system

Abstract

We provide the system. [Solution] A device that records the user's operation history, An information processing device that analyzes recorded operation history and detects repetitive work patterns, An information processing device that generates automation commands for detected repetitive tasks, A means of notifying the user of the generated command and requesting their approval to execute it, A device that automatically executes commands based on user approval, A means for recording the operation history of manufacturing equipment, analyzing repetitive actions, and generating commands for automation, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern desk work, many employees spend time and effort on routine work that occurs repeatedly. Especially for employees without programming knowledge, it is difficult to automate these tasks, which hinders the improvement of work efficiency. In addition, it is difficult to easily grasp the task progress of the entire team, and it is a problem that it is difficult to optimally utilize resources. Furthermore, the lack of reallocation of resources according to the progress situation is an obstacle to the smooth progress of the project.

Means for Solving the Problems

[0005] This invention comprises a terminal that records user operation history and a server that analyzes the recorded operation history and detects recurring work patterns. It also provides a means for generating an automation script for the detected work, notifying the user, and obtaining approval for execution. Subsequently, the script is automatically executed based on the user's approval, thereby improving work efficiency. Furthermore, the server integrates and visualizes the task progress of team members, making it easy to grasp the overall progress. Based on the progress, it includes means for optimally reallocating resources and sending alerts when anomalies are detected, thereby improving the efficiency of project management.

[0006] "User activity history" refers to detailed information about a series of operations performed by a user on a computer terminal and the applications they used.

[0007] A "terminal" is a computer device designed to record user operations and assist in the execution of automation.

[0008] A "server" is a central control unit that receives data transmitted from terminals, analyzes it, and provides information.

[0009] A "repetitive work pattern" is a series of actions or operations that a user frequently performs that are similar to those described above.

[0010] An "automation script" is a set of instructions designed to perform a specific task automatically without user intervention.

[0011] "Notification" refers to a means of communicating information to the user, specifically to inform them of suggestions or results from the system.

[0012] "Task progress data" refers to information about the work status and completion status of each team member.

[0013] "Resource reallocation" in project management refers to the efficient rearrangement of human or material resources as needed.

[0014] "Abnormal detection" is a process of finding problems that deviate from normal work progress or operations.

[0015] "Alert sending" means sending warnings to users or administrators in response to abnormalities or important changes in the situation.

Brief Explanation of Drawings

[0016] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13]It is a sequence diagram showing the processing flow of the data processing system in Example 2 when combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Mode for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of a plurality of types of arithmetic units. Examples of arithmetic units 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), and the like.

[0020] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0029] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0036] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0037] The business efficiency system of the present invention consists of a terminal used by the user and a server that performs data analysis and manages operations in a central location. This system makes it possible to automate repetitive tasks in the user's daily work and improve overall business efficiency.

[0038] First, the terminal records the user's operation history in real time. This operation history includes the applications used, keyboard input history, and mouse operations. Furthermore, by periodically sending this operation history to a server, centralized data management is achieved.

[0039] Next, the server receives and analyzes the operation history data sent from the terminal. Using a generative AI model, it can identify repetitive work patterns frequently performed by the user. This analysis extracts operations that can be automated to improve efficiency.

[0040] Based on these analyses, the server generates an automation script for repetitive tasks. This script is designed to automatically produce the same results as the manual operations the user would normally perform. The server then notifies the user of the generated script and requests their approval to run it, allowing the user to manage the automation.

[0041] Once a user approves an automated script, the terminal automatically executes the script under set times or conditions, reducing the user's workload. As a result, users can focus more on their core tasks, leading to improved overall productivity.

[0042] Furthermore, the server aggregates and analyzes task progress data from each team member, providing a dashboard that visually represents the overall progress. This allows project managers to monitor resource allocation in real time. The server also has the functionality to detect task delays and anomalies in progress as needed, and propose resource reallocation. In addition, when an anomaly is detected, it sends alerts to users and stakeholders to encourage prompt action.

[0043] For example, if a user transfers specific data to a spreadsheet daily, the system generates a script to automate this process and proposes automating the data transfer. If the user approves, the task is then automated by the script, allowing the user to dedicate their time to other important tasks. In this way, the present invention streamlines user work and improves overall business productivity.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] The terminal records user actions in real time and creates operation history data. This data includes the applications used, actions taken within each application, as well as input content and operation time.

[0047] Step 2:

[0048] The terminal sends recorded operation history data to the server at regular intervals. The data is encrypted and properly protected.

[0049] Step 3:

[0050] The server saves the received operation history data to a database and begins analysis. Using a generative AI model, it extracts and identifies recurring work patterns from the data.

[0051] Step 4:

[0052] The server generates an automated script based on the extracted work patterns. This script mimics the work the user was doing under specific conditions and produces equivalent results.

[0053] Step 5:

[0054] The server notifies the user of the generated automation script and its suggestions. The notification includes the script's content and its benefits.

[0055] Step 6:

[0056] The user reviews the proposed automation script and approves or rejects whether to run it. User approval is done through an intuitive user interface.

[0057] Step 7:

[0058] Upon receiving user approval, the device executes an automated script under specified time and conditions. After execution, it reviews the results and sends feedback to the server if necessary.

[0059] Step 8:

[0060] The server integrates task progress data collected from all users and updates a visual dashboard. This makes it possible to monitor the progress of the entire team in real time.

[0061] Step 9:

[0062] The server analyzes progress data and suggests resource reallocation if necessary. It also sends alerts to users and project managers if any anomalies are detected.

[0063] (Example 1)

[0064] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0065] In today's business environment, repetitive tasks that users perform daily often reduce work efficiency. Furthermore, visualizing the overall task progress of a team and appropriately reallocating resources is difficult, which is a common challenge that leads to project delays and wasted resources.

[0066] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0067] In this invention, the server includes means for analyzing the user's operation history using a generated AI model and detecting repetitive work patterns; means for providing a computing device for integrating and visualizing user or team task progress data; and means for improving analytical capabilities using prompt statements and suggesting resource reallocation. This enables the automation of repetitive tasks performed by users and efficient task management for the entire team.

[0068] An "information processing device" is a device that has the function of recording the user's operation history or executing an automated program that has been generated.

[0069] A "processing unit" is a device that analyzes recorded data and has the function of visualizing the task progress of a user or the entire team.

[0070] A "generative AI model" is an artificial intelligence model that analyzes a user's operation history and identifies repetitive work patterns.

[0071] An "automation program" is software code designed to automatically perform detected repetitive tasks.

[0072] A "notification system" is a system for informing the user of the generated automation program and requesting their approval to run it.

[0073] A "prompt message" is an instruction given to a generative AI model, providing information to improve its analytical capabilities.

[0074] A "visualization processing device" is a device that graphically represents the progress of a task and provides information in a format that is easy for users and team members to understand.

[0075] The business efficiency system of the present invention consists of a terminal used by the user and a server that performs central data analysis and business management. By implementing this system, it is possible to automate repetitive tasks in the user's daily work and improve overall business efficiency.

[0076] The terminal records the user's operation history in real time. This operation history includes, for example, usage history of electronic spreadsheet software and word processing applications, as well as keyboard and mouse operation logs. The terminal periodically sends this data to a server, enabling centralized data management.

[0077] The server receives operation history data sent from the terminal and stores it in a database. The server uses a generative AI model to analyze the operation data. Specifically, it uses machine learning libraries such as TENSORFLOW® and PyTorch to identify repetitive work patterns from the data. This allows for the extraction of tasks that can be effectively automated to improve business efficiency.

[0078] Based on the analysis results, the server generates an automation script. This script is written in a programming language such as Python or Bash and automatically mimics operations that a user would normally perform manually. The server notifies the user of the generated script and requests their approval to execute it. This approval process can be carried out through the user interface.

[0079] Once the user approves the execution of a script, the device automatically runs the script based on the specified conditions. This frees users from tedious routine tasks, allowing them to focus their resources on more creative work. As a result, an overall increase in productivity can be expected.

[0080] For example, in the case of a user who performs the task of transferring specific data to a spreadsheet every day, this system records and analyzes that task and suggests an automation script. If the user approves it, the script automates the task, allowing the user to focus on other tasks.

[0081] An example of a prompt for a generating AI model would be, "Identify routine, repetitive tasks performed by the user and generate scripts that can be automated." Through this prompt, the system can analyze repetitive tasks more efficiently and automate them.

[0082] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0083] Step 1:

[0084] The device records the user's activity history. Specifically, it captures keyboard input, mouse clicks, and application usage information in real time. All user actions are considered input. This data is saved locally in JSON or CSV format and prepared for periodic transmission to the server.

[0085] Step 2:

[0086] The terminal sends the recorded operation history data to the server. A secure communication protocol (e.g., HTTPS) is used to ensure data security during transmission. The input is the operation history data saved in step 1, and the output is the data sent to the server. This transmission eliminates the need for data retention on the client side, enabling centralized management.

[0087] Step 3:

[0088] The server receives operation history data sent from the terminal and stores it in the database. The input is operation history data sent from the terminal, and the output is stored in the database in an organized format. The server uses this stored data for subsequent analysis processing.

[0089] Step 4:

[0090] The server analyzes stored operation history data using a generated AI model. The input is the operation history stored in the database, and the output identifies patterns of frequently performed repetitive tasks. By processing the data using the AI ​​model, it analyzes which operations users perform frequently and identifies procedures that can be automated.

[0091] Step 5:

[0092] The server generates automation scripts based on the analysis results. The input is information about repetitive work patterns identified through the analysis process, and the output is automation scripts in the form of Python or Bash scripts. These scripts are coded to mimic specific application operations.

[0093] Step 6:

[0094] The server notifies the user of the generated script and requests their approval to execute it. The input is the information of the generated automation script, and the output is in the form of a notification to the user. The notification is displayed to the user as a pop-up or an in-app message, and they confirm it using an approval button or similar.

[0095] Step 7:

[0096] If the user approves the execution of a script, the terminal automatically executes the script under the specified conditions. The input is the user's approval information, and the output is the result of the automated task. This automates operations that are normally performed manually, reducing labor.

[0097] Step 8:

[0098] The server creates a dashboard to integrate and visualize the task progress of users and teams. Input is progress data collected from multiple devices, and output is visualized data in graph and chart format. The dashboard allows for a quick overview of resource allocation and project progress.

[0099] (Application Example 1)

[0100] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0101] In manufacturing, repetitive operations and inefficient procedures are common, and optimizing these is necessary to improve productivity. Furthermore, a lack of standardization in operations across different work stations can reduce overall efficiency. Solving these problems and automating and optimizing manufacturing processes is essential.

[0102] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0103] In this invention, the server includes means for analyzing the operation history of manufacturing equipment and detecting repetitive actions, means for generating automation commands for the detected actions, and means for facilitating the integration and efficiency of operations at different work stations. This enables automation and efficiency of operations on the manufacturing floor.

[0104] "User operation history" refers to a record of a series of actions and behaviors performed by a user on a device or terminal.

[0105] An "information processing device" is a device that receives, analyzes, stores, and generates commands for data.

[0106] An "automation command" is a series of actions or instructions for automatically performing repetitive operations.

[0107] A "user" is a person or organization that operates a device or terminal.

[0108] "Manufacturing equipment" refers to the machines and robotic systems used in the manufacturing process.

[0109] "Operation history" refers to a series of records of actions taken by users or manufacturing machines, as recorded by the device.

[0110] "Repetitive action" refers to the repeated execution of the same or similar operation multiple times.

[0111] A "work station" is a place or area in the manufacturing process where specific tasks or operations are performed.

[0112] "Integration" refers to the process of combining multiple elements or data into a single system or format.

[0113] "Efficiency improvement" refers to optimizing work and processes to achieve maximum results with fewer resources and less time.

[0114] To implement this invention, it is necessary to appropriately combine and arrange the manufacturing equipment and the information processing equipment. The server uses machine learning libraries such as Python and TensorFlow to receive and analyze operation history data transmitted from the manufacturing equipment. Specifically, the server identifies repetitive actions from the operations and generates automation commands for those actions.

[0115] The generated automation command is notified to the manufacturing equipment via the information processing device, and once the user approves it, the manufacturing equipment performs automation based on the command. The server also integrates operations across work stations, thereby standardizing operations at different stations. Furthermore, the generated automation command can be input into the generated AI model using the prompt message, "Robots in the factory are repeating the same task for 90% of their operating time. Please suggest automating this task."

[0116] As a concrete example, if tool replacement is frequently performed at a particular factory station, the server analyzes the operation history and generates a command to automate the tool replacement. In this way, the burden on workers can be reduced and the overall efficiency of the work can be improved. This invention promotes automation and standardization in factories and contributes to increased productivity.

[0117] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0118] Step 1:

[0119] The server receives operation history data transmitted from the manufacturing equipment. The input is the operation history recorded by the manufacturing equipment, and the server prepares to analyze the data based on this.

[0120] Step 2:

[0121] The server stores the received operation history data in a database, filters the data, and cleanses it. This process removes noise and prepares the data for optimal analysis. The input is the raw data before filtering, and the output is the formatted data for analysis.

