system

Abstract

We provide the system. [Solution] Means for collecting operational data, A means of generating business processes by analyzing collected operational data, A generation device that generates business improvement proposals based on the generated business processes, A means of notifying the generated business improvement proposals, 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 the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds 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 business operations, operations using a PC are common, but often include inefficiencies and wasteful procedures due to human operations, and there is a need to improve business efficiency. However, with the conventional methods, it has been difficult to efficiently collect and analyze operation logs and link them to business improvement. Furthermore, although it is commercially useful to automatically visualize existing business processes and generate adaptable improvement plans, this has not been fully achieved. In such a situation, there is a need for a system that visualizes inefficient operations that users are not aware of and provides efficient business improvement measures.

Means for Solving the Problems

[0005] This invention provides means for collecting operation data, thereby recording user operations on a PC in detail. By utilizing means for analyzing the recorded operation data and generating business processes, the current business flow is automatically visualized and analyzed. Furthermore, it incorporates a generation device that generates business improvement proposals based on the analysis results, and makes these specific improvement proposals available to the user through notification means. This allows the user to objectively understand the efficiency of their own operations and easily implement the proposed improvement proposals.

[0006] "Operation data" refers to information about the operations performed by a user on a computer, and specifically includes things like launching applications, manipulating windows, typing, and using the mouse.

[0007] "Analysis" refers to the process of analyzing information based on collected data to reveal patterns and trends.

[0008] A "work process" refers to the steps and flow required to carry out a task, expressing the order and relationships between tasks.

[0009] A "generation device" refers to a device or software used to create new information or proposals based on analysis results.

[0010] "Means of notification" refers to methods or devices for informing users of generated information or suggestions, and specifically includes pop-up notifications and email notifications. [Brief explanation of the drawing]

[0011] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4]This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0012] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0013] First, let's explain the terminology used in the following explanation.

[0014] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple 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.

[0015] 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.

[0016] 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.

[0017] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

[0018] 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."

[0019] [First Embodiment]

[0020] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0021] 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.

[0022] 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).

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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.

[0027] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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".

[0032] The present invention is a system that records in detail the user's operations on a PC, generates business processes from those records, and further generates business improvement proposals based on those processes. This system includes means for collecting operation data, means for analyzing data, a generation device, and means for notifying.

[0033] Server processing:

[0034] The server receives user operation data sent from the terminal and stores it in a database. This data includes application startup status, user input patterns, and details about window operations, which the server uses to prepare for analyzing operation trends and frequencies. This analysis can identify workflows and inefficient processes. For example, the server can quantify the operations a user performs when creating a daily report and show which steps take the most time.

[0035] Terminal processing:

[0036] The terminal records user actions on the PC in real time and sends the data to the server at regular intervals. For example, it meticulously records the click locations and input data when a user edits a spreadsheet. This operation recording is performed in the background and is designed not to interrupt the user's work.

[0037] User actions:

[0038] Based on notifications from their devices, users review business process improvement suggestions generated by the server. These notifications are provided as pop-ups or dashboard updates. Users can review the suggestions and decide whether to incorporate them into their work. Specifically, if automated work procedures are suggested, users can apply the scripts to reduce work time.

[0039] The system of this invention enables users to objectively review their own work flow and quickly improve inefficient areas. Through the cooperation of the server and terminals, the system automates the collection, analysis, and generation of suggestions for operational data, resulting in an overall improvement in work efficiency.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The terminal records the user's PC operations in real time. This operation record includes application launches, window activations, keystrokes, and mouse click locations. This data is buffered at regular intervals.

[0043] Step 2:

[0044] The terminal periodically sends buffered operation data to the server. Before transmission, the data is properly formatted and includes user ID and timestamp information.

[0045] Step 3:

[0046] The server receives operation data from the terminal and stores it in a database. The data is organized by user and stored in a format suitable for analysis.

[0047] Step 4:

[0048] The server analyzes the accumulated data using analytical tools. Specifically, it executes algorithms to extract patterns in business workflows and inefficient operating procedures.

[0049] Step 5:

[0050] The server generates a business flow in a BPMN-based format based on the analysis results. This flow is formatted to be visually easy for the user to understand.

[0051] Step 6:

[0052] The server uses an AI model to create business improvement proposals based on the generated business flow. These proposals include reducing redundant steps and suggesting processes that can be automated.

[0053] Step 7:

[0054] The server sends the completed business improvement proposal to the terminal. The submitted improvement proposal is then presented to the user via notifications or on the dashboard.

[0055] Step 8:

[0056] Users check notifications from their devices and carefully review the proposed work improvement suggestions. If necessary, they agree to the suggestions and incorporate them into their work to improve efficiency.

[0057] (Example 1)

[0058] 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."

[0059] When aiming to improve business efficiency, conventional systems faced the challenge of not being able to effectively collect and analyze user operation data and quickly and appropriately notify users of improvement suggestions based on the results. As a result, optimizing business processes took a long time, and consequently, the full potential of the improvements could not be realized.

[0060] 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.

[0061] In this invention, the server includes means for recording user operations, means for analyzing the recorded data to identify business procedures, and means for generating and notifying efficiency improvement suggestions based on the analysis results. This makes it possible to precisely analyze the user's workflow and immediately propose business improvement plans.

[0062] "Operational actions" refer to a series of actions performed by a user on a computer, such as inputting data, clicking, and operating applications.

[0063] "Means of recording" refers to methods and devices for recording user actions as digital data.

[0064] "Means of analysis" refers to algorithms and software used to analyze recorded operational data.

[0065] "Functional procedures" refer to specific steps and procedures that indicate the flow of business processes and tasks, as identified through analysis.

[0066] "Efficiency improvement proposals" refer to improvement plans and recommendations for performing tasks more efficiently based on identified functional procedures.

[0067] A "generation device" refers to hardware and software used to generate efficiency improvement suggestions from data analysis results.

[0068] "Means of notification" refers to methods or devices for communicating generated efficiency suggestions to users.

[0069] "Data repository creation" refers to the process of organizing analyzed data and storing it in a database format.

[0070] "Visual means" refers to methods and devices that display data and information to users in a graphical format to aid their understanding.

[0071] This system aims to improve work efficiency by recording and analyzing user actions. Specifically, terminals use dedicated software to sequentially record user actions and periodically send this data to a server. This software includes functions for tracking operations and generating logs. The server stores the received data in a dedicated database and uses analysis algorithms to identify the workflow. Statistical software and machine learning models are used for data analysis to clarify factors hindering work efficiency. Based on the generated functional procedures, the server creates efficiency improvement suggestions. These suggestions are generated using a generative AI model and present specific improvement plans tailored to the user's characteristics.

[0072] Efficiency suggestions are delivered to the user's device as pop-up notifications or dashboard updates, which the user then reviews. The user can then apply the suggested improvements to their own work processes. For example, in tasks requiring frequent data entry, a script to automate the data entry process might be suggested. This can significantly reduce the time required for the task.

[0073] The following are specific examples of prompt statements.

[0074] "Please generate suggestions for improving work efficiency."

[0075] "Please provide a way to automate the data entry process."

[0076] Therefore, the purpose of this system is to appropriately monitor and analyze the user's work and provide concrete and effective suggestions for efficiency improvements.

[0077] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0078] Step 1:

[0079] The device records user actions in real time. Specifically, it captures data such as mouse click locations, keyboard input, and application launches and shutdowns. This is the input, and the recorded series of actions becomes the output. This process runs in the background and is designed not to interrupt the user's work.

[0080] Step 2:

[0081] The terminal sends operation logs recorded at regular intervals to the server. The transmitted data is the input, and the storage of that data in the server's database is the output. The terminal verifies the integrity and accuracy of the data and performs error checking during the transmission process.

[0082] Step 3:

[0083] The server analyzes the received operation log data. The input is the operation logs stored in the database, and the output is the identification of the analyzed business procedures. Specifically, it uses an analysis algorithm to identify frequently occurring operations and time-consuming steps, and quantifies the business flow.

[0084] Step 4:

[0085] The server uses a generative AI model to create efficiency suggestions based on the analysis results. The analysis results are input into the generative AI model, and the output is a user-optimized suggestion. This process generates automatable procedures and hints for work improvement.

[0086] Step 5:

[0087] The server sends the generated efficiency suggestions to the terminal. The user receives notifications on the terminal via pop-ups or dashboards. The output is that the suggestions are entered and the user recognizes the improvement suggestions. Specifically, the user reviews the suggestions and decides whether to apply them to their own workflow.

[0088] (Application Example 1)

[0089] 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."

[0090] In modern factory production lines, it is essential to effectively collect and analyze operation logs from various machines to optimize efficient production processes. However, conventional systems have limitations in automatically generating and notifying improvement suggestions based on the effective collection and analysis of operation and motion data. This has resulted in insufficient factory efficiency and wasted time and resources.

[0091] 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.

[0092] In this invention, the server includes means for collecting operation data, means for analyzing the collected operation data to generate business processes, and an information processing device for generating business improvement proposals based on the generated business processes. This makes it possible to efficiently collect and analyze operation data of factory machinery and robots, and to automatically generate and propose optimal operation procedures.

[0093] "Means for collecting operation data" refers to a mechanism or process for collecting information about operations performed by a user or device.

[0094] "Means for generating business processes" refers to devices or algorithms for analyzing collected data, extracting business flows and procedures, and visualizing them.

[0095] An "information processing device" is a computing device or system for processing data and generating new information based on that data.

[0096] A "knowledge generation device" is a device that executes algorithms or programs to propose efficient procedures and actions based on collected motion data.

[0097] A "visual presentation device" is a device or interface that displays information to a user in text, graphics, or other forms to aid in understanding.

[0098] An "information management device" is a system for organizing, storing, and providing data in a format that is accessible as needed.

[0099] This system collects and analyzes operational data from various robots and machines in real time to improve production efficiency within the factory and generates optimization proposals. First, an operational data collection device installed in the terminal collects detailed data on the operation of each robot. This collected data includes the start and stop timing of work, movement patterns, and energy consumption.

[0100] The collected data is sent directly to the server. The server uses an information processing device to analyze the operational data. This analysis uses Python and its data analysis libraries, specifically Pandas and NumPy. From the information obtained through data analysis, business processes are generated, and inefficient processes and bottlenecks are identified. Furthermore, this information is processed by a knowledge generation device, and the optimal operating procedure is proposed. Machine learning libraries such as TENSORFLOW® are used for business process generation.

