, it generates scripts to automate data entry tasks and suggests tools to automate file organization tasks. Step 4: The Generative AI Learning Unit trains the Generative AI based on the operation log, creating a Generative AI that is knowledgeable about that task. For example, the Generative AI can analyze the operation log, accumulate knowledge about a specific task, and answer questions about that task or provide advice. Step 5: The Privacy Protection Department will protect the operation logs and the generation AI's training data based on strict privacy and security measures. For example, the operation logs will be encrypted and can only be viewed by authorized personnel, and the generation AI's training data will be similarly protected to prevent unauthorized access and data leaks.

[0095] 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.

[0096] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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 a data format such as voice data and text data. 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0097] 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.

[0098] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0099] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0112] 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.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] In the headset type terminal 314, 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. 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 specific processing unit 290 using these models.

[0124] 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.

[0125] 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.

[0126] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0127] 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.

[0128] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0129] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] In the robot 414, 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 robot 414 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.

[0140] 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.

[0141] 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.

[0142] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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."

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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. [Explanation of symbols]

[0162] 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. An operation monitoring department that monitors operations from employees' PC monitors, a task identification unit that identifies repetitive routine tasks from the operation log acquired by the operation monitoring unit; an automation suggestion unit that suggests automation for the routine tasks identified by the task identification unit; A generation AI learning unit that allows the generation AI to learn based on the operation log and create a generation AI that is familiar with the business; It also has a privacy protection unit that protects operation logs and the learning data of the generating AI based on strict privacy and security measures. A system characterized by:

2. The operation monitoring unit In addition to the operation log, voice input and gesture operations are also monitored to identify a wider variety of routine tasks.

2. The system of claim 1.

3. The task identification unit In analyzing the operation log, the context before and after the appearance of a specific operation pattern is also taken into consideration, enabling more accurate task identification.

2. The system of claim 1.

4. The task identification unit Identify the operations that cause stress to employees and make suggestions to prioritize automation of those operations 2. The system of claim 1.

5. The operation monitoring unit Monitor not only your PC monitor but also other devices such as smartphones and tablets to identify routine tasks 2. The system of claim 1.

6. The task identification unit Using task identification algorithms customized for different business departments, we propose automation tailored to the characteristics of each department.

2. The system of claim 1.

7. The task identification unit Identify the operations that employees find most satisfying and generate guidelines that recommend those operations.

2. The system of claim 1.

8. The automation suggestion unit When proposing automation, we refer to past success stories and propose the most suitable automation method.

2. The system of claim 1.

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