[0122] Step 3:

[0123] The server analyzes the formatted data using a generative AI model to identify patterns of repetitive actions. The input here is the formatted data, and the output is the identified patterns of repetitive actions. This step primarily involves pattern recognition by the AI ​​model.

[0124] Step 4:

[0125] The server generates automation commands based on identified repetitive actions. Using patterns generated by a generative AI model, it creates an optimal action sequence as an automated script. The input is pattern information of the repetitive actions, and the output is the automation command.

[0126] Step 5:

[0127] The server notifies the terminal of the generated automation command and requests the user's approval to execute it. The input is the generated automation command, and the output is the notification to the user. The specific operation here is the visualization and notification of the command.

[0128] Step 6:

[0129] The user reviews the automated command from the terminal and approves or rejects it. The input here is the information of the notified automated command, and the output is the result of approval or rejection.

[0130] Step 7:

[0131] Once the user approves, the server sends an automation command to the manufacturing equipment, instructing it to execute. The input is the user's approval result, and the output is the start of the automated work on the manufacturing equipment. This achieves increased efficiency in the production line.

[0132] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0133] This invention provides a system for improving user work efficiency, combining automation based on user operation history with work improvement suggestions that take user emotions into consideration. This system comprises a terminal used by the user, a server for data analysis, and an emotion engine module.

[0134] The device records the user's operation history in real time and saves details of the applications and operations used. This data is encrypted and sent to a server. Furthermore, the device is equipped with an emotion engine that recognizes emotions from the user's facial expressions and voice, and analyzes the user's emotional state.

[0135] The server analyzes the transmitted operation history data to identify repetitive work patterns. Based on this, it generates automation scripts and proposes them to the user. It also integrates with emotion data obtained from the emotion engine to provide flexible script suggestions tailored to the user's emotions. For example, if the user is experiencing high stress levels, it prioritizes suggesting automation to reduce their workload. The suggestions are then approved by the user and executed on the terminal.

[0136] Users receive automation script suggestions from the server and review their contents. Approved scripts are automatically executed on the terminal, reducing the burden of repetitive daily tasks. Furthermore, the emotion engine allows users to understand their emotional state during work, which can be used to improve their workflow.

[0137] For example, if a user's regularly performed data entry task is causing excessive stress, the emotion engine will detect this. Based on this emotional information, the server will prioritize suggesting the automation of the data entry task, helping to reduce the user's workload. The emotion engine will also record the user's emotional changes and analyze long-term trends to make further suggestions for improving work efficiency.

[0138] Thus, the present invention provides integrated support for improving work efficiency based on user actions and emotions, and offers an environment in which users can perform their work more comfortably.

[0139] The following describes the processing flow.

[0140] Step 1:

[0141] The terminal monitors user activity in real time, recording the software used, details of the operations, and input history. This data is encrypted and periodically transmitted to the server.

[0142] Step 2:

[0143] The emotion engine uses the camera and microphone built into the device to analyze the user's facial expressions and voice, and determines their emotional state in real time. This emotional data, along with the operation history, is sent to the server.

[0144] Step 3:

[0145] The server receives operation history and sentiment data sent from the terminal and stores it in a database. The stored data is then analyzed in detail by a generative AI model.

[0146] Step 4:

[0147] The server identifies repetitive work patterns from the analyzed data and generates automation scripts. These scripts are designed to reduce user effort by automating tasks.

[0148] Step 5:

[0149] When the server notifies the user of automated scripts, it takes into account the results of the emotion engine's analysis. If the user is experiencing stress, it prioritizes suggesting automated scripts that contribute to stress reduction.

[0150] Step 6:

[0151] The user reviews the proposed automation script and decides whether to run it. Approved scripts are executed on the terminal according to the schedule.

[0152] Step 7:

[0153] The terminal executes an automated script approved by the user under specified conditions. The results are recorded as logs and sent to the server as feedback.

[0154] Step 8:

[0155] The server visualizes each user's workload and stress level based on collected task progress data and sentiment data. This allows for real-time monitoring of the team's overall progress.

[0156] Step 9:

[0157] The server uses both progress and sentiment data to reallocate resources and suggest improvements to operations. When an anomaly is detected, it sends alerts to users and project managers.

[0158] (Example 2)

[0159] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0160] While conventional business efficiency systems had functions to analyze and automate user operation history, they did not adequately offer flexible business improvement suggestions that took into account the user's emotional state. Therefore, there was a need for overall efficiency improvement, not only through efficiency improvements via automation of operation patterns, but also through reducing the user's mental burden.

[0161] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0162] In this invention, the server includes a device for recording user operation information, an information processing device for analyzing the recorded operation information and detecting recurring operation patterns, and an emotion analysis device for analyzing the user's emotional state and making suggestions for business improvement. This makes it possible not only to improve the efficiency of the user's operation patterns but also to reduce the workload based on the emotional state.

[0163] "User operation information" refers to data related to a series of actions and interactions a user performs while using a device.

[0164] "Device" refers to hardware or equipment used by a user to record operational information or to perform automated procedures.

[0165] An "information processing device" is a computing system that has the function of analyzing input data and detecting repetitive operation patterns.

[0166] An "automation procedure" is a script or program generated based on a repetitive action pattern to streamline or reduce the effort required for a series of operations.

[0167] "Emotional state" refers to the psychological or emotional state of a user, as analyzed from their facial expressions, voice, and other factors.

[0168] An "emotion analysis device" is a module or system that has the function of analyzing a user's emotional state based on their facial expressions and voice.

[0169] The business efficiency system of the present invention consists of a terminal used daily by the user, a server for analyzing data, and an emotion analysis device for performing emotion analysis. Based on this configuration, the system automates the user's operation patterns and provides business improvement suggestions that take into account the user's emotional state.

[0170] A terminal is a computer or mobile device used by a user. The terminal is equipped with software to record user actions in real time, including the duration of the action, the applications used, and specific interaction details. The terminal's emotion engine module uses a camera and microphone to analyze the user's facial expressions and voice to understand their emotional state. This data is securely encrypted and transmitted to a server over the network.

[0171] The server uses machine learning algorithms to analyze operation information received from the terminal. It detects repetitive action patterns and generates automation procedures that can be made more efficient. Furthermore, based on user emotion data provided by an emotion analysis device, it identifies stress points in the work and provides flexible work improvement suggestions tailored to the user's emotional state. For example, automation procedures may include automating routine tasks performed by users under high stress levels.

[0172] Users receive automation suggestions from the server and have a means to review the procedures on their own terminals. After user approval, the suggested procedures are executed on the terminal. Executing these procedures frees users from routine tasks, allowing them to dedicate more time to higher-level work. Furthermore, users can understand their own emotional state through sentiment analysis data and use this information to improve their work environment.

[0173] For example, if a user experiences excessive stress due to their weekly report writing task, the emotion analysis device will detect that stress level. By analyzing this data on the server, it will prioritize suggesting procedures to automate part of the report generation. In this way, the user's mental and physical burden is reduced.

[0174] An example of a prompt using a generative AI model is, "Suggest a way to simplify a specific task performed by the user."

[0175] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0176] Step 1:

[0177] The terminal records user activity in real time. Inputs include keyboard input, mouse clicks, and application launches and shutdowns. This information is temporarily stored as a log and protected by encryption. The output is encrypted activity data, which is then sent to the server.

[0178] Step 2:

[0179] The device uses a camera and microphone to collect the user's facial expressions and voice, and performs emotion analysis. The input is the user's real-time facial expression and voice data. This data is analyzed by an emotion engine module. Data processing includes facial expression feature extraction and voice emotion tone analysis. As output, digital data indicating the user's emotional state is generated and also sent to the server.

[0180] Step 3:

[0181] The server receives operation information from the terminal and stores it in a database. The input consists of encrypted operation information and sentiment data sent from the terminal in the previous step. The server uses machine learning algorithms to analyze recurring behavior patterns from the operation information. Data normalization and clustering are performed as part of the data calculations. The output is a list of automated procedures with recognized patterns.

[0182] Step 4:

[0183] The server takes in emotion analysis data and evaluates the user's stress level, concentration, and other factors. The input is emotional state data from an emotion analysis device. The system performs calculations such as time-series analysis and trend analysis of the emotional data. The output is a business improvement proposal that takes the user's emotional tendencies into account, and generates scripts with priorities aligned with the user's emotional state.

[0184] Step 5:

[0185] The server notifies the user of the generated automation script and requests confirmation. The inputs are the list of automation steps and business improvement suggestions obtained in steps 3 and 4. As output, a message to notify the user is generated and sent to the terminal.

[0186] Step 6:

[0187] The user reviews the notified automation script and approves its execution. The input consists of the automation proposal and detailed message received from the server. The user reviews the content, selects the appropriate script, and approves it. The output is feedback information regarding the approved script.

[0188] Step 7:

[0189] The terminal automatically executes scripts approved by the user. The input is the approved automation script. The terminal starts the instructed steps and executes them sequentially. The output is the result data of the task completed by the execution. This streamlines repetitive daily tasks and reduces the user's burden.

[0190] (Application Example 2)

[0191] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0192] In today's work environment, employees often perform a wide variety of repetitive tasks, leading to significant workload and stress. Furthermore, the impact of employees' emotional states on work efficiency cannot be ignored, yet there are insufficient tools for analyzing and addressing this issue. Therefore, there is a need for a system that analyzes operation history and considers emotional states to achieve efficient automation and improvement of work processes.

[0193] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0194] In this invention, the server includes a device for recording the user's operation history, a processing device for analyzing the recorded operation history and detecting repetitive work patterns, and means for analyzing the user's emotions from their facial expressions and voice and making suggestions for business improvements using that information. This reduces the workload on employees and enables flexible business improvements that respond to their emotional state.

[0195] "Operation history" refers to the history of a series of operations and procedures performed by a user when using a device.

[0196] A "device" is an electronic device used to record or manage user operation history and emotional data.

[0197] A "processing device" is a device that performs data processing to analyze operation history and emotional data and extract necessary information.

[0198] An "automation program" is a script that mechanically performs predefined procedures in order to efficiently carry out repetitive tasks.

[0199] "Means" refer to methods or devices used to achieve a specific function or purpose.

[0200] "Emotional analysis" is the process of analyzing a user's psychological state and emotions based on non-verbal information such as facial expressions and voice.

[0201] A "business improvement suggestion" is a recommendation of improvement measures or methods to facilitate users' efficient work.

[0202] The system of the present invention consists of an operation history recording device, a sensor with emotion analysis capabilities, and a processing device. The device records the user's operation history in real time and generates an efficient automation program based on it. This also includes a sensor that collects data for analyzing the user's emotional state. Specifically, this is a technology that uses cameras and microphones mounted on smart glasses or other wearable devices to analyze the user's facial expressions and voice.

[0203] The server identifies repetitive work patterns using collected operation history and sentiment data. Data analysis utilizes cloud computing technologies and algorithms designed to efficiently identify operation patterns. For example, Amazon Web Services' data analysis tools could be used.

[0204] Based on the analysis results, the server generates and proposes an appropriate automation program to the user. In particular, if the user's emotions indicate stress, automation of tasks will be prioritized. When making a proposal, clear and simple instructions will be provided from a UI / UX perspective to allow the user to quickly approve it.

[0205] For example, if emotional analysis determines that employees at a logistics center are experiencing increased burden and stress from sorting goods, the system can support efficient operations by suggesting an automated sorting program.

[0206] Through this process, the system helps users perform their tasks in a comfortable environment. An example of a prompt for the generated AI model is, "Generate automation suggestions to reduce the burden of repetitive tasks based on the emotional state of employees at the logistics center."

[0207] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0208] Step 1:

[0209] The terminal records user actions in real time and sends that data to the server. As input, it collects detailed data such as the type and timing of the operation and the functions used. This data is encrypted and then securely transferred to the server using communication technology. The output is operation history data formatted in a way that the server can analyze.

[0210] Step 2:

[0211] The server analyzes received operation history data to identify patterns of recurring tasks. The input is operation history data sent from the terminal, and data mining techniques are used to extract patterns of repeatedly occurring operations. The output is a list of identified recurring task patterns. This process efficiently handles large amounts of data using high-performance computing equipment.

[0212] Step 3:

[0213] The device acquires the user's facial expressions and voice data through its camera and microphone, and performs emotion analysis. Input consists of image and voice data, and emotions are analyzed using a machine learning algorithm. Output is data representing the user's emotional state numerically or categorically. This obtained emotion data is immediately transmitted to the server.

[0214] Step 4:

[0215] The server combines repetitive work patterns and sentiment data to generate an optimal automation program for the user. Inputs include repetitive work patterns from operation history and sentiment data from sentiment analysis. A generative AI model is used to automatically generate programs designed to reduce workload. The output is an automation program in an executable format. The generated program is transferred to the terminal in a format easily understood by the user.