[0101] The proposed business improvement plans are displayed on a dashboard using a visual display device. This allows users to review the new work procedures and decide whether or not to implement them. For example, a proposal might be made to shorten the route taken when transporting a specific part, which is expected to reduce travel time by approximately 20%.

[0102] Furthermore, by utilizing a generative AI model and taking input such as "Please propose an optimization plan to improve transport efficiency based on the latest transport data" as an example of a prompt, the system can provide more accurate suggestions. In this way, the entire system can objectively and efficiently improve operations within the factory.

[0103] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0104] Step 1:

[0105] The terminal collects operational data from factory robots in real time. This data includes the start and stop timings, movement paths, and energy consumption of each robot. The input is robot sensor information, and the output is operation log data compiled from this information. The collected data is stored in a buffer and periodically sent to the server.

[0106] Step 2:

[0107] The server receives operation log data sent from the terminal and stores it in the database. The input is the operation log data received from the terminal, and the output is structured information recorded in the database. A database management system is used to organize this data.

[0108] Step 3:

[0109] The server analyzes the received data using a data analysis engine. The input is robot operation data stored in a database. Using Python and data analysis libraries such as Pandas and NumPy, trends and patterns are extracted from the data, and a report indicating process bottlenecks is generated as output.

[0110] Step 4:

[0111] The server uses the generated AI model based on the analysis results to produce business improvement proposals. An example of a prompt message provided to the AI ​​is, "Please suggest the optimal operating procedure for a specific transport task." The input is the analysis results obtained earlier, and the output is the recommended operating procedure or improvement proposal.

[0112] Step 5:

[0113] The user receives business improvement suggestions generated from the server through a visual display device. The input is improvement suggestion data from the server, and the output is visualized improvement suggestions displayed on a dashboard. The user reviews these and decides whether to implement them.

[0114] 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.

[0115] This invention relates to a system that generates business processes by meticulously recording user operation data on a PC and analyzing that data. By combining this system with an emotion engine to recognize user emotions, the system considers the user's reaction to automatically generated business improvement proposals and provides more personalized improvement suggestions.

[0116] Server processing:

[0117] The server receives operation data sent from the terminal and stores it in a database. This collected data includes not only the operation history of the applications used by the user, but also sentiment data generated by the sentiment engine. The server analyzes this data to generate a business flow. The generated business flow allows for the extraction of elements that contribute to business improvement through visualization based on BPMN and comparison with sentiment data. For example, if the server finds that a user tends to show frustration at a particular step, it will identify that part of the work as a target for improvement.

[0118] Terminal processing:

[0119] The terminal not only records the user's PC operations in real time, but also uses an emotion engine to analyze the user's emotions from their facial expressions and voice. The analyzed emotion data is sent to the server along with the operation log. For example, if a user is confused when performing a difficult operation, that emotion is captured as data and taken into consideration in subsequent analysis.

[0120] User actions:

[0121] Users receive business improvement suggestions generated by the server and access them through notifications. These suggestions are refined based on user emotional feedback and are designed to reduce user stress. For example, tasks that frequently cause frustration are suggested to be automated.

[0122] This invention provides a system that offers deep insights for improving work efficiency from both user operation behavior and emotional data, and provides users with practical and highly satisfying improvement suggestions. The system not only improves the quality of user work but also significantly contributes to enhancing the user experience.

[0123] The following describes the processing flow.

[0124] Step 1:

[0125] The device records the user's PC operations in real time and uses an emotion engine to detect emotions from the user's facial expressions and voice. Emotional data includes emotions such as joy, confusion, and frustration.

[0126] Step 2:

[0127] The device buffers the collected operational and emotional data at regular intervals and formats them into a single dataset.

[0128] Step 3:

[0129] The terminal sends a formatted dataset to the server. This dataset includes timestamps and user profile information to ensure data consistency.

[0130] Step 4:

[0131] The server stores the received datasets in a database. The data is organized separately for each user and stored in a format suitable for analysis.

[0132] Step 5:

[0133] The server processes the accumulated data using analysis tools. Specifically, it generates business flows and visualizes user sentiment trends based on operation patterns and sentiment data.

[0134] Step 6:

[0135] The server generates business improvement proposals based on the analysis results. Here, the improvement suggestions focus particularly on steps where users exhibit strong emotions (e.g., frustration or confusion).

[0136] Step 7:

[0137] The server sends the generated business improvement proposals to the terminal. The improvement proposals are appropriately visualized and provided in a format that is easy for the user to understand.

[0138] Step 8:

[0139] Users can review the suggested improvements notified via their devices, evaluate their content, and incorporate them into their own work. Suggestions that are particularly considerate of users' emotions make them more likely to accept the improvements.

[0140] (Example 2)

[0141] 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 as the "terminal".

[0142] In today's work environment, improving work procedures is essential for efficient and stress-free work execution. However, conventional systems analyze only information about user actions to improve processes, making it difficult to provide personalized improvement suggestions that take into account user emotions and reactions. Furthermore, many process improvement proposals tend to overlook some key points, limiting the overall efficiency of operations.

[0143] 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.

[0144] In this invention, the server includes means for collecting information about operations, means for analyzing the collected information about operations to generate work procedures, and means for analyzing the user's emotions and incorporating the feedback into suggestions for improving operations. This enables the dynamic improvement of work procedures in response to the user's emotional reactions, leading to more personalized work improvements and increased work efficiency.

[0145] "Information regarding operations" refers to the specific actions performed by the user on the application or system, and includes the type of operation, timing, and duration.

[0146] "Business procedures" define the sequence of tasks and procedures necessary for business operations, including the specific order and content of actions.

[0147] A "generation device" refers to a device that automatically assembles strategies and proposals for business improvement based on collected operational and emotional information.

[0148] "Emotional information" refers to data on emotional states and psychological responses obtained by analyzing the user's facial expressions, tone of voice, and other nonverbal cues.

[0149] "Means of notification" refers to methods and technologies for informing users of generated business improvement suggestions, and includes email, pop-up notifications, and dashboard displays.

[0150] "Information aggregation" refers to the process of combining and organizing data obtained from multiple different data sources into a single, unified dataset.

[0151] "Visualization" refers to the process of converting abstract data or complex information into a visual format to make it easier to understand.

[0152] This invention is a system for collecting information on user actions and emotions to improve business operations. This system is implemented as follows:

[0153] The server receives information about the user's actions from their terminal and securely stores it in a database. This information includes details about which applications the user interacted with and how. Sentimental information obtained through the sentiment engine is also stored. The server analyzes the collected data and generates business procedures. Machine learning algorithms are used for the analysis to identify areas in the business procedures where efficiency can be improved. The business procedures are visualized based on BPMN, visually highlighting key areas for improvement.

[0154] The terminal monitors user actions in real time and records data. Furthermore, an emotion engine built into the terminal analyzes the user's facial expressions and voice to determine their emotions, and transmits this data to a server. This allows changes in the user's emotions to be incorporated as a crucial element for improving work processes.

[0155] Users receive notifications from the server regarding suggestions for improving their work processes and review the suggestions. These suggestions incorporate user emotional feedback and are designed to reduce stress and improve work efficiency.

[0156] For example, if a specific work procedure that frequently frustrates users is identified, automation of that procedure is proposed. This process improves overall work productivity and enhances the user experience.

[0157] As an example of a prompt, providing the AI ​​model with input such as, "How should user sentiment data be utilized in generating business improvement proposals?" can be expected to result in more accurate and personalized improvement proposals.

[0158] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0159] Step 1:

[0160] The device records the user's actions on the computer in real time. Specifically, it collects information such as the name of the application the user is using, the time the action started, and the content of the action. This action data is output as a detailed log of the user's usage and input into the emotion engine.

[0161] Step 2:

[0162] The emotion engine built into the device analyzes the user's facial expressions and voice to determine their emotional state. Inputs include real-time video and audio captured from the camera and microphone. Based on this, facial recognition and voice analysis technologies are used to quantify the user's emotions. The output emotion data, along with operation data, is sent to the server as information indicating the user's stress level and degree of pleasure or displeasure.

[0163] Step 3:

[0164] The server receives operation data and sentiment data sent from the terminal. The input for this step is the detailed operation log and sentiment data sent from the terminal. The server stores this data in a database and outputs it as basic information for later analysis.

[0165] Step 4:

[0166] The server analyzes collected operational and sentiment data to generate business procedures. Inputs include operational logs and sentiment information stored in a database. This analysis utilizes machine learning algorithms to identify tasks within the business that are particularly susceptible to efficiency improvements. Outputs include a business flow template and a list of areas requiring improvement.

[0167] Step 5:

[0168] The server visualizes business procedures using BPMN based on the analysis results. The input for this step is a business flow template and an improvement list, and the output is a visual business improvement suggestion for the user. The business flow is presented in an easy-to-understand format, with areas requiring improvement highlighted.

[0169] Step 6:

[0170] The server notifies the user of the generated business improvement suggestions. The input consists of a visualized business flow and improvement suggestions, which are then output and notified in a format suitable for the user's device. The user can then receive these suggestions and use them to review and streamline their business processes.

[0171] (Application Example 2)

[0172] 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".

[0173] In modern industrial settings, optimizing worker-machine interaction is essential for increasing production efficiency. However, stress and confusion during the production process contribute to decreased work efficiency, necessitating their identification and improvement. A challenge with existing methods is that they do not fully utilize operational history and emotional feedback.

[0174] 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.

[0175] In this invention, the server includes means for collecting operation records, means for analyzing the collected operation records to generate work processes, an emotion analysis device for analyzing the user's emotions, and means for optimizing work efficiency improvement proposals based on the user's emotion data. This makes it possible to identify stress factors in the production process and generate optimal improvement proposals to improve work efficiency.

[0176] "Operation records" refer to data collected from a user's history of a series of operations.

[0177] A "business process" refers to a series of business procedures or flows generated by analyzing operational records.

[0178] A "business efficiency improvement proposal" is a suggestion to improve efficiency based on business processes.

[0179] A "generation device" is a device that has the function of generating business processes and suggestions for improving business efficiency based on operation records.

[0180] An "emotion analysis device" is a device that has the function of analyzing a user's emotions.

[0181] "Information aggregation" refers to the process of accumulating analyzed operation records and emotional data.

[0182] "Visual representation" refers to a method of displaying generated business processes visually.

[0183] In implementing this invention, a system is primarily employed in which a server, a terminal, and a user work together as a unified entity. The server plays a central role, processing operation records and emotional data in an integrated manner. The terminal collects data in real time via wearable devices such as smart glasses. The user utilizes the resulting suggestions for improving work processes to contribute to increased productivity.

[0184] The server utilizes analysis libraries based on programming languages ​​such as Python and R to perform advanced data analysis, analyzing operation records and emotion data. During this process, emotions are extracted from image and audio data using OpenCV and Google® Cloud Speech-to-Text API. The analyzed data is stored in an information aggregation system, which then optimizes the production process.

[0185] As a concrete example, if an employee encounters difficulty with a new operating procedure while working in a factory, their feelings of confusion are captured through smart glasses and immediately analyzed on a server. Based on this analysis, suggestions for improving work efficiency are made, such as providing training videos to alleviate the employee's frustration or modifying the operating procedure.

[0186] By utilizing generative AI models, prompts like the following can be used: "Design a program to suggest improvements to work efficiency by integrating employee emotional data and operation records on a production line. The emotional data is to be obtained from smart glasses."

[0187] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0188] Step 1:

[0189] The device collects user operation records and emotion data in real time through smart glasses. Operation records include log data detailing the user's actions, while emotion data extracts feelings such as joy, anger, sadness, and happiness from facial recognition and voice analysis. This input data is bundled into packets and sent to a server via a communication module.

[0190] Step 2:

[0191] The server receives operation logs and sentiment data sent from the terminal. Upon receipt, it checks the data's integrity and standardizes the format for storage in the database. Specifically, it organizes the data based on timestamps and user IDs and associates operations with sentiment. This results in a consistent dataset necessary for analysis.

[0192] Step 3:

[0193] The server analyzes operation records based on accumulated data and extracts patterns in business processes. During this process, data mining algorithms are used to identify areas where problems frequently occur and areas where efficiency improvements are expected. The results of this analysis are output as business processes and used to generate efficiency improvements in the next step.

[0194] Step 4:

[0195] The server integrates patterns of work processes and emotional data, and uses a generative AI model to generate suggestions for improving work efficiency. The generative AI model prioritizes suggesting improvements for areas with significant emotional fluctuations or where specific operations cause stress. The output obtained here is then fed back to the user as suggestions.

[0196] Step 5:

[0197] Users receive notifications from the server regarding suggestions for improving work efficiency. These notifications are displayed on the terminal's screen or smart glasses screen. The suggestions may also include specific operating instructions and additional training materials, allowing users to adjust their work methods and improve their operations based on these suggestions.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] [Second Embodiment]

[0202] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0203] 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.

[0204] 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).

[0205] 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.

[0206] 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.

[0207] 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).

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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".

[0214] The present invention is a system that records in detail the user's operations on a PC, generates business processes from those records, and further generates business improvement proposals based on those processes. This system includes means for collecting operation data, means for analyzing data, a generation device, and means for notifying.

[0215] Server processing:

[0216] The server receives user operation data sent from the terminal and stores it in a database. This data includes application startup status, user input patterns, and details about window operations, which the server uses to prepare for analyzing operation trends and frequencies. This analysis can identify workflows and inefficient processes. For example, the server can quantify the operations a user performs when creating a daily report and show which steps take the most time.

[0217] Terminal processing:

[0218] The terminal records user actions on the PC in real time and sends the data to the server at regular intervals. For example, it meticulously records the click locations and input data when a user edits a spreadsheet. This operation recording is performed in the background and is designed not to interrupt the user's work.

[0219] User actions:

[0220] Based on notifications from their devices, users review business process improvement suggestions generated by the server. These notifications are provided as pop-ups or dashboard updates. Users can review the suggestions and decide whether to incorporate them into their work. Specifically, if automated work procedures are suggested, users can apply the scripts to reduce work time.

[0221] The system of this invention enables users to objectively review their own work flow and quickly improve inefficient areas. Through the cooperation of the server and terminals, the system automates the collection, analysis, and generation of suggestions for operational data, resulting in an overall improvement in work efficiency.

[0222] The following describes the processing flow.

[0223] Step 1:

[0224] The terminal records the user's PC operations in real time. This operation record includes application launches, window activations, keystrokes, and mouse click locations. This data is buffered at regular intervals.

[0225] Step 2:

[0226] The terminal periodically sends buffered operation data to the server. Before transmission, the data is properly formatted and includes user ID and timestamp information.

[0227] Step 3:

[0228] The server receives operation data from the terminal and stores it in a database. The data is organized by user and stored in a format suitable for analysis.

[0229] Step 4:

[0230] The server analyzes the accumulated data using analytical tools. Specifically, it executes algorithms to extract patterns in business workflows and inefficient operating procedures.

[0231] Step 5:

[0232] The server generates a business flow in a BPMN-based format based on the analysis results. This flow is formatted to be visually easy for the user to understand.

[0233] Step 6:

[0234] The server uses an AI model to create business improvement proposals based on the generated business flow. These proposals include reducing redundant steps and suggesting processes that can be automated.

[0235] Step 7:

[0236] The server sends the completed business improvement proposal to the terminal. The submitted improvement proposal is then presented to the user via notifications or on the dashboard.

[0237] Step 8:

[0238] Users check notifications from their devices and carefully review the proposed work improvement suggestions. If necessary, they agree to the suggestions and incorporate them into their work to improve efficiency.

[0239] (Example 1)

[0240] 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."

[0241] When aiming to improve business efficiency, conventional systems faced the challenge of not being able to effectively collect and analyze user operation data and quickly and appropriately notify users of improvement suggestions based on the results. As a result, optimizing business processes took a long time, and consequently, the full potential of the improvements could not be realized.

[0242] 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.

[0243] In this invention, the server includes means for recording user operations, means for analyzing the recorded data to identify business procedures, and means for generating and notifying efficiency improvement suggestions based on the analysis results. This makes it possible to precisely analyze the user's workflow and immediately propose business improvement plans.

[0244] "Operational actions" refer to a series of actions performed by a user on a computer, such as inputting data, clicking, and operating applications.

[0245] "Means of recording" refers to methods and devices for recording user actions as digital data.

[0246] "Means of analysis" refers to algorithms and software used to analyze recorded operational data.

[0247] "Functional procedures" refer to specific steps and procedures that indicate the flow of business processes and tasks, as identified through analysis.

[0248] "Efficiency improvement proposals" refer to improvement plans and recommendations for performing tasks more efficiently based on identified functional procedures.

[0249] A "generation device" refers to hardware and software used to generate efficiency improvement suggestions from data analysis results.

[0250] "Means of notification" refers to methods or devices for communicating generated efficiency suggestions to users.

[0251] "Data repository creation" refers to the process of organizing analyzed data and storing it in a database format.

[0252] "Visual means" refers to methods and devices that display data and information to users in a graphical format to aid their understanding.

[0253] This system aims to improve work efficiency by recording and analyzing user actions. Specifically, terminals use dedicated software to sequentially record user actions and periodically send this data to a server. This software includes functions for tracking operations and generating logs. The server stores the received data in a dedicated database and uses analysis algorithms to identify the workflow. Statistical software and machine learning models are used for data analysis to clarify factors hindering work efficiency. Based on the generated functional procedures, the server creates efficiency improvement suggestions. These suggestions are generated using a generative AI model and present specific improvement plans tailored to the user's characteristics.

[0254] Efficiency suggestions are delivered to the user's device as pop-up notifications or dashboard updates, which the user then reviews. The user can then apply the suggested improvements to their own work processes. For example, in tasks requiring frequent data entry, a script to automate the data entry process might be suggested. This can significantly reduce the time required for the task.

[0255] The following are specific examples of prompt statements.

[0256] "Please generate suggestions for improving work efficiency."

[0257] "Please provide a way to automate the data entry process."

[0258] Therefore, the purpose of this system is to appropriately monitor and analyze the user's work and provide concrete and effective suggestions for efficiency improvements.

[0259] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0260] Step 1:

[0261] The device records user actions in real time. Specifically, it captures data such as mouse click locations, keyboard input, and application launches and shutdowns. This is the input, and the recorded series of actions becomes the output. This process runs in the background and is designed not to interrupt the user's work.

[0262] Step 2:

[0263] The terminal sends operation logs recorded at regular intervals to the server. The transmitted data is the input, and the storage of that data in the server's database is the output. The terminal verifies the integrity and accuracy of the data and performs error checking during the transmission process.

[0264] Step 3:

[0265] The server analyzes the received operation log data. The input is the operation logs stored in the database, and the output is the identification of the analyzed business procedures. Specifically, it uses an analysis algorithm to identify frequently occurring operations and time-consuming steps, and quantifies the business flow.

[0266] Step 4:

[0267] The server uses a generative AI model to create efficiency suggestions based on the analysis results. The analysis results are input into the generative AI model, and the output is a user-optimized suggestion. This process generates automatable procedures and hints for work improvement.

[0268] Step 5:

[0269] The server sends the generated efficiency suggestions to the terminal. The user receives notifications on the terminal via pop-ups or dashboards. The output is that the suggestions are entered and the user recognizes the improvement suggestions. Specifically, the user reviews the suggestions and decides whether to apply them to their own workflow.

[0270] (Application Example 1)

[0271] 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."

[0272] In modern factory production lines, it is essential to effectively collect and analyze operation logs from various machines to optimize efficient production processes. However, conventional systems have limitations in automatically generating and notifying improvement suggestions based on the effective collection and analysis of operation and motion data. This has resulted in insufficient factory efficiency and wasted time and resources.

[0273] 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.

[0274] In this invention, the server includes means for collecting operation data, means for analyzing the collected operation data to generate business processes, and an information processing device for generating business improvement proposals based on the generated business processes. This makes it possible to efficiently collect and analyze operation data of factory machinery and robots, and to automatically generate and propose optimal operation procedures.

[0275] "Means for collecting operation data" refers to a mechanism or process for collecting information about operations performed by a user or device.

[0276] "Means for generating business processes" refers to devices or algorithms for analyzing collected data, extracting business flows and procedures, and visualizing them.

[0277] An "information processing device" is a computing device or system for processing data and generating new information based on that data.

[0278] A "knowledge generation device" is a device that executes algorithms or programs to propose efficient procedures and actions based on collected motion data.

[0279] A "visual presentation device" is a device or interface that displays information to a user in text, graphics, or other forms to aid in understanding.

[0280] An "information management device" is a system for organizing, storing, and providing data in a format that is accessible as needed.

[0281] This system collects and analyzes the operation data of various robots and machines in real time to improve production efficiency in the factory and generates optimization plans. First, the operation data collection means installed on the terminal collects detailed data on the operation of each robot. The collected data includes the start and stop timings of work, movement patterns, and energy consumption, etc.