[0216] Step 5:

[0217] The user reviews the proposed automation program on the terminal and chooses whether to approve it. The input is the proposed automation program sent from the server and displayed through the UI. The output is a control signal indicating the user's approval or rejection. If the user approves, the process proceeds to the next step.

[0218] Step 6:

[0219] The terminal executes an automated program based on user approval. The input is the approved automated program, and the terminal automatically performs the instructed operations according to its contents. The output is the result of the executed operations, and the user is notified of the completion of the execution.

[0220] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0221] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0222] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0223] [Second Embodiment]

[0224] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0225] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0226] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0227] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0228] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0229] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0230] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0231] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0232] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0233] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0234] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0235] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0236] The business efficiency system of the present invention consists of a terminal used by the user and a server that performs data analysis and manages operations in a central location. This system makes it possible to automate repetitive tasks in the user's daily work and improve overall business efficiency.

[0237] First, the terminal records the user's operation history in real time. This operation history includes the applications used, keyboard input history, and mouse operations. Furthermore, by periodically sending this operation history to a server, centralized data management is achieved.

[0238] Next, the server receives and analyzes the operation history data sent from the terminal. Using a generative AI model, it can identify repetitive work patterns frequently performed by the user. This analysis extracts operations that can be automated to improve efficiency.

[0239] Based on these analyses, the server generates an automation script for repetitive tasks. This script is designed to automatically produce the same results as the manual operations the user would normally perform. The server then notifies the user of the generated script and requests their approval to run it, allowing the user to manage the automation.

[0240] Once a user approves an automated script, the terminal automatically executes the script under set times or conditions, reducing the user's workload. As a result, users can focus more on their core tasks, leading to improved overall productivity.

[0241] Furthermore, the server aggregates and analyzes task progress data from each team member, providing a dashboard that visually represents the overall progress. This allows project managers to monitor resource allocation in real time. The server also has the functionality to detect task delays and anomalies in progress as needed, and propose resource reallocation. In addition, when an anomaly is detected, it sends alerts to users and stakeholders to encourage prompt action.

[0242] For example, if a user transfers specific data to a spreadsheet daily, the system generates a script to automate this process and proposes automating the data transfer. If the user approves, the task is then automated by the script, allowing the user to dedicate their time to other important tasks. In this way, the present invention streamlines user work and improves overall business productivity.

[0243] The following describes the processing flow.

[0244] Step 1:

[0245] The terminal records user actions in real time and creates operation history data. This data includes the applications used, actions taken within each application, as well as input content and operation time.

[0246] Step 2:

[0247] The terminal sends recorded operation history data to the server at regular intervals. The data is encrypted and properly protected.

[0248] Step 3:

[0249] The server saves the received operation history data to a database and begins analysis. Using a generative AI model, it extracts and identifies recurring work patterns from the data.

[0250] Step 4:

[0251] The server generates an automated script based on the extracted work patterns. This script mimics the work the user was doing under specific conditions and produces equivalent results.

[0252] Step 5:

[0253] The server notifies the user of the generated automation script and its suggestions. The notification includes the script's content and its benefits.

[0254] Step 6:

[0255] The user reviews the proposed automation script and approves or rejects whether to run it. User approval is done through an intuitive user interface.

[0256] Step 7:

[0257] Upon receiving user approval, the device executes an automated script under specified time and conditions. After execution, it reviews the results and sends feedback to the server if necessary.

[0258] Step 8:

[0259] The server integrates task progress data collected from all users and updates a visual dashboard. This makes it possible to monitor the progress of the entire team in real time.

[0260] Step 9:

[0261] The server analyzes progress data and suggests resource reallocation if necessary. It also sends alerts to users and project managers if any anomalies are detected.

[0262] (Example 1)

[0263] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0264] In today's business environment, repetitive tasks that users perform daily often reduce work efficiency. Furthermore, visualizing the overall task progress of a team and appropriately reallocating resources is difficult, which is a common challenge that leads to project delays and wasted resources.

[0265] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0266] In this invention, the server includes means for analyzing the user's operation history using a generated AI model and detecting repetitive work patterns; means for providing a computing device for integrating and visualizing user or team task progress data; and means for improving analytical capabilities using prompt statements and suggesting resource reallocation. This enables the automation of repetitive tasks performed by users and efficient task management for the entire team.

[0267] An "information processing device" is a device that has the function of recording the user's operation history or executing an automated program that has been generated.

[0268] A "processing unit" is a device that analyzes recorded data and has the function of visualizing the task progress of a user or the entire team.

[0269] A "generative AI model" is an artificial intelligence model that analyzes a user's operation history and identifies repetitive work patterns.

[0270] An "automation program" is software code designed to automatically perform detected repetitive tasks.

[0271] A "notification system" is a system for informing the user of the generated automation program and requesting their approval to run it.

[0272] A "prompt message" is an instruction given to a generative AI model, providing information to improve its analytical capabilities.

[0273] A "visualization processing device" is a device that graphically represents the progress of a task and provides information in a format that is easy for users and team members to understand.

[0274] The business efficiency system of the present invention consists of a terminal used by the user and a server that performs central data analysis and business management. By implementing this system, it is possible to automate repetitive tasks in the user's daily work and improve overall business efficiency.

[0275] The terminal records the user's operation history in real time. This operation history includes, for example, usage history of electronic spreadsheet software and word processing applications, as well as keyboard and mouse operation logs. The terminal periodically sends this data to a server, enabling centralized data management.

[0276] The server receives operation history data sent from the terminal and stores it in a database. The server uses a generative AI model to analyze the operation data. Specifically, it uses machine learning libraries such as TensorFlow and PyTorch in its software to identify repetitive work patterns from the data. This allows it to extract tasks that can be effectively automated to improve business efficiency.

[0277] Based on the analysis results, the server generates an automation script. This script is written in a programming language such as Python or Bash and automatically mimics operations that a user would normally perform manually. The server notifies the user of the generated script and requests their approval to execute it. This approval process can be carried out through the user interface.

[0278] Once the user approves the execution of a script, the device automatically runs the script based on the specified conditions. This frees users from tedious routine tasks, allowing them to focus their resources on more creative work. As a result, an overall increase in productivity can be expected.

[0279] As a specific example, for a user who performs the task of transferring specific data to a spreadsheet every day, this system records the task, analyzes it, and proposes an automated script. If the user approves, the script automates the task, and the user can focus on other tasks.

[0280] As an example of the prompt text for the generative AI model, there is a form like "Identify daily and repetitive tasks by user operations and generate an automatable script." Through this prompt, the system analyzes repetitive tasks more efficiently and realizes automation.

[0281] The flow of the specific process in Example 1 will be described using FIG. 11.

[0282] Step 1:

[0283] The terminal records the user's operation history. Specifically, it captures in real-time information such as keyboard input, mouse clicks, and the applications used. All operations performed by the user are applicable as inputs. These data are saved locally in JSON format or CSV format and prepared to be sent to the server periodically.

[0284] Step 2:

[0285] The terminal sends the recorded operation history data to the server. When sending, a secure communication protocol (e.g., HTTPS) is used to ensure the security of the data. As an input, there is the operation history data saved in Step 1, and the data is sent as an output to the server. This sending eliminates the need for data retention on the client side and enables centralized unified management.

[0286] Step 3:

[0287] The server receives operation history data sent from the terminal and stores it in the database. The input is operation history data sent from the terminal, and the output is stored in the database in an organized format. The server uses this stored data for subsequent analysis processing.

[0288] Step 4:

[0289] The server analyzes stored operation history data using a generated AI model. The input is the operation history stored in the database, and the output identifies patterns of frequently performed repetitive tasks. By processing the data using the AI ​​model, it analyzes which operations users perform frequently and identifies procedures that can be automated.

[0290] Step 5:

[0291] The server generates automation scripts based on the analysis results. The input is information about repetitive work patterns identified through the analysis process, and the output is automation scripts in the form of Python or Bash scripts. These scripts are coded to mimic specific application operations.

[0292] Step 6:

[0293] The server notifies the user of the generated script and requests their approval to execute it. The input is the information of the generated automation script, and the output is in the form of a notification to the user. The notification is displayed to the user as a pop-up or an in-app message, and they confirm it using an approval button or similar.

[0294] Step 7:

[0295] If the user approves the execution of a script, the terminal automatically executes the script under the specified conditions. The input is the user's approval information, and the output is the result of the automated task. This automates operations that are normally performed manually, reducing labor.

[0296] Step 8:

[0297] The server creates a dashboard to integrate and visualize the task progress of users and teams. Input is progress data collected from multiple devices, and output is visualized data in graph and chart format. The dashboard allows for a quick overview of resource allocation and project progress.

[0298] (Application Example 1)

[0299] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0300] In manufacturing, repetitive operations and inefficient procedures are common, and optimizing these is necessary to improve productivity. Furthermore, a lack of standardization in operations across different work stations can reduce overall efficiency. Solving these problems and automating and optimizing manufacturing processes is essential.

[0301] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0302] In this invention, the server includes means for analyzing the operation history of manufacturing equipment and detecting repetitive actions, means for generating automation commands for the detected actions, and means for facilitating the integration and efficiency of operations at different work stations. This enables automation and efficiency of operations on the manufacturing floor.

[0303] "User operation history" refers to a record of a series of actions and behaviors performed by a user on a device or terminal.

[0304] An "information processing device" is a device that receives, analyzes, stores, and generates commands for data.

[0305] An "Automation Instruction" refers to a series of operation procedures and commands for automatically executing repetitive operations.

[0306] A "User" refers to a person or organization that operates a device or terminal.

[0307] A "Manufacturing Device" refers to a machine or robot system used in the manufacturing process.

[0308] An "Operation History" refers to a series of records of the operations performed by the user or manufacturing machine recorded by the device.

[0309] "Repeated Operation" refers to the repetition of the same or similar operations multiple times.

[0310] A "Workstation" refers to a location or area where specific tasks or operations are performed in the manufacturing process.

[0311] "Integration" refers to combining multiple elements or data into one system or format.

[0312] "Efficiency Improvement" refers to aiming to optimize work or processes to obtain the maximum results with fewer resources and time.

[0313] To implement this invention, it is necessary to appropriately combine and arrange a manufacturing device and an information processing device. The server uses machine learning libraries such as Python and TensorFlow to receive and analyze the operation history data transmitted from the manufacturing device. Specifically, the server identifies repeated operations from the operations and generates automation instructions for those operations.

[0314] The generated automation command is notified to the manufacturing equipment via the information processing device, and once the user approves it, the manufacturing equipment performs automation based on the command. The server also integrates operations across work stations, thereby standardizing operations at different stations. Furthermore, the generated automation command can be input into the generated AI model using the prompt message, "Robots in the factory are repeating the same task for 90% of their operating time. Please suggest automating this task."

[0315] As a concrete example, if tool replacement is frequently performed at a particular factory station, the server analyzes the operation history and generates a command to automate the tool replacement. In this way, the burden on workers can be reduced and the overall efficiency of the work can be improved. This invention promotes automation and standardization in factories and contributes to increased productivity.

[0316] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0317] Step 1:

[0318] The server receives operation history data transmitted from the manufacturing equipment. The input is the operation history recorded by the manufacturing equipment, and the server prepares to analyze the data based on this.

[0319] Step 2:

[0320] The server stores the received operation history data in a database, filters the data, and cleanses it. This process removes noise and prepares the data for optimal analysis. The input is the raw data before filtering, and the output is the formatted data for analysis.

[0321] Step 3:

[0322] The server analyzes the formatted data using a generative AI model to identify patterns of repetitive actions. The input here is the formatted data, and the output is the identified patterns of repetitive actions. This step primarily involves pattern recognition by the AI ​​model.

[0323] Step 4:

[0324] The server generates automation commands based on identified repetitive actions. Using patterns generated by a generative AI model, it creates an optimal action sequence as an automated script. The input is pattern information of the repetitive actions, and the output is the automation command.

[0325] Step 5:

[0326] The server notifies the terminal of the generated automation command and requests the user's approval to execute it. The input is the generated automation command, and the output is the notification to the user. The specific operation here is the visualization and notification of the command.

[0327] Step 6:

[0328] The user reviews the automated command from the terminal and approves or rejects it. The input here is the information of the notified automated command, and the output is the result of approval or rejection.

[0329] Step 7:

[0330] Once the user approves, the server sends an automation command to the manufacturing equipment, instructing it to execute. The input is the user's approval result, and the output is the start of the automated work on the manufacturing equipment. This achieves increased efficiency in the production line.

[0331] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0332] This invention provides a system for improving user work efficiency, combining automation based on user operation history with work improvement suggestions that take user emotions into consideration. This system comprises a terminal used by the user, a server for data analysis, and an emotion engine module.

[0333] The device records the user's operation history in real time and saves details of the applications and operations used. This data is encrypted and sent to a server. Furthermore, the device is equipped with an emotion engine that recognizes emotions from the user's facial expressions and voice, and analyzes the user's emotional state.

[0334] The server analyzes the transmitted operation history data to identify repetitive work patterns. Based on this, it generates automation scripts and proposes them to the user. It also integrates with emotion data obtained from the emotion engine to provide flexible script suggestions tailored to the user's emotions. For example, if the user is experiencing high stress levels, it prioritizes suggesting automation to reduce their workload. The suggestions are then approved by the user and executed on the terminal.

[0335] Users receive automation script suggestions from the server and review their contents. Approved scripts are automatically executed on the terminal, reducing the burden of repetitive daily tasks. Furthermore, the emotion engine allows users to understand their emotional state during work, which can be used to improve their workflow.

[0336] For example, if a user's regularly performed data entry task is causing excessive stress, the emotion engine will detect this. Based on this emotional information, the server will prioritize suggesting the automation of the data entry task, helping to reduce the user's workload. The emotion engine will also record the user's emotional changes and analyze long-term trends to make further suggestions for improving work efficiency.

[0337] Thus, the present invention provides integrated support for improving work efficiency based on user actions and emotions, and offers an environment in which users can perform their work more comfortably.

[0338] The following describes the processing flow.

[0339] Step 1:

[0340] The terminal monitors user activity in real time, recording the software used, details of the operations, and input history. This data is encrypted and periodically transmitted to the server.

[0341] Step 2:

[0342] The emotion engine uses the camera and microphone built into the device to analyze the user's facial expressions and voice, and determines their emotional state in real time. This emotional data, along with the operation history, is sent to the server.

[0343] Step 3:

[0344] The server receives operation history and sentiment data sent from the terminal and stores it in a database. The stored data is then analyzed in detail by a generative AI model.

[0345] Step 4:

[0346] The server identifies repetitive work patterns from the analyzed data and generates automation scripts. These scripts are designed to reduce user effort by automating tasks.

[0347] Step 5:

[0348] When the server notifies the user of automated scripts, it takes into account the results of the emotion engine's analysis. If the user is experiencing stress, it prioritizes suggesting automated scripts that contribute to stress reduction.

[0349] Step 6:

[0350] The user reviews the proposed automation script and decides whether to run it. Approved scripts are executed on the terminal according to the schedule.

[0351] Step 7:

[0352] The terminal executes an automated script approved by the user under specified conditions. The results are recorded as logs and sent to the server as feedback.

[0353] Step 8:

[0354] The server visualizes each user's workload and stress level based on collected task progress data and sentiment data. This allows for real-time monitoring of the team's overall progress.

[0355] Step 9:

[0356] The server uses both progress and sentiment data to reallocate resources and suggest improvements to operations. When an anomaly is detected, it sends alerts to users and project managers.

[0357] (Example 2)

[0358] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0359] While conventional business efficiency systems had functions to analyze and automate user operation history, they did not adequately offer flexible business improvement suggestions that took into account the user's emotional state. Therefore, there was a need for overall efficiency improvement, not only through efficiency improvements via automation of operation patterns, but also through reducing the user's mental burden.

[0360] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0361] In this invention, the server includes a device for recording user operation information, an information processing device for analyzing the recorded operation information and detecting recurring operation patterns, and an emotion analysis device for analyzing the user's emotional state and making suggestions for business improvement. This makes it possible not only to improve the efficiency of the user's operation patterns but also to reduce the workload based on the emotional state.

[0362] "User operation information" refers to data related to a series of actions and interactions a user performs while using a device.

[0363] "Device" refers to hardware or equipment used by a user to record operational information or to perform automated procedures.

[0364] An "information processing device" is a computing system that has the function of analyzing input data and detecting repetitive operation patterns.

[0365] An "automation procedure" is a script or program generated based on a repetitive action pattern to streamline or reduce the effort required for a series of operations.

[0366] "Emotional state" refers to the psychological or emotional state of a user, as analyzed from their facial expressions, voice, and other factors.

[0367] An "emotion analysis device" is a module or system that has the function of analyzing a user's emotional state based on their facial expressions and voice.

[0368] The business efficiency system of the present invention consists of a terminal used daily by the user, a server for analyzing data, and an emotion analysis device for performing emotion analysis. Based on this configuration, the system automates the user's operation patterns and provides business improvement suggestions that take into account the user's emotional state.

[0369] A terminal is a computer or mobile device used by a user. The terminal is equipped with software to record user actions in real time, including the duration of the action, the applications used, and specific interaction details. The terminal's emotion engine module uses a camera and microphone to analyze the user's facial expressions and voice to understand their emotional state. This data is securely encrypted and transmitted to a server over the network.

[0370] The server uses machine learning algorithms to analyze operation information received from the terminal. It detects repetitive action patterns and generates automation procedures that can be made more efficient. Furthermore, based on user emotion data provided by an emotion analysis device, it identifies stress points in the work and provides flexible work improvement suggestions tailored to the user's emotional state. For example, automation procedures may include automating routine tasks performed by users under high stress levels.

[0371] Users receive automation suggestions from the server and have a means to review the procedures on their own terminals. After user approval, the suggested procedures are executed on the terminal. Executing these procedures frees users from routine tasks, allowing them to dedicate more time to higher-level work. Furthermore, users can understand their own emotional state through sentiment analysis data and use this information to improve their work environment.

[0372] For example, if a user experiences excessive stress due to their weekly report writing task, the emotion analysis device will detect that stress level. By analyzing this data on the server, it will prioritize suggesting procedures to automate part of the report generation. In this way, the user's mental and physical burden is reduced.

[0373] An example of a prompt using a generative AI model is, "Suggest a way to simplify a specific task performed by the user."

[0374] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0375] Step 1:

[0376] The terminal records user activity in real time. Inputs include keyboard input, mouse clicks, and application launches and shutdowns. This information is temporarily stored as a log and protected by encryption. The output is encrypted activity data, which is then sent to the server.

[0377] Step 2:

[0378] The device uses a camera and microphone to collect the user's facial expressions and voice, and performs emotion analysis. The input is the user's real-time facial expression and voice data. This data is analyzed by an emotion engine module. Data processing includes facial expression feature extraction and voice emotion tone analysis. As output, digital data indicating the user's emotional state is generated and also sent to the server.

[0379] Step 3:

[0380] The server receives operation information from the terminal and stores it in a database. The input consists of encrypted operation information and sentiment data sent from the terminal in the previous step. The server uses machine learning algorithms to analyze recurring behavior patterns from the operation information. Data normalization and clustering are performed as part of the data calculations. The output is a list of automated procedures with recognized patterns.

[0381] Step 4:

[0382] The server takes in emotion analysis data and evaluates the user's stress level, concentration, and other factors. The input is emotional state data from an emotion analysis device. The system performs calculations such as time-series analysis and trend analysis of the emotional data. The output is a business improvement proposal that takes the user's emotional tendencies into account, and generates scripts with priorities aligned with the user's emotional state.

[0383] Step 5:

[0384] The server notifies the user of the generated automation script and requests confirmation. The inputs are the list of automation steps and business improvement suggestions obtained in steps 3 and 4. As output, a message to notify the user is generated and sent to the terminal.

[0385] Step 6:

[0386] The user reviews the notified automation script and approves its execution. The input consists of the automation proposal and detailed message received from the server. The user reviews the content, selects the appropriate script, and approves it. The output is feedback information regarding the approved script.

[0387] Step 7:

[0388] The terminal automatically executes scripts approved by the user. The input is the approved automation script. The terminal starts the instructed steps and executes them sequentially. The output is the result data of the task completed by the execution. This streamlines repetitive daily tasks and reduces the user's burden.

[0389] (Application Example 2)

[0390] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0391] In today's work environment, employees often perform a wide variety of repetitive tasks, leading to significant workload and stress. Furthermore, the impact of employees' emotional states on work efficiency cannot be ignored, yet there are insufficient tools for analyzing and addressing this issue. Therefore, there is a need for a system that analyzes operation history and considers emotional states to achieve efficient automation and improvement of work processes.

[0392] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0393] In this invention, the server includes a device for recording the user's operation history, a processing device for analyzing the recorded operation history and detecting repetitive work patterns, and means for analyzing the user's emotions from their facial expressions and voice and making suggestions for business improvements using that information. This reduces the workload on employees and enables flexible business improvements that respond to their emotional state.

[0394] "Operation history" refers to the history of a series of operations and procedures performed by a user when using a device.

[0395] A "device" is an electronic device used to record or manage user operation history and emotional data.

[0396] A "processing device" is a device that performs data processing to analyze operation history and emotional data and extract necessary information.

[0397] An "automation program" is a script that mechanically performs predefined procedures in order to efficiently carry out repetitive tasks.

[0398] "Means" refer to methods or devices used to achieve a specific function or purpose.

[0399] "Emotional analysis" is the process of analyzing a user's psychological state and emotions based on non-verbal information such as facial expressions and voice.

[0400] A "business improvement suggestion" is a recommendation of improvement measures or methods to facilitate users' efficient work.

[0401] The system of the present invention consists of an operation history recording device, a sensor with emotion analysis capabilities, and a processing device. The device records the user's operation history in real time and generates an efficient automation program based on it. This also includes a sensor that collects data for analyzing the user's emotional state. Specifically, this is a technology that uses cameras and microphones mounted on smart glasses or other wearable devices to analyze the user's facial expressions and voice.

[0402] The server identifies repetitive work patterns using collected operation history and sentiment data. Data analysis utilizes cloud computing technologies and algorithms designed to efficiently identify operation patterns. For example, Amazon Web Services' data analysis tools could be used.

[0403] Based on the analysis results, the server generates and proposes an appropriate automation program to the user. In particular, if the user's emotions indicate stress, automation of tasks will be prioritized. When making a proposal, clear and simple instructions will be provided from a UI / UX perspective to allow the user to quickly approve it.

[0404] For example, if emotional analysis determines that employees at a logistics center are experiencing increased burden and stress from sorting goods, the system can support efficient operations by suggesting an automated sorting program.

[0405] Through this process, the system helps users perform their tasks in a comfortable environment. An example of a prompt for the generated AI model is, "Generate automation suggestions to reduce the burden of repetitive tasks based on the emotional state of employees at the logistics center."

[0406] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0407] Step 1:

[0408] The terminal records user actions in real time and sends that data to the server. As input, it collects detailed data such as the type and timing of the operation and the functions used. This data is encrypted and then securely transferred to the server using communication technology. The output is operation history data formatted in a way that the server can analyze.

[0409] Step 2:

[0410] The server analyzes received operation history data to identify patterns of recurring tasks. The input is operation history data sent from the terminal, and data mining techniques are used to extract patterns of repeatedly occurring operations. The output is a list of identified recurring task patterns. This process efficiently handles large amounts of data using high-performance computing equipment.

[0411] Step 3:

[0412] The device acquires the user's facial expressions and voice data through its camera and microphone, and performs emotion analysis. Input consists of image and voice data, and emotions are analyzed using a machine learning algorithm. Output is data representing the user's emotional state numerically or categorically. This obtained emotion data is immediately transmitted to the server.

[0413] Step 4:

[0414] The server combines repetitive work patterns and sentiment data to generate an optimal automation program for the user. Inputs include repetitive work patterns from operation history and sentiment data from sentiment analysis. A generative AI model is used to automatically generate programs designed to reduce workload. The output is an automation program in an executable format. The generated program is transferred to the terminal in a format easily understood by the user.

[0415] Step 5:

[0416] The user reviews the proposed automation program on the terminal and chooses whether to approve it. The input is the proposed automation program sent from the server and displayed through the UI. The output is a control signal indicating the user's approval or rejection. If the user approves, the process proceeds to the next step.

[0417] Step 6:

[0418] The terminal executes an automated program based on user approval. The input is the approved automated program, and the terminal automatically performs the instructed operations according to its contents. The output is the result of the executed operations, and the user is notified of the completion of the execution.

[0419] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0420] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0421] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0422] [Third Embodiment]

[0423] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0424] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0425] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0426] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0427] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0428] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0429] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0430] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0431] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0432] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0433] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0434] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0435] The business efficiency system of the present invention consists of a terminal used by the user and a server that performs data analysis and manages operations in a central location. This system makes it possible to automate repetitive tasks in the user's daily work and improve overall business efficiency.

[0436] First, the terminal records the user's operation history in real time. This operation history includes the applications used, keyboard input history, and mouse operations. Furthermore, by periodically sending this operation history to a server, centralized data management is achieved.

[0437] Next, the server receives and analyzes the operation history data sent from the terminal. Using a generative AI model, it can identify repetitive work patterns frequently performed by the user. This analysis extracts operations that can be automated to improve efficiency.

[0438] Based on these analyses, the server generates an automation script for repetitive tasks. This script is designed to automatically produce the same results as the manual operations the user would normally perform. The server then notifies the user of the generated script and requests their approval to run it, allowing the user to manage the automation.

[0439] Once a user approves an automated script, the terminal automatically executes the script under set times or conditions, reducing the user's workload. As a result, users can focus more on their core tasks, leading to improved overall productivity.