[0282] The collected data is directly sent to the server. The server uses an information processing device to analyze the operation data. Python and its data analysis libraries, specifically Pandas and numpy, are used for this analysis. A business process is generated from the information obtained through data analysis to identify inefficient processes and bottlenecks. Furthermore, this information is processed by a knowledge generation device, and an optimal operation procedure is proposed. Machine learning libraries such as TensorFlow are adopted for business process generation.

[0283] The proposed business improvement plan is displayed on the dashboard using a visual presentation device. As a result, the user can check the new operation procedure and decide whether to execute it. For example, a proposal is made to shorten the flow line when transferring a specific part, and it is assumed that the transfer time can be reduced by about 20%.

[0284] Furthermore, this system utilizes a generative AI model. By inputting, for example, "Please propose an optimization plan to improve transfer efficiency based on the latest transfer data." as an example of a prompt sentence, a more accurate proposal becomes possible. In this way, the system as a whole can objectively and efficiently improve the operations in the factory.

[0285] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0286] Step 1:

[0287] The terminal collects operational data from factory robots in real time. This data includes the start and stop timings, movement paths, and energy consumption of each robot. The input is robot sensor information, and the output is operation log data compiled from this information. The collected data is stored in a buffer and periodically sent to the server.

[0288] Step 2:

[0289] The server receives operation log data sent from the terminal and stores it in the database. The input is the operation log data received from the terminal, and the output is structured information recorded in the database. A database management system is used to organize this data.

[0290] Step 3:

[0291] The server analyzes the received data using a data analysis engine. The input is robot operation data stored in a database. Using Python and data analysis libraries such as Pandas and NumPy, trends and patterns are extracted from the data, and a report indicating process bottlenecks is generated as output.

[0292] Step 4:

[0293] The server uses the generated AI model based on the analysis results to produce business improvement proposals. An example of a prompt message provided to the AI ​​is, "Please suggest the optimal operating procedure for a specific transport task." The input is the analysis results obtained earlier, and the output is the recommended operating procedure or improvement proposal.

[0294] Step 5:

[0295] The user receives business improvement suggestions generated from the server through a visual display device. The input is improvement suggestion data from the server, and the output is visualized improvement suggestions displayed on a dashboard. The user reviews these and decides whether to implement them.

[0296] 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.

[0297] This invention relates to a system that generates business processes by meticulously recording user operation data on a PC and analyzing that data. By combining this system with an emotion engine to recognize user emotions, the system considers the user's reaction to automatically generated business improvement proposals and provides more personalized improvement suggestions.

[0298] Server processing:

[0299] The server receives operation data sent from the terminal and stores it in a database. This collected data includes not only the operation history of the applications used by the user, but also sentiment data generated by the sentiment engine. The server analyzes this data to generate a business flow. The generated business flow allows for the extraction of elements that contribute to business improvement through visualization based on BPMN and comparison with sentiment data. For example, if the server finds that a user tends to show frustration at a particular step, it will identify that part of the work as a target for improvement.

[0300] Terminal processing:

[0301] The terminal not only records the user's PC operations in real time, but also uses an emotion engine to analyze the user's emotions from their facial expressions and voice. The analyzed emotion data is sent to the server along with the operation log. For example, if a user is confused when performing a difficult operation, that emotion is captured as data and taken into consideration in subsequent analysis.

[0302] User actions:

[0303] The user receives the business improvement plan analyzed and generated by the server and accesses it through the notification. This improvement plan is adjusted based on the user's emotional feedback and is designed to reduce the user's stress. For example, tasks that frequently cause frustration are proposed to be automated.

[0304] According to the present invention, a deep insight for business efficiency improvement is obtained from both the user's operation behavior and emotional data, and a system is provided that offers practical and highly satisfactory improvement plans for the user. The system not only improves the quality of the user's work but also greatly contributes to the improvement of the user experience.

[0305] The following describes the processing flow.

[0306] Step 1:

[0307] The terminal records the user's PC operations in real time and uses an emotion engine to detect emotions from the user's expressions and voices. The emotional data includes emotions such as joy, confusion, and frustration.

[0308] Step 2:

[0309] The terminal buffers the collected operation data and emotional data at regular intervals and formats them as a single data set.

[0310] Step 3:

[0311] The terminal sends the formatted data set to the server. This data set also includes a timestamp and user profile information to maintain data consistency.

[0312] Step 4:

[0313] The server accumulates the received data set in the database. The data is sorted for each user and stored in a format suitable for analysis.

[0314] Step 5:

[0315] The server processes the accumulated data using analysis tools. Specifically, it generates business flows and visualizes user sentiment trends based on operation patterns and sentiment data.

[0316] Step 6:

[0317] The server generates business improvement proposals based on the analysis results. Here, the improvement suggestions focus particularly on steps where users exhibit strong emotions (e.g., frustration or confusion).

[0318] Step 7:

[0319] The server sends the generated business improvement proposals to the terminal. The improvement proposals are appropriately visualized and provided in a format that is easy for the user to understand.

[0320] Step 8:

[0321] Users can review the suggested improvements notified via their devices, evaluate their content, and incorporate them into their own work. Suggestions that are particularly considerate of users' emotions make them more likely to accept the improvements.

[0322] (Example 2)

[0323] 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".

[0324] In today's work environment, improving work procedures is essential for efficient and stress-free work execution. However, conventional systems analyze only information about user actions to improve processes, making it difficult to provide personalized improvement suggestions that take into account user emotions and reactions. Furthermore, many process improvement proposals tend to overlook some key points, limiting the overall efficiency of operations.

[0325] 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.

[0326] In this invention, the server includes means for collecting information about operations, means for analyzing the collected information about operations to generate work procedures, and means for analyzing the user's emotions and incorporating the feedback into suggestions for improving operations. This enables the dynamic improvement of work procedures in response to the user's emotional reactions, leading to more personalized work improvements and increased work efficiency.

[0327] "Information regarding operations" refers to the specific actions performed by the user on the application or system, and includes the type of operation, timing, and duration.

[0328] "Business procedures" define the sequence of tasks and procedures necessary for business operations, including the specific order and content of actions.

[0329] A "generation device" refers to a device that automatically assembles strategies and proposals for business improvement based on collected operational and emotional information.

[0330] "Emotional information" refers to data on emotional states and psychological responses obtained by analyzing the user's facial expressions, tone of voice, and other nonverbal cues.

[0331] "Means of notification" refers to methods and technologies for informing users of generated business improvement suggestions, and includes email, pop-up notifications, and dashboard displays.

[0332] "Information aggregation" refers to the process of combining and organizing data obtained from multiple different data sources into a single, unified dataset.

[0333] "Visualization" refers to the process of converting abstract data or complex information into a visual format to make it easier to understand.

[0334] This invention is a system for collecting information on user actions and emotions to improve business operations. This system is implemented as follows:

[0335] The server receives information about the user's actions from their terminal and securely stores it in a database. This information includes details about which applications the user interacted with and how. Sentimental information obtained through the sentiment engine is also stored. The server analyzes the collected data and generates business procedures. Machine learning algorithms are used for the analysis to identify areas in the business procedures where efficiency can be improved. The business procedures are visualized based on BPMN, visually highlighting key areas for improvement.

[0336] The terminal monitors user actions in real time and records data. Furthermore, an emotion engine built into the terminal analyzes the user's facial expressions and voice to determine their emotions, and transmits this data to a server. This allows changes in the user's emotions to be incorporated as a crucial element for improving work processes.

[0337] Users receive notifications from the server regarding suggestions for improving their work processes and review the suggestions. These suggestions incorporate user emotional feedback and are designed to reduce stress and improve work efficiency.

[0338] For example, if a specific work procedure that frequently frustrates users is identified, automation of that procedure is proposed. This process improves overall work productivity and enhances the user experience.

[0339] As an example of a prompt, providing the AI ​​model with input such as, "How should user sentiment data be utilized in generating business improvement proposals?" can be expected to result in more accurate and personalized improvement proposals.

[0340] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0341] Step 1:

[0342] The device records the user's actions on the computer in real time. Specifically, it collects information such as the name of the application the user is using, the time the action started, and the content of the action. This action data is output as a detailed log of the user's usage and input into the emotion engine.

[0343] Step 2:

[0344] The emotion engine built into the device analyzes the user's facial expressions and voice to determine their emotional state. Inputs include real-time video and audio captured from the camera and microphone. Based on this, facial recognition and voice analysis technologies are used to quantify the user's emotions. The output emotion data, along with operation data, is sent to the server as information indicating the user's stress level and degree of pleasure or displeasure.

[0345] Step 3:

[0346] The server receives operation data and sentiment data sent from the terminal. The input for this step is the detailed operation log and sentiment data sent from the terminal. The server stores this data in a database and outputs it as basic information for later analysis.

[0347] Step 4:

[0348] The server analyzes collected operational and sentiment data to generate business procedures. Inputs include operational logs and sentiment information stored in a database. This analysis utilizes machine learning algorithms to identify tasks within the business that are particularly susceptible to efficiency improvements. Outputs include a business flow template and a list of areas requiring improvement.

[0349] Step 5:

[0350] The server visualizes business procedures using BPMN based on the analysis results. The input for this step is a business flow template and an improvement list, and the output is a visual business improvement suggestion for the user. The business flow is presented in an easy-to-understand format, with areas requiring improvement highlighted.

[0351] Step 6:

[0352] The server notifies the user of the generated business improvement suggestions. The input consists of a visualized business flow and improvement suggestions, which are then output and notified in a format suitable for the user's device. The user can then receive these suggestions and use them to review and streamline their business processes.

[0353] (Application Example 2)

[0354] 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 will be referred to as the "terminal."

[0355] In modern industrial settings, optimizing worker-machine interaction is essential for increasing production efficiency. However, stress and confusion during the production process contribute to decreased work efficiency, necessitating their identification and improvement. A challenge with existing methods is that they do not fully utilize operational history and emotional feedback.

[0356] 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.

[0357] In this invention, the server includes means for collecting operation records, means for analyzing the collected operation records to generate work processes, an emotion analysis device for analyzing the user's emotions, and means for optimizing work efficiency improvement proposals based on the user's emotion data. This makes it possible to identify stress factors in the production process and generate optimal improvement proposals to improve work efficiency.

[0358] "Operation records" refer to data collected from a user's history of a series of operations.

[0359] A "business process" refers to a series of business procedures or flows generated by analyzing operational records.

[0360] A "business efficiency improvement proposal" is a suggestion to improve efficiency based on business processes.