[0440] Furthermore, the server aggregates and analyzes task progress data from each team member, providing a dashboard that visually represents the overall progress. This allows project managers to monitor resource allocation in real time. The server also has the functionality to detect task delays and anomalies in progress as needed, and propose resource reallocation. In addition, when an anomaly is detected, it sends alerts to users and stakeholders to encourage prompt action.

[0441] For example, if a user transfers specific data to a spreadsheet daily, the system generates a script to automate this process and proposes automating the data transfer. If the user approves, the task is then automated by the script, allowing the user to dedicate their time to other important tasks. In this way, the present invention streamlines user work and improves overall business productivity.

[0442] The following describes the processing flow.

[0443] Step 1:

[0444] The terminal records user actions in real time and creates operation history data. This data includes the applications used, actions taken within each application, as well as input content and operation time.

[0445] Step 2:

[0446] The terminal sends recorded operation history data to the server at regular intervals. The data is encrypted and properly protected.

[0447] Step 3:

[0448] The server saves the received operation history data to a database and begins analysis. Using a generative AI model, it extracts and identifies recurring work patterns from the data.

[0449] Step 4:

[0450] The server generates an automated script based on the extracted work patterns. This script mimics the work the user was doing under specific conditions and produces equivalent results.

[0451] Step 5:

[0452] The server notifies the user of the generated automation script and its suggestions. The notification includes the script's content and its benefits.

[0453] Step 6:

[0454] The user reviews the proposed automation script and approves or rejects whether to run it. User approval is done through an intuitive user interface.

[0455] Step 7:

[0456] Upon receiving user approval, the device executes an automated script under specified time and conditions. After execution, it reviews the results and sends feedback to the server if necessary.

[0457] Step 8:

[0458] The server integrates task progress data collected from all users and updates a visual dashboard. This makes it possible to monitor the progress of the entire team in real time.

[0459] Step 9:

[0460] The server analyzes progress data and suggests resource reallocation if necessary. It also sends alerts to users and project managers if any anomalies are detected.

[0461] (Example 1)

[0462] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0463] In today's business environment, repetitive tasks that users perform daily often reduce work efficiency. Furthermore, visualizing the overall task progress of a team and appropriately reallocating resources is difficult, which is a common challenge that leads to project delays and wasted resources.

[0464] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0465] In this invention, the server includes means for analyzing the user's operation history using a generated AI model and detecting repetitive work patterns; means for providing a computing device for integrating and visualizing user or team task progress data; and means for improving analytical capabilities using prompt statements and suggesting resource reallocation. This enables the automation of repetitive tasks performed by users and efficient task management for the entire team.

[0466] An "information processing device" is a device that has the function of recording the user's operation history or executing an automated program that has been generated.

[0467] A "processing unit" is a device that analyzes recorded data and has the function of visualizing the task progress of a user or the entire team.

[0468] A "generative AI model" is an artificial intelligence model that analyzes a user's operation history and identifies repetitive work patterns.

[0469] An "automation program" is software code designed to automatically perform detected repetitive tasks.

[0470] A "notification system" is a system for informing the user of the generated automation program and requesting their approval to run it.

[0471] A "prompt message" is an instruction given to a generative AI model, providing information to improve its analytical capabilities.

[0472] A "visualization processing device" is a device that graphically represents the progress of a task and provides information in a format that is easy for users and team members to understand.

[0473] The business efficiency system of the present invention consists of a terminal used by the user and a server that performs central data analysis and business management. By implementing this system, it is possible to automate repetitive tasks in the user's daily work and improve overall business efficiency.

[0474] The terminal records the user's operation history in real time. This operation history includes, for example, usage history of electronic spreadsheet software and word processing applications, as well as keyboard and mouse operation logs. The terminal periodically sends this data to a server, enabling centralized data management.

[0475] The server receives operation history data sent from the terminal and stores it in a database. The server uses a generative AI model to analyze the operation data. Specifically, it uses machine learning libraries such as TensorFlow and PyTorch in its software to identify repetitive work patterns from the data. This allows it to extract tasks that can be effectively automated to improve business efficiency.

[0476] Based on the analysis results, the server generates an automation script. This script is written in a programming language such as Python or Bash and automatically mimics operations that a user would normally perform manually. The server notifies the user of the generated script and requests their approval to execute it. This approval process can be carried out through the user interface.

[0477] Once the user approves the execution of a script, the device automatically runs the script based on the specified conditions. This frees users from tedious routine tasks, allowing them to focus their resources on more creative work. As a result, an overall increase in productivity can be expected.

[0478] For example, in the case of a user who performs the task of transferring specific data to a spreadsheet every day, this system records and analyzes that task and suggests an automation script. If the user approves it, the script automates the task, allowing the user to focus on other tasks.

[0479] An example of a prompt for a generating AI model would be, "Identify routine, repetitive tasks performed by the user and generate scripts that can be automated." Through this prompt, the system can analyze repetitive tasks more efficiently and automate them.

[0480] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0481] Step 1:

[0482] The device records the user's activity history. Specifically, it captures keyboard input, mouse clicks, and application usage information in real time. All user actions are considered input. This data is saved locally in JSON or CSV format and prepared for periodic transmission to the server.

[0483] Step 2:

[0484] The terminal sends the recorded operation history data to the server. A secure communication protocol (e.g., HTTPS) is used to ensure data security during transmission. The input is the operation history data saved in step 1, and the output is the data sent to the server. This transmission eliminates the need for data retention on the client side, enabling centralized management.

[0485] Step 3:

[0486] The server receives operation history data sent from the terminal and stores it in the database. The input is operation history data sent from the terminal, and the output is stored in the database in an organized format. The server uses this stored data for subsequent analysis processing.

[0487] Step 4:

[0488] The server analyzes stored operation history data using a generated AI model. The input is the operation history stored in the database, and the output identifies patterns of frequently performed repetitive tasks. By processing the data using the AI ​​model, it analyzes which operations users perform frequently and identifies procedures that can be automated.

[0489] Step 5:

[0490] The server generates automation scripts based on the analysis results. The input is information about repetitive work patterns identified through the analysis process, and the output is automation scripts in the form of Python or Bash scripts. These scripts are coded to mimic specific application operations.

[0491] Step 6:

[0492] The server notifies the user of the generated script and requests their approval to execute it. The input is the information of the generated automation script, and the output is in the form of a notification to the user. The notification is displayed to the user as a pop-up or an in-app message, and they confirm it using an approval button or similar.

[0493] Step 7:

[0494] If the user approves the execution of a script, the terminal automatically executes the script under the specified conditions. The input is the user's approval information, and the output is the result of the automated task. This automates operations that are normally performed manually, reducing labor.

[0495] Step 8:

[0496] The server creates a dashboard to integrate and visualize the task progress of users and teams. Input is progress data collected from multiple devices, and output is visualized data in graph and chart format. The dashboard allows for a quick overview of resource allocation and project progress.

[0497] (Application Example 1)

[0498] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0499] In manufacturing, repetitive operations and inefficient procedures are common, and optimizing these is necessary to improve productivity. Furthermore, a lack of standardization in operations across different work stations can reduce overall efficiency. Solving these problems and automating and optimizing manufacturing processes is essential.

[0500] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0501] In this invention, the server includes means for analyzing the operation history of manufacturing equipment and detecting repetitive actions, means for generating automation commands for the detected actions, and means for facilitating the integration and efficiency of operations at different work stations. This enables automation and efficiency of operations on the manufacturing floor.

[0502] "User operation history" refers to a record of a series of actions and behaviors performed by a user on a device or terminal.

[0503] An "information processing device" is a device that receives, analyzes, stores, and generates commands for data.

[0504] An "automation command" is a series of actions or instructions for automatically performing repetitive operations.

[0505] A "user" is a person or organization that operates a device or terminal.

[0506] "Manufacturing equipment" refers to the machines and robotic systems used in the manufacturing process.

[0507] "Operation history" refers to a series of records of actions taken by users or manufacturing machines, as recorded by the device.

[0508] "Repetitive action" refers to the repeated execution of the same or similar operation multiple times.

[0509] A "work station" is a place or area in the manufacturing process where specific tasks or operations are performed.

[0510] "Integration" refers to the process of combining multiple elements or data into a single system or format.

[0511] "Efficiency improvement" refers to optimizing work and processes to achieve maximum results with fewer resources and less time.

[0512] To implement this invention, it is necessary to appropriately combine and arrange the manufacturing equipment and the information processing equipment. The server uses machine learning libraries such as Python and TensorFlow to receive and analyze operation history data transmitted from the manufacturing equipment. Specifically, the server identifies repetitive actions from the operations and generates automation commands for those actions.

[0513] The generated automation command is notified to the manufacturing equipment via the information processing device, and once the user approves it, the manufacturing equipment performs automation based on the command. The server also integrates operations across work stations, thereby standardizing operations at different stations. Furthermore, the generated automation command can be input into the generated AI model using the prompt message, "Robots in the factory are repeating the same task for 90% of their operating time. Please suggest automating this task."

[0514] As a concrete example, if tool replacement is frequently performed at a particular factory station, the server analyzes the operation history and generates a command to automate the tool replacement. In this way, the burden on workers can be reduced and the overall efficiency of the work can be improved. This invention promotes automation and standardization in factories and contributes to increased productivity.

[0515] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0516] Step 1:

[0517] The server receives operation history data transmitted from the manufacturing equipment. The input is the operation history recorded by the manufacturing equipment, and the server prepares to analyze the data based on this.

[0518] Step 2:

[0519] The server stores the received operation history data in a database, filters the data, and cleanses it. This process removes noise and prepares the data for optimal analysis. The input is the raw data before filtering, and the output is the formatted data for analysis.

[0520] Step 3:

[0521] The server analyzes the formatted data using a generative AI model to identify patterns of repetitive actions. The input here is the formatted data, and the output is the identified patterns of repetitive actions. This step primarily involves pattern recognition by the AI ​​model.

[0522] Step 4:

[0523] The server generates automation commands based on identified repetitive actions. Using patterns generated by a generative AI model, it creates an optimal action sequence as an automated script. The input is pattern information of the repetitive actions, and the output is the automation command.

[0524] Step 5:

[0525] The server notifies the terminal of the generated automation command and requests the user's approval to execute it. The input is the generated automation command, and the output is the notification to the user. The specific operation here is the visualization and notification of the command.

[0526] Step 6:

[0527] The user reviews the automated command from the terminal and approves or rejects it. The input here is the information of the notified automated command, and the output is the result of approval or rejection.

[0528] Step 7:

[0529] Once the user approves, the server sends an automation command to the manufacturing equipment, instructing it to execute. The input is the user's approval result, and the output is the start of the automated work on the manufacturing equipment. This achieves increased efficiency in the production line.

[0530] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0531] This invention provides a system for improving user work efficiency, combining automation based on user operation history with work improvement suggestions that take user emotions into consideration. This system comprises a terminal used by the user, a server for data analysis, and an emotion engine module.

[0532] The device records the user's operation history in real time and saves details of the applications and operations used. This data is encrypted and sent to a server. Furthermore, the device is equipped with an emotion engine that recognizes emotions from the user's facial expressions and voice, and analyzes the user's emotional state.

[0533] The server analyzes the transmitted operation history data to identify repetitive work patterns. Based on this, it generates automation scripts and proposes them to the user. It also integrates with emotion data obtained from the emotion engine to provide flexible script suggestions tailored to the user's emotions. For example, if the user is experiencing high stress levels, it prioritizes suggesting automation to reduce their workload. The suggestions are then approved by the user and executed on the terminal.

[0534] Users receive automation script suggestions from the server and review their contents. Approved scripts are automatically executed on the terminal, reducing the burden of repetitive daily tasks. Furthermore, the emotion engine allows users to understand their emotional state during work, which can be used to improve their workflow.

[0535] For example, if a user's regularly performed data entry task is causing excessive stress, the emotion engine will detect this. Based on this emotional information, the server will prioritize suggesting the automation of the data entry task, helping to reduce the user's workload. The emotion engine will also record the user's emotional changes and analyze long-term trends to make further suggestions for improving work efficiency.

[0536] Thus, the present invention provides integrated support for improving work efficiency based on user actions and emotions, and offers an environment in which users can perform their work more comfortably.

[0537] The following describes the processing flow.

[0538] Step 1:

[0539] The terminal monitors user activity in real time, recording the software used, details of the operations, and input history. This data is encrypted and periodically transmitted to the server.

[0540] Step 2:

[0541] The emotion engine uses the camera and microphone built into the device to analyze the user's facial expressions and voice, and determines their emotional state in real time. This emotional data, along with the operation history, is sent to the server.

[0542] Step 3:

[0543] The server receives operation history and sentiment data sent from the terminal and stores it in a database. The stored data is then analyzed in detail by a generative AI model.

[0544] Step 4:

[0545] The server identifies repetitive work patterns from the analyzed data and generates automation scripts. These scripts are designed to reduce user effort by automating tasks.

[0546] Step 5:

[0547] When the server notifies the user of automated scripts, it takes into account the results of the emotion engine's analysis. If the user is experiencing stress, it prioritizes suggesting automated scripts that contribute to stress reduction.