[0361] A "generation device" is a device that has the function of generating business processes and suggestions for improving business efficiency based on operation records.

[0362] An "emotion analysis device" is a device that has the function of analyzing a user's emotions.

[0363] "Information aggregation" refers to the process of accumulating analyzed operation records and emotional data.

[0364] "Visual representation" refers to a method of displaying generated business processes visually.

[0365] In implementing this invention, a system is primarily employed in which a server, a terminal, and a user work together as a unified entity. The server plays a central role, processing operation records and emotional data in an integrated manner. The terminal collects data in real time via wearable devices such as smart glasses. The user utilizes the resulting suggestions for improving work processes to contribute to increased productivity.

[0366] The server utilizes analysis libraries based on programming languages ​​such as Python and R to perform advanced data analysis, analyzing operation records and emotion data. During this process, emotions are extracted from image and audio data using OpenCV and the Google Cloud Speech-to-Text API. The analyzed data is stored in an information aggregation system, which then optimizes the production process.

[0367] As a concrete example, if an employee encounters difficulty with a new operating procedure while working in a factory, their feelings of confusion are captured through smart glasses and immediately analyzed on a server. Based on this analysis, suggestions for improving work efficiency are made, such as providing training videos to alleviate the employee's frustration or modifying the operating procedure.

[0368] By utilizing generative AI models, prompts like the following can be used: "Design a program to suggest improvements to work efficiency by integrating employee emotional data and operation records on a production line. The emotional data is to be obtained from smart glasses."

[0369] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0370] Step 1:

[0371] The device collects user operation records and emotion data in real time through smart glasses. Operation records include log data detailing the user's actions, while emotion data extracts feelings such as joy, anger, sadness, and happiness from facial recognition and voice analysis. This input data is bundled into packets and sent to a server via a communication module.

[0372] Step 2:

[0373] The server receives operation logs and sentiment data sent from the terminal. Upon receipt, it checks the data's integrity and standardizes the format for storage in the database. Specifically, it organizes the data based on timestamps and user IDs and associates operations with sentiment. This results in a consistent dataset necessary for analysis.

[0374] Step 3:

[0375] The server analyzes operation records based on accumulated data and extracts patterns in business processes. During this process, data mining algorithms are used to identify areas where problems frequently occur and areas where efficiency improvements are expected. The results of this analysis are output as business processes and used to generate efficiency improvements in the next step.

[0376] Step 4:

[0377] The server integrates patterns of work processes and emotional data, and uses a generative AI model to generate suggestions for improving work efficiency. The generative AI model prioritizes suggesting improvements for areas with significant emotional fluctuations or where specific operations cause stress. The output obtained here is then fed back to the user as suggestions.

[0378] Step 5:

[0379] Users receive notifications from the server regarding suggestions for improving work efficiency. These notifications are displayed on the terminal's screen or smart glasses screen. The suggestions may also include specific operating instructions and additional training materials, allowing users to adjust their work methods and improve their operations based on these suggestions.

[0380] 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.

[0381] 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.

[0382] 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.

[0383] [Third Embodiment]

[0384] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0385] 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.

[0386] 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).

[0387] 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.

[0388] 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.

[0389] 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).

[0390] 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.

[0391] 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.

[0392] 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.

[0393] 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.

[0394] 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.

[0395] 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".

[0396] The present invention is a system that records in detail the user's operations on a PC, generates business processes from those records, and further generates business improvement proposals based on those processes. This system includes means for collecting operation data, means for analyzing data, a generation device, and means for notifying.

[0397] Server processing:

[0398] The server receives user operation data sent from the terminal and stores it in a database. This data includes application startup status, user input patterns, and details about window operations, which the server uses to prepare for analyzing operation trends and frequencies. This analysis can identify workflows and inefficient processes. For example, the server can quantify the operations a user performs when creating a daily report and show which steps take the most time.

[0399] Terminal processing:

[0400] The terminal records user actions on the PC in real time and sends the data to the server at regular intervals. For example, it meticulously records the click locations and input data when a user edits a spreadsheet. This operation recording is performed in the background and is designed not to interrupt the user's work.

[0401] User actions:

[0402] Based on notifications from their devices, users review business process improvement suggestions generated by the server. These notifications are provided as pop-ups or dashboard updates. Users can review the suggestions and decide whether to incorporate them into their work. Specifically, if automated work procedures are suggested, users can apply the scripts to reduce work time.

[0403] The system of this invention enables users to objectively review their own work flow and quickly improve inefficient areas. Through the cooperation of the server and terminals, the system automates the collection, analysis, and generation of suggestions for operational data, resulting in an overall improvement in work efficiency.

[0404] The following describes the processing flow.

[0405] Step 1:

[0406] The terminal records the user's PC operations in real time. This operation record includes application launches, window activations, keystrokes, and mouse click locations. This data is buffered at regular intervals.

[0407] Step 2:

[0408] The terminal periodically sends buffered operation data to the server. Before transmission, the data is properly formatted and includes user ID and timestamp information.

[0409] Step 3:

[0410] The server receives operation data from the terminal and stores it in a database. The data is organized by user and stored in a format suitable for analysis.

[0411] Step 4:

[0412] The server analyzes the accumulated data using analytical tools. Specifically, it executes algorithms to extract patterns in business workflows and inefficient operating procedures.

[0413] Step 5:

[0414] The server generates a business flow in a BPMN-based format based on the analysis results. This flow is formatted to be visually easy for the user to understand.

[0415] Step 6:

[0416] The server uses an AI model to create business improvement proposals based on the generated business flow. These proposals include reducing redundant steps and suggesting processes that can be automated.

[0417] Step 7:

[0418] The server sends the completed business improvement proposal to the terminal. The submitted improvement proposal is then presented to the user via notifications or on the dashboard.

[0419] Step 8:

[0420] Users check notifications from their devices and carefully review the proposed work improvement suggestions. If necessary, they agree to the suggestions and incorporate them into their work to improve efficiency.

[0421] (Example 1)

[0422] 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."

[0423] When aiming to improve business efficiency, conventional systems faced the challenge of not being able to effectively collect and analyze user operation data and quickly and appropriately notify users of improvement suggestions based on the results. As a result, optimizing business processes took a long time, and consequently, the full potential of the improvements could not be realized.

[0424] 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.

[0425] In this invention, the server includes means for recording user operations, means for analyzing the recorded data to identify business procedures, and means for generating and notifying efficiency improvement suggestions based on the analysis results. This makes it possible to precisely analyze the user's workflow and immediately propose business improvement plans.

[0426] "Operational actions" refer to a series of actions performed by a user on a computer, such as inputting data, clicking, and operating applications.

[0427] "Means of recording" refers to methods and devices for recording user actions as digital data.

[0428] "Means of analysis" refers to algorithms and software used to analyze recorded operational data.

[0429] "Functional procedures" refer to specific steps and procedures that indicate the flow of business processes and tasks, as identified through analysis.

[0430] "Efficiency improvement proposals" refer to improvement plans and recommendations for performing tasks more efficiently based on identified functional procedures.

[0431] A "generation device" refers to hardware and software used to generate efficiency improvement suggestions from data analysis results.

[0432] "Means of notification" refers to methods or devices for communicating generated efficiency suggestions to users.

[0433] "Data repository creation" refers to the process of organizing analyzed data and storing it in a database format.

[0434] "Visual means" refers to methods and devices that display data and information to users in a graphical format to aid their understanding.

[0435] This system aims to improve work efficiency by recording and analyzing user actions. Specifically, terminals use dedicated software to sequentially record user actions and periodically send this data to a server. This software includes functions for tracking operations and generating logs. The server stores the received data in a dedicated database and uses analysis algorithms to identify the workflow. Statistical software and machine learning models are used for data analysis to clarify factors hindering work efficiency. Based on the generated functional procedures, the server creates efficiency improvement suggestions. These suggestions are generated using a generative AI model and present specific improvement plans tailored to the user's characteristics.

[0436] Efficiency suggestions are delivered to the user's device as pop-up notifications or dashboard updates, which the user then reviews. The user can then apply the suggested improvements to their own work processes. For example, in tasks requiring frequent data entry, a script to automate the data entry process might be suggested. This can significantly reduce the time required for the task.

[0437] The following are specific examples of prompt statements.

[0438] "Please generate suggestions for improving work efficiency."

[0439] "Please provide a way to automate the data entry process."

[0440] Therefore, the purpose of this system is to appropriately monitor and analyze the user's work and provide concrete and effective suggestions for efficiency improvements.

[0441] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0442] Step 1:

[0443] The device records user actions in real time. Specifically, it captures data such as mouse click locations, keyboard input, and application launches and shutdowns. This is the input, and the recorded series of actions becomes the output. This process runs in the background and is designed not to interrupt the user's work.

[0444] Step 2:

[0445] The terminal sends operation logs recorded at regular intervals to the server. The transmitted data is the input, and the storage of that data in the server's database is the output. The terminal verifies the integrity and accuracy of the data and performs error checking during the transmission process.

[0446] Step 3:

[0447] The server analyzes the received operation log data. The input is the operation logs stored in the database, and the output is the identification of the analyzed business procedures. Specifically, it uses an analysis algorithm to identify frequently occurring operations and time-consuming steps, and quantifies the business flow.

[0448] Step 4:

[0449] The server uses a generative AI model to create efficiency suggestions based on the analysis results. The analysis results are input into the generative AI model, and the output is a user-optimized suggestion. This process generates automatable procedures and hints for work improvement.

[0450] Step 5:

[0451] The server sends the generated efficiency suggestions to the terminal. The user receives notifications on the terminal via pop-ups or dashboards. The output is that the suggestions are entered and the user recognizes the improvement suggestions. Specifically, the user reviews the suggestions and decides whether to apply them to their own workflow.

[0452] (Application Example 1)

[0453] 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."

[0454] In modern factory production lines, it is essential to effectively collect and analyze operation logs from various machines to optimize efficient production processes. However, conventional systems have limitations in automatically generating and notifying improvement suggestions based on the effective collection and analysis of operation and motion data. This has resulted in insufficient factory efficiency and wasted time and resources.

[0455] 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.

[0456] In this invention, the server includes means for collecting operation data, means for analyzing the collected operation data to generate business processes, and an information processing device for generating business improvement proposals based on the generated business processes. This makes it possible to efficiently collect and analyze operation data of factory machinery and robots, and to automatically generate and propose optimal operation procedures.

[0457] "Means for collecting operation data" refers to a mechanism or process for collecting information about operations performed by a user or device.

[0458] "Means for generating business processes" refers to devices or algorithms for analyzing collected data, extracting business flows and procedures, and visualizing them.

[0459] An "information processing device" is a computing device or system for processing data and generating new information based on that data.

[0460] A "knowledge generation device" is a device that executes algorithms or programs to propose efficient procedures and actions based on collected motion data.

[0461] A "visual presentation device" is a device or interface that displays information to a user in text, graphics, or other forms to aid in understanding.

[0462] An "information management device" is a system for organizing, storing, and providing data in a format that is accessible as needed.

[0463] This system collects and analyzes operational data from various robots and machines in real time to improve production efficiency within the factory and generates optimization proposals. First, an operational data collection device installed in the terminal collects detailed data on the operation of each robot. This collected data includes the start and stop timing of work, movement patterns, and energy consumption.

[0464] The collected data is sent directly to the server. The server uses an information processing device to analyze the operational data. Python and its data analysis libraries, specifically Pandas and NumPy, are used for this analysis. From the information obtained through data analysis, business processes are generated, and inefficient processes and bottlenecks are identified. Furthermore, this information is processed by a knowledge generation device, and the optimal operating procedure is proposed. Machine learning libraries such as TensorFlow are used for business process generation.

[0465] The proposed business improvement plans are displayed on a dashboard using a visual display device. This allows users to review the new work procedures and decide whether or not to implement them. For example, a proposal might be made to shorten the route taken when transporting a specific part, which is expected to reduce travel time by approximately 20%.

[0466] Furthermore, by utilizing a generative AI model and taking input such as "Please propose an optimization plan to improve transport efficiency based on the latest transport data" as an example of a prompt, the system can provide more accurate suggestions. In this way, the entire system can objectively and efficiently improve operations within the factory.

[0467] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0468] Step 1:

[0469] The terminal collects operational data from factory robots in real time. This data includes the start and stop timings, movement paths, and energy consumption of each robot. The input is robot sensor information, and the output is operation log data compiled from this information. The collected data is stored in a buffer and periodically sent to the server.

[0470] Step 2:

[0471] The server receives operation log data sent from the terminal and stores it in the database. The input is the operation log data received from the terminal, and the output is structured information recorded in the database. A database management system is used to organize this data.

[0472] Step 3:

[0473] The server analyzes the received data using a data analysis engine. The input is robot operation data stored in a database. Using Python and data analysis libraries such as Pandas and NumPy, trends and patterns are extracted from the data, and a report indicating process bottlenecks is generated as output.

[0474] Step 4:

[0475] The server uses the generated AI model based on the analysis results to produce business improvement proposals. An example of a prompt message provided to the AI ​​is, "Please suggest the optimal operating procedure for a specific transport task." The input is the analysis results obtained earlier, and the output is the recommended operating procedure or improvement proposal.

[0476] Step 5:

[0477] The user receives business improvement suggestions generated from the server through a visual display device. The input is improvement suggestion data from the server, and the output is visualized improvement suggestions displayed on a dashboard. The user reviews these and decides whether to implement them.

[0478] 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.

[0479] This invention relates to a system that generates business processes by meticulously recording user operation data on a PC and analyzing that data. By combining this system with an emotion engine to recognize user emotions, the system considers the user's reaction to automatically generated business improvement proposals and provides more personalized improvement suggestions.

[0480] Server processing:

[0481] The server receives operation data sent from the terminal and stores it in a database. This collected data includes not only the operation history of the applications used by the user, but also sentiment data generated by the sentiment engine. The server analyzes this data to generate a business flow. The generated business flow allows for the extraction of elements that contribute to business improvement through visualization based on BPMN and comparison with sentiment data. For example, if the server finds that a user tends to show frustration at a particular step, it will identify that part of the work as a target for improvement.

[0482] Terminal processing:

[0483] The terminal not only records the user's PC operations in real time, but also uses an emotion engine to analyze the user's emotions from their facial expressions and voice. The analyzed emotion data is sent to the server along with the operation log. For example, if a user is confused when performing a difficult operation, that emotion is captured as data and taken into consideration in subsequent analysis.

[0484] User actions:

[0485] Users receive business improvement suggestions generated by the server and access them through notifications. These suggestions are refined based on user emotional feedback and are designed to reduce user stress. For example, tasks that frequently cause frustration are suggested to be automated.

[0486] This invention provides a system that offers deep insights for improving work efficiency from both user operation behavior and emotional data, and provides users with practical and highly satisfying improvement suggestions. The system not only improves the quality of user work but also significantly contributes to enhancing the user experience.

[0487] The following describes the processing flow.

[0488] Step 1:

[0489] The device records the user's PC operations in real time and uses an emotion engine to detect emotions from the user's facial expressions and voice. Emotional data includes emotions such as joy, confusion, and frustration.

[0490] Step 2:

[0491] The device buffers the collected operational and emotional data at regular intervals and formats them into a single dataset.

[0492] Step 3:

[0493] The terminal sends a formatted dataset to the server. This dataset includes timestamps and user profile information to ensure data consistency.

[0494] Step 4:

[0495] The server stores the received datasets in a database. The data is organized separately for each user and stored in a format suitable for analysis.

[0496] Step 5:

[0497] The server processes the accumulated data using analysis tools. Specifically, it generates business flows and visualizes user sentiment trends based on operation patterns and sentiment data.

[0498] Step 6:

[0499] The server generates business improvement proposals based on the analysis results. Here, the improvement suggestions focus particularly on steps where users exhibit strong emotions (e.g., frustration or confusion).

[0500] Step 7:

[0501] The server sends the generated business improvement proposals to the terminal. The improvement proposals are appropriately visualized and provided in a format that is easy for the user to understand.

[0502] Step 8:

[0503] Users can review the suggested improvements notified via their devices, evaluate their content, and incorporate them into their own work. Suggestions that are particularly considerate of users' emotions make them more likely to accept the improvements.

[0504] (Example 2)

[0505] 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."

[0506] In today's work environment, improving work procedures is essential for efficient and stress-free work execution. However, conventional systems analyze only information about user actions to improve processes, making it difficult to provide personalized improvement suggestions that take into account user emotions and reactions. Furthermore, many process improvement proposals tend to overlook some key points, limiting the overall efficiency of operations.

[0507] 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.

[0508] In this invention, the server includes means for collecting information about operations, means for analyzing the collected information about operations to generate work procedures, and means for analyzing the user's emotions and incorporating the feedback into suggestions for improving operations. This enables the dynamic improvement of work procedures in response to the user's emotional reactions, leading to more personalized work improvements and increased work efficiency.

[0509] "Information regarding operations" refers to the specific actions performed by the user on the application or system, and includes the type of operation, timing, and duration.

[0510] "Business procedures" define the sequence of tasks and procedures necessary for business operations, including the specific order and content of actions.

[0511] A "generation device" refers to a device that automatically assembles strategies and proposals for business improvement based on collected operational and emotional information.

[0512] "Emotional information" refers to data on emotional states and psychological responses obtained by analyzing the user's facial expressions, tone of voice, and other nonverbal cues.

[0513] "Means of notification" refers to methods and technologies for informing users of generated business improvement suggestions, and includes email, pop-up notifications, and dashboard displays.

[0514] "Information aggregation" refers to the process of combining and organizing data obtained from multiple different data sources into a single, unified dataset.

[0515] "Visualization" refers to the process of converting abstract data or complex information into a visual format to make it easier to understand.

[0516] This invention is a system for collecting information on user actions and emotions to improve business operations. This system is implemented as follows:

[0517] The server receives information about the user's actions from their terminal and securely stores it in a database. This information includes details about which applications the user interacted with and how. Sentimental information obtained through the sentiment engine is also stored. The server analyzes the collected data and generates business procedures. Machine learning algorithms are used for the analysis to identify areas in the business procedures where efficiency can be improved. The business procedures are visualized based on BPMN, visually highlighting key areas for improvement.

[0518] The terminal monitors user actions in real time and records data. Furthermore, an emotion engine built into the terminal analyzes the user's facial expressions and voice to determine their emotions, and transmits this data to a server. This allows changes in the user's emotions to be incorporated as a crucial element for improving work processes.

[0519] Users receive notifications from the server regarding suggestions for improving their work processes and review the suggestions. These suggestions incorporate user emotional feedback and are designed to reduce stress and improve work efficiency.

[0520] For example, if a specific work procedure that frequently frustrates users is identified, automation of that procedure is proposed. This process improves overall work productivity and enhances the user experience.

[0521] As an example of a prompt, providing the AI ​​model with input such as, "How should user sentiment data be utilized in generating business improvement proposals?" can be expected to result in more accurate and personalized improvement proposals.

[0522] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0523] Step 1:

[0524] The device records the user's actions on the computer in real time. Specifically, it collects information such as the name of the application the user is using, the time the action started, and the content of the action. This action data is output as a detailed log of the user's usage and input into the emotion engine.

[0525] Step 2:

[0526] The emotion engine built into the device analyzes the user's facial expressions and voice to determine their emotional state. Inputs include real-time video and audio captured from the camera and microphone. Based on this, facial recognition and voice analysis technologies are used to quantify the user's emotions. The output emotion data, along with operation data, is sent to the server as information indicating the user's stress level and degree of pleasure or displeasure.

[0527] Step 3:

[0528] The server receives operation data and sentiment data sent from the terminal. The input for this step is the detailed operation log and sentiment data sent from the terminal. The server stores this data in a database and outputs it as basic information for later analysis.

[0529] Step 4:

[0530] The server analyzes collected operational and sentiment data to generate business procedures. Inputs include operational logs and sentiment information stored in a database. This analysis utilizes machine learning algorithms to identify tasks within the business that are particularly susceptible to efficiency improvements. Outputs include a business flow template and a list of areas requiring improvement.

[0531] Step 5:

[0532] The server visualizes business procedures using BPMN based on the analysis results. The input for this step is a business flow template and an improvement list, and the output is a visual business improvement suggestion for the user. The business flow is presented in an easy-to-understand format, with areas requiring improvement highlighted.

[0533] Step 6:

[0534] The server notifies the user of the generated business improvement suggestions. The input consists of a visualized business flow and improvement suggestions, which are then output and notified in a format suitable for the user's device. The user can then receive these suggestions and use them to review and streamline their business processes.

[0535] (Application Example 2)

[0536] 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."

[0537] In modern industrial settings, optimizing worker-machine interaction is essential for increasing production efficiency. However, stress and confusion during the production process contribute to decreased work efficiency, necessitating their identification and improvement. A challenge with existing methods is that they do not fully utilize operational history and emotional feedback.