[0548] Step 6:

[0549] The user reviews the proposed automation script and decides whether to run it. Approved scripts are executed on the terminal according to the schedule.

[0550] Step 7:

[0551] The terminal executes an automated script approved by the user under specified conditions. The results are recorded as logs and sent to the server as feedback.

[0552] Step 8:

[0553] The server visualizes each user's workload and stress level based on collected task progress data and sentiment data. This allows for real-time monitoring of the team's overall progress.

[0554] Step 9:

[0555] The server uses both progress and sentiment data to reallocate resources and suggest improvements to operations. When an anomaly is detected, it sends alerts to users and project managers.

[0556] (Example 2)

[0557] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0558] While conventional business efficiency systems had functions to analyze and automate user operation history, they did not adequately offer flexible business improvement suggestions that took into account the user's emotional state. Therefore, there was a need for overall efficiency improvement, not only through efficiency improvements via automation of operation patterns, but also through reducing the user's mental burden.

[0559] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0560] In this invention, the server includes a device for recording user operation information, an information processing device for analyzing the recorded operation information and detecting recurring operation patterns, and an emotion analysis device for analyzing the user's emotional state and making suggestions for business improvement. This makes it possible not only to improve the efficiency of the user's operation patterns but also to reduce the workload based on the emotional state.

[0561] "User operation information" refers to data related to a series of actions and interactions a user performs while using a device.

[0562] "Device" refers to hardware or equipment used by a user to record operational information or to perform automated procedures.

[0563] An "information processing device" is a computing system that has the function of analyzing input data and detecting repetitive operation patterns.

[0564] An "automation procedure" is a script or program generated based on a repetitive action pattern to streamline or reduce the effort required for a series of operations.

[0565] "Emotional state" refers to the psychological or emotional state of a user, as analyzed from their facial expressions, voice, and other factors.

[0566] An "emotion analysis device" is a module or system that has the function of analyzing a user's emotional state based on their facial expressions and voice.

[0567] The business efficiency system of the present invention consists of a terminal used daily by the user, a server for analyzing data, and an emotion analysis device for performing emotion analysis. Based on this configuration, the system automates the user's operation patterns and provides business improvement suggestions that take into account the user's emotional state.

[0568] A terminal is a computer or mobile device used by a user. The terminal is equipped with software to record user actions in real time, including the duration of the action, the applications used, and specific interaction details. The terminal's emotion engine module uses a camera and microphone to analyze the user's facial expressions and voice to understand their emotional state. This data is securely encrypted and transmitted to a server over the network.

[0569] The server uses machine learning algorithms to analyze operation information received from the terminal. It detects repetitive action patterns and generates automation procedures that can be made more efficient. Furthermore, based on user emotion data provided by an emotion analysis device, it identifies stress points in the work and provides flexible work improvement suggestions tailored to the user's emotional state. For example, automation procedures may include automating routine tasks performed by users under high stress levels.

[0570] Users receive automation suggestions from the server and have a means to review the procedures on their own terminals. After user approval, the suggested procedures are executed on the terminal. Executing these procedures frees users from routine tasks, allowing them to dedicate more time to higher-level work. Furthermore, users can understand their own emotional state through sentiment analysis data and use this information to improve their work environment.

[0571] For example, if a user experiences excessive stress due to their weekly report writing task, the emotion analysis device will detect that stress level. By analyzing this data on the server, it will prioritize suggesting procedures to automate part of the report generation. In this way, the user's mental and physical burden is reduced.

[0572] An example of a prompt using a generative AI model is, "Suggest a way to simplify a specific task performed by the user."

[0573] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0574] Step 1:

[0575] The terminal records user activity in real time. Inputs include keyboard input, mouse clicks, and application launches and shutdowns. This information is temporarily stored as a log and protected by encryption. The output is encrypted activity data, which is then sent to the server.

[0576] Step 2:

[0577] The device uses a camera and microphone to collect the user's facial expressions and voice, and performs emotion analysis. The input is the user's real-time facial expression and voice data. This data is analyzed by an emotion engine module. Data processing includes facial expression feature extraction and voice emotion tone analysis. As output, digital data indicating the user's emotional state is generated and also sent to the server.

[0578] Step 3:

[0579] The server receives operation information from the terminal and stores it in a database. The input consists of encrypted operation information and sentiment data sent from the terminal in the previous step. The server uses machine learning algorithms to analyze recurring behavior patterns from the operation information. Data normalization and clustering are performed as part of the data calculations. The output is a list of automated procedures with recognized patterns.

[0580] Step 4:

[0581] The server takes in emotion analysis data and evaluates the user's stress level, concentration, and other factors. The input is emotional state data from an emotion analysis device. The system performs calculations such as time-series analysis and trend analysis of the emotional data. The output is a business improvement proposal that takes the user's emotional tendencies into account, and generates scripts with priorities aligned with the user's emotional state.

[0582] Step 5:

[0583] The server notifies the user of the generated automation script and requests confirmation. The inputs are the list of automation steps and business improvement suggestions obtained in steps 3 and 4. As output, a message to notify the user is generated and sent to the terminal.

[0584] Step 6:

[0585] The user reviews the notified automation script and approves its execution. The input consists of the automation proposal and detailed message received from the server. The user reviews the content, selects the appropriate script, and approves it. The output is feedback information regarding the approved script.

[0586] Step 7:

[0587] The terminal automatically executes scripts approved by the user. The input is the approved automation script. The terminal starts the instructed steps and executes them sequentially. The output is the result data of the task completed by the execution. This streamlines repetitive daily tasks and reduces the user's burden.

[0588] (Application Example 2)

[0589] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0590] In today's work environment, employees often perform a wide variety of repetitive tasks, leading to significant workload and stress. Furthermore, the impact of employees' emotional states on work efficiency cannot be ignored, yet there are insufficient tools for analyzing and addressing this issue. Therefore, there is a need for a system that analyzes operation history and considers emotional states to achieve efficient automation and improvement of work processes.

[0591] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0592] In this invention, the server includes a device for recording the user's operation history, a processing device for analyzing the recorded operation history and detecting repetitive work patterns, and means for analyzing the user's emotions from their facial expressions and voice and making suggestions for business improvements using that information. This reduces the workload on employees and enables flexible business improvements that respond to their emotional state.

[0593] "Operation history" refers to the history of a series of operations and procedures performed by a user when using a device.

[0594] A "device" is an electronic device used to record or manage user operation history and emotional data.

[0595] A "processing device" is a device that performs data processing to analyze operation history and emotional data and extract necessary information.

[0596] An "automation program" is a script that mechanically performs predefined procedures in order to efficiently carry out repetitive tasks.

[0597] "Means" refer to methods or devices used to achieve a specific function or purpose.

[0598] "Emotional analysis" is the process of analyzing a user's psychological state and emotions based on non-verbal information such as facial expressions and voice.

[0599] A "business improvement suggestion" is a recommendation of improvement measures or methods to facilitate users' efficient work.

[0600] The system of the present invention consists of an operation history recording device, a sensor with emotion analysis capabilities, and a processing device. The device records the user's operation history in real time and generates an efficient automation program based on it. This also includes a sensor that collects data for analyzing the user's emotional state. Specifically, this is a technology that uses cameras and microphones mounted on smart glasses or other wearable devices to analyze the user's facial expressions and voice.

[0601] The server identifies repetitive work patterns using collected operation history and sentiment data. Data analysis utilizes cloud computing technologies and algorithms designed to efficiently identify operation patterns. For example, Amazon Web Services' data analysis tools could be used.

[0602] Based on the analysis results, the server generates and proposes an appropriate automation program to the user. In particular, if the user's emotions indicate stress, automation of tasks will be prioritized. When making a proposal, clear and simple instructions will be provided from a UI / UX perspective to allow the user to quickly approve it.

[0603] For example, if emotional analysis determines that employees at a logistics center are experiencing increased burden and stress from sorting goods, the system can support efficient operations by suggesting an automated sorting program.

[0604] Through this process, the system helps users perform their tasks in a comfortable environment. An example of a prompt for the generated AI model is, "Generate automation suggestions to reduce the burden of repetitive tasks based on the emotional state of employees at the logistics center."

[0605] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0606] Step 1:

[0607] The terminal records user actions in real time and sends that data to the server. As input, it collects detailed data such as the type and timing of the operation and the functions used. This data is encrypted and then securely transferred to the server using communication technology. The output is operation history data formatted in a way that the server can analyze.

[0608] Step 2:

[0609] The server analyzes received operation history data to identify patterns of recurring tasks. The input is operation history data sent from the terminal, and data mining techniques are used to extract patterns of repeatedly occurring operations. The output is a list of identified recurring task patterns. This process efficiently handles large amounts of data using high-performance computing equipment.

[0610] Step 3:

[0611] The device acquires the user's facial expressions and voice data through its camera and microphone, and performs emotion analysis. Input consists of image and voice data, and emotions are analyzed using a machine learning algorithm. Output is data representing the user's emotional state numerically or categorically. This obtained emotion data is immediately transmitted to the server.

[0612] Step 4:

[0613] The server combines repetitive work patterns and sentiment data to generate an optimal automation program for the user. Inputs include repetitive work patterns from operation history and sentiment data from sentiment analysis. A generative AI model is used to automatically generate programs designed to reduce workload. The output is an automation program in an executable format. The generated program is transferred to the terminal in a format easily understood by the user.

[0614] Step 5:

[0615] The user reviews the proposed automation program on the terminal and chooses whether to approve it. The input is the proposed automation program sent from the server and displayed through the UI. The output is a control signal indicating the user's approval or rejection. If the user approves, the process proceeds to the next step.

[0616] Step 6:

[0617] The terminal executes an automated program based on user approval. The input is the approved automated program, and the terminal automatically performs the instructed operations according to its contents. The output is the result of the executed operations, and the user is notified of the completion of the execution.

[0618] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0619] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0620] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0621] [Fourth Embodiment]

[0622] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0623] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0624] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0625] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0626] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0627] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0628] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0629] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0630] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0631] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0632] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0633] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0634] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0635] The business efficiency system of the present invention consists of a terminal used by the user and a server that performs data analysis and manages operations in a central location. This system makes it possible to automate repetitive tasks in the user's daily work and improve overall business efficiency.

[0636] First, the terminal records the user's operation history in real time. This operation history includes the applications used, keyboard input history, and mouse operations. Furthermore, by periodically sending this operation history to a server, centralized data management is achieved.

[0637] Next, the server receives and analyzes the operation history data sent from the terminal. Using a generative AI model, it can identify repetitive work patterns frequently performed by the user. This analysis extracts operations that can be automated to improve efficiency.

[0638] Based on these analyses, the server generates an automation script for repetitive tasks. This script is designed to automatically produce the same results as the manual operations the user would normally perform. The server then notifies the user of the generated script and requests their approval to run it, allowing the user to manage the automation.

[0639] Once a user approves an automated script, the terminal automatically executes the script under set times or conditions, reducing the user's workload. As a result, users can focus more on their core tasks, leading to improved overall productivity.

[0640] Furthermore, the server aggregates and analyzes task progress data from each team member, providing a dashboard that visually represents the overall progress. This allows project managers to monitor resource allocation in real time. The server also has the functionality to detect task delays and anomalies in progress as needed, and propose resource reallocation. In addition, when an anomaly is detected, it sends alerts to users and stakeholders to encourage prompt action.

[0641] For example, if a user transfers specific data to a spreadsheet daily, the system generates a script to automate this process and proposes automating the data transfer. If the user approves, the task is then automated by the script, allowing the user to dedicate their time to other important tasks. In this way, the present invention streamlines user work and improves overall business productivity.

[0642] The following describes the processing flow.

[0643] Step 1:

[0644] The terminal records user actions in real time and creates operation history data. This data includes the applications used, actions taken within each application, as well as input content and operation time.

[0645] Step 2:

[0646] The terminal sends recorded operation history data to the server at regular intervals. The data is encrypted and properly protected.

[0647] Step 3:

[0648] The server saves the received operation history data to a database and begins analysis. Using a generative AI model, it extracts and identifies recurring work patterns from the data.

[0649] Step 4:

[0650] The server generates an automated script based on the extracted work patterns. This script mimics the work the user was doing under specific conditions and produces equivalent results.

[0651] Step 5:

[0652] The server notifies the user of the generated automation script and its suggestions. The notification includes the script's content and its benefits.

[0653] Step 6:

[0654] The user reviews the proposed automation script and approves or rejects whether to run it. User approval is done through an intuitive user interface.

[0655] Step 7:

[0656] Upon receiving user approval, the device executes an automated script under specified time and conditions. After execution, it reviews the results and sends feedback to the server if necessary.

[0657] Step 8:

[0658] The server integrates task progress data collected from all users and updates a visual dashboard. This makes it possible to monitor the progress of the entire team in real time.

[0659] Step 9:

[0660] The server analyzes progress data and suggests resource reallocation if necessary. It also sends alerts to users and project managers if any anomalies are detected.