[0538] 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.

[0539] In this invention, the server includes means for collecting operation records, means for analyzing the collected operation records to generate work processes, an emotion analysis device for analyzing the user's emotions, and means for optimizing work efficiency improvement proposals based on the user's emotion data. This makes it possible to identify stress factors in the production process and generate optimal improvement proposals to improve work efficiency.

[0540] "Operation records" refer to data collected from a user's history of a series of operations.

[0541] A "business process" refers to a series of business procedures or flows generated by analyzing operational records.

[0542] A "business efficiency improvement proposal" is a suggestion to improve efficiency based on business processes.

[0543] A "generation device" is a device that has the function of generating business processes and suggestions for improving business efficiency based on operation records.

[0544] An "emotion analysis device" is a device that has the function of analyzing a user's emotions.

[0545] "Information aggregation" refers to the process of accumulating analyzed operation records and emotional data.

[0546] "Visual representation" refers to a method of displaying generated business processes visually.

[0547] In implementing this invention, a system is primarily employed in which a server, a terminal, and a user work together as a unified entity. The server plays a central role, processing operation records and emotional data in an integrated manner. The terminal collects data in real time via wearable devices such as smart glasses. The user utilizes the resulting suggestions for improving work processes to contribute to increased productivity.

[0548] The server utilizes analysis libraries based on programming languages ​​such as Python and R to perform advanced data analysis, analyzing operation records and emotion data. During this process, emotions are extracted from image and audio data using OpenCV and the Google Cloud Speech-to-Text API. The analyzed data is stored in an information aggregation system, which then optimizes the production process.

[0549] As a concrete example, if an employee encounters difficulty with a new operating procedure while working in a factory, their feelings of confusion are captured through smart glasses and immediately analyzed on a server. Based on this analysis, suggestions for improving work efficiency are made, such as providing training videos to alleviate the employee's frustration or modifying the operating procedure.

[0550] By utilizing generative AI models, prompts like the following can be used: "Design a program to suggest improvements to work efficiency by integrating employee emotional data and operation records on a production line. The emotional data is to be obtained from smart glasses."

[0551] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0552] Step 1:

[0553] The device collects user operation records and emotion data in real time through smart glasses. Operation records include log data detailing the user's actions, while emotion data extracts feelings such as joy, anger, sadness, and happiness from facial recognition and voice analysis. This input data is bundled into packets and sent to a server via a communication module.

[0554] Step 2:

[0555] The server receives operation logs and sentiment data sent from the terminal. Upon receipt, it checks the data's integrity and standardizes the format for storage in the database. Specifically, it organizes the data based on timestamps and user IDs and associates operations with sentiment. This results in a consistent dataset necessary for analysis.

[0556] Step 3:

[0557] The server analyzes operation records based on accumulated data and extracts patterns in business processes. During this process, data mining algorithms are used to identify areas where problems frequently occur and areas where efficiency improvements are expected. The results of this analysis are output as business processes and used to generate efficiency improvements in the next step.

[0558] Step 4:

[0559] The server integrates patterns of work processes and emotional data, and uses a generative AI model to generate suggestions for improving work efficiency. The generative AI model prioritizes suggesting improvements for areas with significant emotional fluctuations or where specific operations cause stress. The output obtained here is then fed back to the user as suggestions.

[0560] Step 5:

[0561] Users receive notifications from the server regarding suggestions for improving work efficiency. These notifications are displayed on the terminal's screen or smart glasses screen. The suggestions may also include specific operating instructions and additional training materials, allowing users to adjust their work methods and improve their operations based on these suggestions.

[0562] 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.

[0563] 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.

[0564] 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.

[0565] [Fourth Embodiment]

[0566] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0567] 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.

[0568] 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).

[0569] 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.

[0570] 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.

[0571] 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).

[0572] 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.

[0573] 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.

[0574] 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.

[0575] 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.

[0576] 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.

[0577] 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.

[0578] 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".

[0579] The present invention is a system that records in detail the user's operations on a PC, generates business processes from those records, and further generates business improvement proposals based on those processes. This system includes means for collecting operation data, means for analyzing data, a generation device, and means for notifying.

[0580] Server processing:

[0581] The server receives user operation data sent from the terminal and stores it in a database. This data includes application startup status, user input patterns, and details about window operations, which the server uses to prepare for analyzing operation trends and frequencies. This analysis can identify workflows and inefficient processes. For example, the server can quantify the operations a user performs when creating a daily report and show which steps take the most time.

[0582] Terminal processing:

[0583] The terminal records user actions on the PC in real time and sends the data to the server at regular intervals. For example, it meticulously records the click locations and input data when a user edits a spreadsheet. This operation recording is performed in the background and is designed not to interrupt the user's work.

[0584] User actions:

[0585] Based on notifications from their devices, users review business process improvement suggestions generated by the server. These notifications are provided as pop-ups or dashboard updates. Users can review the suggestions and decide whether to incorporate them into their work. Specifically, if automated work procedures are suggested, users can apply the scripts to reduce work time.

[0586] The system of this invention enables users to objectively review their own work flow and quickly improve inefficient areas. Through the cooperation of the server and terminals, the system automates the collection, analysis, and generation of suggestions for operational data, resulting in an overall improvement in work efficiency.

[0587] The following describes the processing flow.

[0588] Step 1:

[0589] The terminal records the user's PC operations in real time. This operation record includes application launches, window activations, keystrokes, and mouse click locations. This data is buffered at regular intervals.

[0590] Step 2:

[0591] The terminal periodically sends buffered operation data to the server. Before transmission, the data is properly formatted and includes user ID and timestamp information.

[0592] Step 3:

[0593] The server receives operation data from the terminal and stores it in a database. The data is organized by user and stored in a format suitable for analysis.

[0594] Step 4:

[0595] The server analyzes the accumulated data using analytical tools. Specifically, it executes algorithms to extract patterns in business workflows and inefficient operating procedures.

[0596] Step 5:

[0597] The server generates a business flow in a BPMN-based format based on the analysis results. This flow is formatted to be visually easy for the user to understand.

[0598] Step 6:

[0599] The server uses an AI model to create business improvement proposals based on the generated business flow. These proposals include reducing redundant steps and suggesting processes that can be automated.

[0600] Step 7:

[0601] The server sends the completed business improvement proposal to the terminal. The submitted improvement proposal is then presented to the user via notifications or on the dashboard.

[0602] Step 8:

[0603] Users check notifications from their devices and carefully review the proposed work improvement suggestions. If necessary, they agree to the suggestions and incorporate them into their work to improve efficiency.

[0604] (Example 1)

[0605] 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".

[0606] When aiming to improve business efficiency, conventional systems faced the challenge of not being able to effectively collect and analyze user operation data and quickly and appropriately notify users of improvement suggestions based on the results. As a result, optimizing business processes took a long time, and consequently, the full potential of the improvements could not be realized.

[0607] 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.

[0608] In this invention, the server includes means for recording user operations, means for analyzing the recorded data to identify business procedures, and means for generating and notifying efficiency improvement suggestions based on the analysis results. This makes it possible to precisely analyze the user's workflow and immediately propose business improvement plans.

[0609] "Operational actions" refer to a series of actions performed by a user on a computer, such as inputting data, clicking, and operating applications.

[0610] "Means of recording" refers to methods and devices for recording user actions as digital data.

[0611] "Means of analysis" refers to algorithms and software used to analyze recorded operational data.

[0612] "Functional procedures" refer to specific steps and procedures that indicate the flow of business processes and tasks, as identified through analysis.

[0613] "Efficiency improvement proposals" refer to improvement plans and recommendations for performing tasks more efficiently based on identified functional procedures.

[0614] A "generation device" refers to hardware and software used to generate efficiency improvement suggestions from data analysis results.

[0615] "Means of notification" refers to methods or devices for communicating generated efficiency suggestions to users.

[0616] "Data repository creation" refers to the process of organizing analyzed data and storing it in a database format.

[0617] "Visual means" refers to methods and devices that display data and information to users in a graphical format to aid their understanding.

[0618] This system aims to improve work efficiency by recording and analyzing user actions. Specifically, terminals use dedicated software to sequentially record user actions and periodically send this data to a server. This software includes functions for tracking operations and generating logs. The server stores the received data in a dedicated database and uses analysis algorithms to identify the workflow. Statistical software and machine learning models are used for data analysis to clarify factors hindering work efficiency. Based on the generated functional procedures, the server creates efficiency improvement suggestions. These suggestions are generated using a generative AI model and present specific improvement plans tailored to the user's characteristics.

[0619] Efficiency suggestions are delivered to the user's device as pop-up notifications or dashboard updates, which the user then reviews. The user can then apply the suggested improvements to their own work processes. For example, in tasks requiring frequent data entry, a script to automate the data entry process might be suggested. This can significantly reduce the time required for the task.

[0620] The following are specific examples of prompt statements.

[0621] "Please generate suggestions for improving work efficiency."

[0622] "Please provide a way to automate the data entry process."

[0623] Therefore, the purpose of this system is to appropriately monitor and analyze the user's work and provide concrete and effective suggestions for efficiency improvements.

[0624] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0625] Step 1:

[0626] The device records user actions in real time. Specifically, it captures data such as mouse click locations, keyboard input, and application launches and shutdowns. This is the input, and the recorded series of actions becomes the output. This process runs in the background and is designed not to interrupt the user's work.

[0627] Step 2:

[0628] The terminal sends operation logs recorded at regular intervals to the server. The transmitted data is the input, and the storage of that data in the server's database is the output. The terminal verifies the integrity and accuracy of the data and performs error checking during the transmission process.

[0629] Step 3:

[0630] The server analyzes the received operation log data. The input is the operation logs stored in the database, and the output is the identification of the analyzed business procedures. Specifically, it uses an analysis algorithm to identify frequently occurring operations and time-consuming steps, and quantifies the business flow.

[0631] Step 4:

[0632] The server uses a generative AI model to create efficiency suggestions based on the analysis results. The analysis results are input into the generative AI model, and the output is a user-optimized suggestion. This process generates automatable procedures and hints for work improvement.

[0633] Step 5:

[0634] The server sends the generated efficiency suggestions to the terminal. The user receives notifications on the terminal via pop-ups or dashboards. The output is that the suggestions are entered and the user recognizes the improvement suggestions. Specifically, the user reviews the suggestions and decides whether to apply them to their own workflow.

[0635] (Application Example 1)

[0636] 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".