[0661] (Example 1)

[0662] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0663] In today's business environment, repetitive tasks that users perform daily often reduce work efficiency. Furthermore, visualizing the overall task progress of a team and appropriately reallocating resources is difficult, which is a common challenge that leads to project delays and wasted resources.

[0664] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0665] In this invention, the server includes means for analyzing the user's operation history using a generated AI model and detecting repetitive work patterns; means for providing a computing device for integrating and visualizing user or team task progress data; and means for improving analytical capabilities using prompt statements and suggesting resource reallocation. This enables the automation of repetitive tasks performed by users and efficient task management for the entire team.

[0666] An "information processing device" is a device that has the function of recording the user's operation history or executing an automated program that has been generated.

[0667] A "processing unit" is a device that analyzes recorded data and has the function of visualizing the task progress of a user or the entire team.

[0668] A "generative AI model" is an artificial intelligence model that analyzes a user's operation history and identifies repetitive work patterns.

[0669] An "automation program" is software code designed to automatically perform detected repetitive tasks.

[0670] A "notification system" is a system for informing the user of the generated automation program and requesting their approval to run it.

[0671] A "prompt message" is an instruction given to a generative AI model, providing information to improve its analytical capabilities.

[0672] A "visualization processing device" is a device that graphically represents the progress of a task and provides information in a format that is easy for users and team members to understand.

[0673] The business efficiency system of the present invention consists of a terminal used by the user and a server that performs central data analysis and business management. By implementing this system, it is possible to automate repetitive tasks in the user's daily work and improve overall business efficiency.

[0674] The terminal records the user's operation history in real time. This operation history includes, for example, usage history of electronic spreadsheet software and word processing applications, as well as keyboard and mouse operation logs. The terminal periodically sends this data to a server, enabling centralized data management.

[0675] The server receives operation history data sent from the terminal and stores it in a database. The server uses a generative AI model to analyze the operation data. Specifically, it uses machine learning libraries such as TensorFlow and PyTorch in its software to identify repetitive work patterns from the data. This allows it to extract tasks that can be effectively automated to improve business efficiency.

[0676] Based on the analysis results, the server generates an automation script. This script is written in a programming language such as Python or Bash and automatically mimics operations that a user would normally perform manually. The server notifies the user of the generated script and requests their approval to execute it. This approval process can be carried out through the user interface.

[0677] Once the user approves the execution of a script, the device automatically runs the script based on the specified conditions. This frees users from tedious routine tasks, allowing them to focus their resources on more creative work. As a result, an overall increase in productivity can be expected.

[0678] For example, in the case of a user who performs the task of transferring specific data to a spreadsheet every day, this system records and analyzes that task and suggests an automation script. If the user approves it, the script automates the task, allowing the user to focus on other tasks.

[0679] An example of a prompt for a generating AI model would be, "Identify routine, repetitive tasks performed by the user and generate scripts that can be automated." Through this prompt, the system can analyze repetitive tasks more efficiently and automate them.

[0680] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0681] Step 1:

[0682] The device records the user's activity history. Specifically, it captures keyboard input, mouse clicks, and application usage information in real time. All user actions are considered input. This data is saved locally in JSON or CSV format and prepared for periodic transmission to the server.

[0683] Step 2:

[0684] The terminal sends the recorded operation history data to the server. A secure communication protocol (e.g., HTTPS) is used to ensure data security during transmission. The input is the operation history data saved in step 1, and the output is the data sent to the server. This transmission eliminates the need for data retention on the client side, enabling centralized management.

[0685] Step 3:

[0686] The server receives operation history data sent from the terminal and stores it in the database. The input is operation history data sent from the terminal, and the output is stored in the database in an organized format. The server uses this stored data for subsequent analysis processing.

[0687] Step 4:

[0688] The server analyzes stored operation history data using a generated AI model. The input is the operation history stored in the database, and the output identifies patterns of frequently performed repetitive tasks. By processing the data using the AI ​​model, it analyzes which operations users perform frequently and identifies procedures that can be automated.

[0689] Step 5:

[0690] The server generates automation scripts based on the analysis results. The input is information about repetitive work patterns identified through the analysis process, and the output is automation scripts in the form of Python or Bash scripts. These scripts are coded to mimic specific application operations.

[0691] Step 6:

[0692] The server notifies the user of the generated script and requests their approval to execute it. The input is the information of the generated automation script, and the output is in the form of a notification to the user. The notification is displayed to the user as a pop-up or an in-app message, and they confirm it using an approval button or similar.

[0693] Step 7:

[0694] If the user approves the execution of a script, the terminal automatically executes the script under the specified conditions. The input is the user's approval information, and the output is the result of the automated task. This automates operations that are normally performed manually, reducing labor.

[0695] Step 8:

[0696] The server creates a dashboard to integrate and visualize the task progress of users and teams. Input is progress data collected from multiple devices, and output is visualized data in graph and chart format. The dashboard allows for a quick overview of resource allocation and project progress.

[0697] (Application Example 1)

[0698] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0699] In manufacturing, repetitive operations and inefficient procedures are common, and optimizing these is necessary to improve productivity. Furthermore, a lack of standardization in operations across different work stations can reduce overall efficiency. Solving these problems and automating and optimizing manufacturing processes is essential.

[0700] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0701] In this invention, the server includes means for analyzing the operation history of manufacturing equipment and detecting repetitive actions, means for generating automation commands for the detected actions, and means for facilitating the integration and efficiency of operations at different work stations. This enables automation and efficiency of operations on the manufacturing floor.

[0702] "User operation history" refers to a record of a series of actions and behaviors performed by a user on a device or terminal.

[0703] An "information processing device" is a device that receives, analyzes, stores, and generates commands for data.

[0704] An "automation command" is a series of actions or instructions for automatically performing repetitive operations.

[0705] A "user" is a person or organization that operates a device or terminal.

[0706] "Manufacturing equipment" refers to the machines and robotic systems used in the manufacturing process.

[0707] "Operation history" refers to a series of records of actions taken by users or manufacturing machines, as recorded by the device.

[0708] "Repetitive action" refers to the repeated execution of the same or similar operation multiple times.

[0709] A "work station" is a place or area in the manufacturing process where specific tasks or operations are performed.

[0710] "Integration" refers to the process of combining multiple elements or data into a single system or format.

[0711] "Efficiency improvement" refers to optimizing work and processes to achieve maximum results with fewer resources and less time.

[0712] To implement this invention, it is necessary to appropriately combine and arrange the manufacturing equipment and the information processing equipment. The server uses machine learning libraries such as Python and TensorFlow to receive and analyze operation history data transmitted from the manufacturing equipment. Specifically, the server identifies repetitive actions from the operations and generates automation commands for those actions.

[0713] The generated automation command is notified to the manufacturing equipment via the information processing device, and once the user approves it, the manufacturing equipment performs automation based on the command. The server also integrates operations across work stations, thereby standardizing operations at different stations. Furthermore, the generated automation command can be input into the generated AI model using the prompt message, "Robots in the factory are repeating the same task for 90% of their operating time. Please suggest automating this task."

[0714] As a concrete example, if tool replacement is frequently performed at a particular factory station, the server analyzes the operation history and generates a command to automate the tool replacement. In this way, the burden on workers can be reduced and the overall efficiency of the work can be improved. This invention promotes automation and standardization in factories and contributes to increased productivity.

[0715] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0716] Step 1:

[0717] The server receives operation history data transmitted from the manufacturing equipment. The input is the operation history recorded by the manufacturing equipment, and the server prepares to analyze the data based on this.

[0718] Step 2:

[0719] The server stores the received operation history data in a database, filters the data, and cleanses it. This process removes noise and prepares the data for optimal analysis. The input is the raw data before filtering, and the output is the formatted data for analysis.

[0720] Step 3:

[0721] The server analyzes the formatted data using a generative AI model to identify patterns of repetitive actions. The input here is the formatted data, and the output is the identified patterns of repetitive actions. This step primarily involves pattern recognition by the AI ​​model.

[0722] Step 4:

[0723] The server generates automation commands based on identified repetitive actions. Using patterns generated by a generative AI model, it creates an optimal action sequence as an automated script. The input is pattern information of the repetitive actions, and the output is the automation command.

[0724] Step 5:

[0725] The server notifies the terminal of the generated automation command and requests the user's approval to execute it. The input is the generated automation command, and the output is the notification to the user. The specific operation here is the visualization and notification of the command.

[0726] Step 6:

[0727] The user reviews the automated command from the terminal and approves or rejects it. The input here is the information of the notified automated command, and the output is the result of approval or rejection.

[0728] Step 7:

[0729] Once the user approves, the server sends an automation command to the manufacturing equipment, instructing it to execute. The input is the user's approval result, and the output is the start of the automated work on the manufacturing equipment. This achieves increased efficiency in the production line.

[0730] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0731] This invention provides a system for improving user work efficiency, combining automation based on user operation history with work improvement suggestions that take user emotions into consideration. This system comprises a terminal used by the user, a server for data analysis, and an emotion engine module.

[0732] The device records the user's operation history in real time and saves details of the applications and operations used. This data is encrypted and sent to a server. Furthermore, the device is equipped with an emotion engine that recognizes emotions from the user's facial expressions and voice, and analyzes the user's emotional state.

[0733] The server analyzes the transmitted operation history data to identify repetitive work patterns. Based on this, it generates automation scripts and proposes them to the user. It also integrates with emotion data obtained from the emotion engine to provide flexible script suggestions tailored to the user's emotions. For example, if the user is experiencing high stress levels, it prioritizes suggesting automation to reduce their workload. The suggestions are then approved by the user and executed on the terminal.

[0734] Users receive automation script suggestions from the server and review their contents. Approved scripts are automatically executed on the terminal, reducing the burden of repetitive daily tasks. Furthermore, the emotion engine allows users to understand their emotional state during work, which can be used to improve their workflow.

[0735] For example, if a user's regularly performed data entry task is causing excessive stress, the emotion engine will detect this. Based on this emotional information, the server will prioritize suggesting the automation of the data entry task, helping to reduce the user's workload. The emotion engine will also record the user's emotional changes and analyze long-term trends to make further suggestions for improving work efficiency.

[0736] Thus, the present invention provides integrated support for improving work efficiency based on user actions and emotions, and offers an environment in which users can perform their work more comfortably.

[0737] The following describes the processing flow.

[0738] Step 1:

[0739] The terminal monitors user activity in real time, recording the software used, details of the operations, and input history. This data is encrypted and periodically transmitted to the server.

[0740] Step 2:

[0741] The emotion engine uses the camera and microphone built into the device to analyze the user's facial expressions and voice, and determines their emotional state in real time. This emotional data, along with the operation history, is sent to the server.

[0742] Step 3:

[0743] The server receives operation history and sentiment data sent from the terminal and stores it in a database. The stored data is then analyzed in detail by a generative AI model.

[0744] Step 4:

[0745] The server identifies repetitive work patterns from the analyzed data and generates automation scripts. These scripts are designed to reduce user effort by automating tasks.

[0746] Step 5:

[0747] When the server notifies the user of automated scripts, it takes into account the results of the emotion engine's analysis. If the user is experiencing stress, it prioritizes suggesting automated scripts that contribute to stress reduction.

[0748] Step 6:

[0749] The user reviews the proposed automation script and decides whether to run it. Approved scripts are executed on the terminal according to the schedule.

[0750] Step 7:

[0751] The terminal executes an automated script approved by the user under specified conditions. The results are recorded as logs and sent to the server as feedback.

[0752] Step 8:

[0753] The server visualizes each user's workload and stress level based on collected task progress data and sentiment data. This allows for real-time monitoring of the team's overall progress.

[0754] Step 9:

[0755] The server uses both progress and sentiment data to reallocate resources and suggest improvements to operations. When an anomaly is detected, it sends alerts to users and project managers.

[0756] (Example 2)

[0757] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0758] While conventional business efficiency systems had functions to analyze and automate user operation history, they did not adequately offer flexible business improvement suggestions that took into account the user's emotional state. Therefore, there was a need for overall efficiency improvement, not only through efficiency improvements via automation of operation patterns, but also through reducing the user's mental burden.

[0759] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0760] In this invention, the server includes a device for recording user operation information, an information processing device for analyzing the recorded operation information and detecting recurring operation patterns, and an emotion analysis device for analyzing the user's emotional state and making suggestions for business improvement. This makes it possible not only to improve the efficiency of the user's operation patterns but also to reduce the workload based on the emotional state.

[0761] "User operation information" refers to data related to a series of actions and interactions a user performs while using a device.

[0762] "Device" refers to hardware or equipment used by a user to record operational information or to perform automated procedures.

[0763] An "information processing device" is a computing system that has the function of analyzing input data and detecting repetitive operation patterns.

[0764] An "automation procedure" is a script or program generated based on a repetitive action pattern to streamline or reduce the effort required for a series of operations.

[0765] "Emotional state" refers to the psychological or emotional state of a user, as analyzed from their facial expressions, voice, and other factors.

[0766] An "emotion analysis device" is a module or system that has the function of analyzing a user's emotional state based on their facial expressions and voice.

[0767] The business efficiency system of the present invention consists of a terminal used daily by the user, a server for analyzing data, and an emotion analysis device for performing emotion analysis. Based on this configuration, the system automates the user's operation patterns and provides business improvement suggestions that take into account the user's emotional state.

[0768] A terminal is a computer or mobile device used by a user. The terminal is equipped with software to record user actions in real time, including the duration of the action, the applications used, and specific interaction details. The terminal's emotion engine module uses a camera and microphone to analyze the user's facial expressions and voice to understand their emotional state. This data is securely encrypted and transmitted to a server over the network.

[0769] The server uses machine learning algorithms to analyze operation information received from the terminal. It detects repetitive action patterns and generates automation procedures that can be made more efficient. Furthermore, based on user emotion data provided by an emotion analysis device, it identifies stress points in the work and provides flexible work improvement suggestions tailored to the user's emotional state. For example, automation procedures may include automating routine tasks performed by users under high stress levels.

[0770] Users receive automation suggestions from the server and have a means to review the procedures on their own terminals. After user approval, the suggested procedures are executed on the terminal. Executing these procedures frees users from routine tasks, allowing them to dedicate more time to higher-level work. Furthermore, users can understand their own emotional state through sentiment analysis data and use this information to improve their work environment.

[0771] For example, if a user experiences excessive stress due to their weekly report writing task, the emotion analysis device will detect that stress level. By analyzing this data on the server, it will prioritize suggesting procedures to automate part of the report generation. In this way, the user's mental and physical burden is reduced.

[0772] An example of a prompt using a generative AI model is, "Suggest a way to simplify a specific task performed by the user."

[0773] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0774] Step 1:

[0775] The terminal records user activity in real time. Inputs include keyboard input, mouse clicks, and application launches and shutdowns. This information is temporarily stored as a log and protected by encryption. The output is encrypted activity data, which is then sent to the server.

[0776] Step 2:

[0777] The device uses a camera and microphone to collect the user's facial expressions and voice, and performs emotion analysis. The input is the user's real-time facial expression and voice data. This data is analyzed by an emotion engine module. Data processing includes facial expression feature extraction and voice emotion tone analysis. As output, digital data indicating the user's emotional state is generated and also sent to the server.

[0778] Step 3:

[0779] The server receives operation information from the terminal and stores it in a database. The input consists of encrypted operation information and sentiment data sent from the terminal in the previous step. The server uses machine learning algorithms to analyze recurring behavior patterns from the operation information. Data normalization and clustering are performed as part of the data calculations. The output is a list of automated procedures with recognized patterns.

[0780] Step 4:

[0781] The server takes in emotion analysis data and evaluates the user's stress level, concentration, and other factors. The input is emotional state data from an emotion analysis device. The system performs calculations such as time-series analysis and trend analysis of the emotional data. The output is a business improvement proposal that takes the user's emotional tendencies into account, and generates scripts with priorities aligned with the user's emotional state.

[0782] Step 5:

[0783] The server notifies the user of the generated automation script and requests confirmation. The inputs are the list of automation steps and business improvement suggestions obtained in steps 3 and 4. As output, a message to notify the user is generated and sent to the terminal.

[0784] Step 6:

[0785] The user reviews the notified automation script and approves its execution. The input consists of the automation proposal and detailed message received from the server. The user reviews the content, selects the appropriate script, and approves it. The output is feedback information regarding the approved script.

[0786] Step 7:

[0787] The terminal automatically executes scripts approved by the user. The input is the approved automation script. The terminal starts the instructed steps and executes them sequentially. The output is the result data of the task completed by the execution. This streamlines repetitive daily tasks and reduces the user's burden.

[0788] (Application Example 2)

[0789] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0790] In today's work environment, employees often perform a wide variety of repetitive tasks, leading to significant workload and stress. Furthermore, the impact of employees' emotional states on work efficiency cannot be ignored, yet there are insufficient tools for analyzing and addressing this issue. Therefore, there is a need for a system that analyzes operation history and considers emotional states to achieve efficient automation and improvement of work processes.

[0791] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0792] In this invention, the server includes a device for recording the user's operation history, a processing device for analyzing the recorded operation history and detecting repetitive work patterns, and means for analyzing the user's emotions from their facial expressions and voice and making suggestions for business improvements using that information. This reduces the workload on employees and enables flexible business improvements that respond to their emotional state.

[0793] "Operation history" refers to the history of a series of operations and procedures performed by a user when using a device.

[0794] A "device" is an electronic device used to record or manage user operation history and emotional data.

[0795] A "processing device" is a device that performs data processing to analyze operation history and emotional data and extract necessary information.

[0796] An "automation program" is a script that mechanically performs predefined procedures in order to efficiently carry out repetitive tasks.

[0797] "Means" refer to methods or devices used to achieve a specific function or purpose.

[0798] "Emotional analysis" is the process of analyzing a user's psychological state and emotions based on non-verbal information such as facial expressions and voice.

[0799] A "business improvement suggestion" is a recommendation of improvement measures or methods to facilitate users' efficient work.

[0800] The system of the present invention consists of an operation history recording device, a sensor with emotion analysis capabilities, and a processing device. The device records the user's operation history in real time and generates an efficient automation program based on it. This also includes a sensor that collects data for analyzing the user's emotional state. Specifically, this is a technology that uses cameras and microphones mounted on smart glasses or other wearable devices to analyze the user's facial expressions and voice.

[0801] The server identifies repetitive work patterns using collected operation history and sentiment data. Data analysis utilizes cloud computing technologies and algorithms designed to efficiently identify operation patterns. For example, Amazon Web Services' data analysis tools could be used.

[0802] Based on the analysis results, the server generates and proposes an appropriate automation program to the user. In particular, if the user's emotions indicate stress, automation of tasks will be prioritized. When making a proposal, clear and simple instructions will be provided from a UI / UX perspective to allow the user to quickly approve it.

[0803] For example, if emotional analysis determines that employees at a logistics center are experiencing increased burden and stress from sorting goods, the system can support efficient operations by suggesting an automated sorting program.

[0804] Through this process, the system helps users perform their tasks in a comfortable environment. An example of a prompt for the generated AI model is, "Generate automation suggestions to reduce the burden of repetitive tasks based on the emotional state of employees at the logistics center."

[0805] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0806] Step 1:

[0807] The terminal records user actions in real time and sends that data to the server. As input, it collects detailed data such as the type and timing of the operation and the functions used. This data is encrypted and then securely transferred to the server using communication technology. The output is operation history data formatted in a way that the server can analyze.

[0808] Step 2:

[0809] The server analyzes received operation history data to identify patterns of recurring tasks. The input is operation history data sent from the terminal, and data mining techniques are used to extract patterns of repeatedly occurring operations. The output is a list of identified recurring task patterns. This process efficiently handles large amounts of data using high-performance computing equipment.

[0810] Step 3:

[0811] The device acquires the user's facial expressions and voice data through its camera and microphone, and performs emotion analysis. Input consists of image and voice data, and emotions are analyzed using a machine learning algorithm. Output is data representing the user's emotional state numerically or categorically. This obtained emotion data is immediately transmitted to the server.

[0812] Step 4:

[0813] The server combines repetitive work patterns and sentiment data to generate an optimal automation program for the user. Inputs include repetitive work patterns from operation history and sentiment data from sentiment analysis. A generative AI model is used to automatically generate programs designed to reduce workload. The output is an automation program in an executable format. The generated program is transferred to the terminal in a format easily understood by the user.

[0814] Step 5:

[0815] The user reviews the proposed automation program on the terminal and chooses whether to approve it. The input is the proposed automation program sent from the server and displayed through the UI. The output is a control signal indicating the user's approval or rejection. If the user approves, the process proceeds to the next step.

[0816] Step 6:

[0817] The terminal executes an automated program based on user approval. The input is the approved automated program, and the terminal automatically performs the instructed operations according to its contents. The output is the result of the executed operations, and the user is notified of the completion of the execution.

[0818] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0819] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0820] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0821] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0822] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0823] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0824] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0825] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0826] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0827] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0828] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0829] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0830] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0831] 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.

[0832] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0833] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0834] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0835] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0836] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0837] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0838] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0839] The following is further disclosed regarding the embodiments described above.

[0840] (Claim 1)

[0841] A terminal that records the user's operation history,

[0842] A server that analyzes recorded operation history and detects repetitive work patterns,

[0843] A server that generates automation scripts for detected repetitive tasks,

[0844] A means of notifying the user of the generated script and requesting their permission to execute it,

[0845] A terminal that automatically executes a script based on user approval,

[0846] A business efficiency system that includes this.

[0847] (Claim 2)

[0848] The business efficiency system according to claim 1, wherein the server has means for integrating and visualizing the task progress data of team members.

[0849] (Claim 3)

[0850] The business efficiency system according to claim 1, wherein the server has means for proposing resource reallocation based on the progress of tasks and means for sending an alert when an anomaly is detected.

[0851] "Example 1"

[0852] (Claim 1)

[0853] An information processing device that records the user's operation history,

[0854] A computing device that analyzes recorded operation history and detects repetitive work patterns,

[0855] A computing device that generates automation programs for repetitive tasks detected using a generative AI model,

[0856] A means of notifying the user of the generated program and requesting their permission to run it,

[0857] An information processing device that automatically executes a script based on user approval,

[0858] A computing device for integrating and visualizing user or team task progress data,

[0859] A system that includes this.

[0860] (Claim 2)

[0861] The system according to claim 1, further comprising means for improving analytical capabilities using prompt statements and proposing resource reallocation, and means for sending a warning when an anomaly is detected.

[0862] (Claim 3)

[0863] The system according to claim 1, further comprising means for graphically displaying the completion rate based on the progress of a task.

[0864] "Application Example 1"

[0865] (Claim 1)

[0866] A device that records the user's operation history,

[0867] An information processing device that analyzes recorded operation history and detects repetitive work patterns,

[0868] An information processing device that generates automation commands for detected repetitive tasks,

[0869] A means of notifying the user of the generated command and requesting their approval to execute it,

[0870] A device that automatically executes commands based on user approval,

[0871] A means for recording the operation history of manufacturing equipment, analyzing repetitive actions, and generating commands for automation,

[0872] A system that includes this.

[0873] (Claim 2)

[0874] The system according to claim 1, wherein the manufacturing apparatus has means for recording and integrating operations at different work stations.

[0875] (Claim 3)

[0876] The system according to claim 1, wherein the manufacturing equipment has means for learning repetitive tasks and proposing their automation in order to improve work efficiency.

[0877] "Example 2 of combining an emotion engine"

[0878] (Claim 1)

[0879] A device for recording user operation information,

[0880] An information processing device that analyzes recorded operation information and detects repetitive operation patterns,

[0881] An information processing device that generates an automation procedure for detected repetitive actions,

[0882] An emotion analysis device that analyzes the user's emotional state and makes suggestions for business improvement,

[0883] A means of notifying the user of the generated procedure and requesting their approval to execute it,

[0884] A device that automatically executes procedures based on user approval,

[0885] A system that includes this.

[0886] (Claim 2)

[0887] The system according to claim 1, wherein the information processing device has means for integrating and illustrating worker progress information.

[0888] (Claim 3)

[0889] The system according to claim 1, wherein the information processing device has means for suggesting resource reallocation based on progress and means for transmitting a warning when an anomaly is detected.

[0890] "Application example 2 when combining with an emotional engine"

[0891] (Claim 1)

[0892] A device that records the user's operation history,

[0893] A processing device that analyzes recorded operation history and detects repetitive work patterns,

[0894] A processing unit that generates an automation program for detected repetitive tasks,

[0895] A means of notifying the user of the generated program and requesting their permission to run it,

[0896] A device that automatically executes a program based on user approval,

[0897] A method for analyzing user emotions from facial expressions and voice, and using that information to propose business improvements,

[0898] A system that includes this.

[0899] (Claim 2)

[0900] The system according to claim 1, comprising means for integrating operation history and sentiment data to generate suggestions for long-term business efficiency improvements.

[0901] (Claim 3)

[0902] The system according to claim 1, further comprising means for determining automation priorities to reduce the burden of repetitive tasks, taking into account the emotional state of the user. [Explanation of Symbols]

[0903] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A device that records the user's operation history, An information processing device that analyzes recorded operation history and detects repetitive work patterns, An information processing device that generates automation commands for detected repetitive tasks, A means of notifying the user of the generated command and requesting their approval to execute it, A device that automatically executes commands based on user approval, A means for recording the operation history of manufacturing equipment, analyzing repetitive actions, and generating commands for automation, A system that includes this.

2. The system according to claim 1, wherein the manufacturing apparatus has means for recording and integrating operations at different work stations.

3. The system according to claim 1, wherein the manufacturing equipment has means for learning repetitive tasks and proposing their automation in order to improve work efficiency.

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