[0637] In modern factory production lines, it is essential to effectively collect and analyze operation logs from various machines to optimize efficient production processes. However, conventional systems have limitations in automatically generating and notifying improvement suggestions based on the effective collection and analysis of operation and motion data. This has resulted in insufficient factory efficiency and wasted time and resources.

[0638] 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.

[0639] In this invention, the server includes means for collecting operation data, means for analyzing the collected operation data to generate business processes, and an information processing device for generating business improvement proposals based on the generated business processes. This makes it possible to efficiently collect and analyze operation data of factory machinery and robots, and to automatically generate and propose optimal operation procedures.

[0640] "Means for collecting operation data" refers to a mechanism or process for collecting information about operations performed by a user or device.

[0641] "Means for generating business processes" refers to devices or algorithms for analyzing collected data, extracting business flows and procedures, and visualizing them.

[0642] An "information processing device" is a computing device or system for processing data and generating new information based on that data.

[0643] A "knowledge generation device" is a device that executes algorithms or programs to propose efficient procedures and actions based on collected motion data.

[0644] A "visual presentation device" is a device or interface that displays information to a user in text, graphics, or other forms to aid in understanding.

[0645] An "information management device" is a system for organizing, storing, and providing data in a format that is accessible as needed.

[0646] This system collects and analyzes operational data from various robots and machines in real time to improve production efficiency within the factory and generates optimization proposals. First, an operational data collection device installed in the terminal collects detailed data on the operation of each robot. This collected data includes the start and stop timing of work, movement patterns, and energy consumption.

[0647] The collected data is sent directly to the server. The server uses an information processing device to analyze the operational data. Python and its data analysis libraries, specifically Pandas and NumPy, are used for this analysis. From the information obtained through data analysis, business processes are generated, and inefficient processes and bottlenecks are identified. Furthermore, this information is processed by a knowledge generation device, and the optimal operating procedure is proposed. Machine learning libraries such as TensorFlow are used for business process generation.

[0648] The proposed business improvement plans are displayed on a dashboard using a visual display device. This allows users to review the new work procedures and decide whether or not to implement them. For example, a proposal might be made to shorten the route taken when transporting a specific part, which is expected to reduce travel time by approximately 20%.

[0649] Furthermore, by utilizing a generative AI model and taking input such as "Please propose an optimization plan to improve transport efficiency based on the latest transport data" as an example of a prompt, the system can provide more accurate suggestions. In this way, the entire system can objectively and efficiently improve operations within the factory.

[0650] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0651] Step 1:

[0652] The terminal collects operational data from factory robots in real time. This data includes the start and stop timings, movement paths, and energy consumption of each robot. The input is robot sensor information, and the output is operation log data compiled from this information. The collected data is stored in a buffer and periodically sent to the server.

[0653] Step 2:

[0654] The server receives operation log data sent from the terminal and stores it in the database. The input is the operation log data received from the terminal, and the output is structured information recorded in the database. A database management system is used to organize this data.

[0655] Step 3:

[0656] The server analyzes the received data using a data analysis engine. The input is robot operation data stored in a database. Using Python and data analysis libraries such as Pandas and NumPy, trends and patterns are extracted from the data, and a report indicating process bottlenecks is generated as output.

[0657] Step 4:

[0658] The server uses the generated AI model based on the analysis results to produce business improvement proposals. An example of a prompt message provided to the AI ​​is, "Please suggest the optimal operating procedure for a specific transport task." The input is the analysis results obtained earlier, and the output is the recommended operating procedure or improvement proposal.

[0659] Step 5:

[0660] The user receives business improvement suggestions generated from the server through a visual display device. The input is improvement suggestion data from the server, and the output is visualized improvement suggestions displayed on a dashboard. The user reviews these and decides whether to implement them.

[0661] 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.

[0662] This invention relates to a system that generates business processes by meticulously recording user operation data on a PC and analyzing that data. By combining this system with an emotion engine to recognize user emotions, the system considers the user's reaction to automatically generated business improvement proposals and provides more personalized improvement suggestions.

[0663] Server processing:

[0664] The server receives operation data sent from the terminal and stores it in a database. This collected data includes not only the operation history of the applications used by the user, but also sentiment data generated by the sentiment engine. The server analyzes this data to generate a business flow. The generated business flow allows for the extraction of elements that contribute to business improvement through visualization based on BPMN and comparison with sentiment data. For example, if the server finds that a user tends to show frustration at a particular step, it will identify that part of the work as a target for improvement.

[0665] Terminal processing:

[0666] The terminal not only records the user's PC operations in real time, but also uses an emotion engine to analyze the user's emotions from their facial expressions and voice. The analyzed emotion data is sent to the server along with the operation log. For example, if a user is confused when performing a difficult operation, that emotion is captured as data and taken into consideration in subsequent analysis.

[0667] User actions:

[0668] Users receive business improvement suggestions generated by the server and access them through notifications. These suggestions are refined based on user emotional feedback and are designed to reduce user stress. For example, tasks that frequently cause frustration are suggested to be automated.

[0669] This invention provides a system that offers deep insights for improving work efficiency from both user operation behavior and emotional data, and provides users with practical and highly satisfying improvement suggestions. The system not only improves the quality of user work but also significantly contributes to enhancing the user experience.

[0670] The following describes the processing flow.

[0671] Step 1:

[0672] The device records the user's PC operations in real time and uses an emotion engine to detect emotions from the user's facial expressions and voice. Emotional data includes emotions such as joy, confusion, and frustration.

[0673] Step 2:

[0674] The device buffers the collected operational and emotional data at regular intervals and formats them into a single dataset.

[0675] Step 3:

[0676] The terminal sends a formatted dataset to the server. This dataset includes timestamps and user profile information to ensure data consistency.

[0677] Step 4:

[0678] The server stores the received datasets in a database. The data is organized separately for each user and stored in a format suitable for analysis.

[0679] Step 5:

[0680] The server processes the accumulated data using analysis tools. Specifically, it generates business flows and visualizes user sentiment trends based on operation patterns and sentiment data.

[0681] Step 6:

[0682] The server generates business improvement proposals based on the analysis results. Here, the improvement suggestions focus particularly on steps where users exhibit strong emotions (e.g., frustration or confusion).

[0683] Step 7:

[0684] The server sends the generated business improvement proposals to the terminal. The improvement proposals are appropriately visualized and provided in a format that is easy for the user to understand.

[0685] Step 8:

[0686] Users can review the suggested improvements notified via their devices, evaluate their content, and incorporate them into their own work. Suggestions that are particularly considerate of users' emotions make them more likely to accept the improvements.

[0687] (Example 2)

[0688] 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".

[0689] In today's work environment, improving work procedures is essential for efficient and stress-free work execution. However, conventional systems analyze only information about user actions to improve processes, making it difficult to provide personalized improvement suggestions that take into account user emotions and reactions. Furthermore, many process improvement proposals tend to overlook some key points, limiting the overall efficiency of operations.

[0690] 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.

[0691] In this invention, the server includes means for collecting information about operations, means for analyzing the collected information about operations to generate work procedures, and means for analyzing the user's emotions and incorporating the feedback into suggestions for improving operations. This enables the dynamic improvement of work procedures in response to the user's emotional reactions, leading to more personalized work improvements and increased work efficiency.

[0692] "Information regarding operations" refers to the specific actions performed by the user on the application or system, and includes the type of operation, timing, and duration.

[0693] "Business procedures" define the sequence of tasks and procedures necessary for business operations, including the specific order and content of actions.

[0694] A "generation device" refers to a device that automatically assembles strategies and proposals for business improvement based on collected operational and emotional information.

[0695] "Emotional information" refers to data on emotional states and psychological responses obtained by analyzing the user's facial expressions, tone of voice, and other nonverbal cues.

[0696] "Means of notification" refers to methods and technologies for informing users of generated business improvement suggestions, and includes email, pop-up notifications, and dashboard displays.

[0697] "Information aggregation" refers to the process of combining and organizing data obtained from multiple different data sources into a single, unified dataset.

[0698] "Visualization" refers to the process of converting abstract data or complex information into a visual format to make it easier to understand.

[0699] This invention is a system for collecting information on user actions and emotions to improve business operations. This system is implemented as follows:

[0700] The server receives information about the user's actions from their terminal and securely stores it in a database. This information includes details about which applications the user interacted with and how. Sentimental information obtained through the sentiment engine is also stored. The server analyzes the collected data and generates business procedures. Machine learning algorithms are used for the analysis to identify areas in the business procedures where efficiency can be improved. The business procedures are visualized based on BPMN, visually highlighting key areas for improvement.

[0701] The terminal monitors user actions in real time and records data. Furthermore, an emotion engine built into the terminal analyzes the user's facial expressions and voice to determine their emotions, and transmits this data to a server. This allows changes in the user's emotions to be incorporated as a crucial element for improving work processes.

[0702] Users receive notifications from the server regarding suggestions for improving their work processes and review the suggestions. These suggestions incorporate user emotional feedback and are designed to reduce stress and improve work efficiency.

[0703] For example, if a specific work procedure that frequently frustrates users is identified, automation of that procedure is proposed. This process improves overall work productivity and enhances the user experience.

[0704] As an example of a prompt, providing the AI ​​model with input such as, "How should user sentiment data be utilized in generating business improvement proposals?" can be expected to result in more accurate and personalized improvement proposals.

[0705] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0706] Step 1:

[0707] The device records the user's actions on the computer in real time. Specifically, it collects information such as the name of the application the user is using, the time the action started, and the content of the action. This action data is output as a detailed log of the user's usage and input into the emotion engine.

[0708] Step 2:

[0709] The emotion engine built into the device analyzes the user's facial expressions and voice to determine their emotional state. Inputs include real-time video and audio captured from the camera and microphone. Based on this, facial recognition and voice analysis technologies are used to quantify the user's emotions. The output emotion data, along with operation data, is sent to the server as information indicating the user's stress level and degree of pleasure or displeasure.

[0710] Step 3:

[0711] The server receives operation data and sentiment data sent from the terminal. The input for this step is the detailed operation log and sentiment data sent from the terminal. The server stores this data in a database and outputs it as basic information for later analysis.

[0712] Step 4:

[0713] The server analyzes collected operational and sentiment data to generate business procedures. Inputs include operational logs and sentiment information stored in a database. This analysis utilizes machine learning algorithms to identify tasks within the business that are particularly susceptible to efficiency improvements. Outputs include a business flow template and a list of areas requiring improvement.

[0714] Step 5:

[0715] The server visualizes business procedures using BPMN based on the analysis results. The input for this step is a business flow template and an improvement list, and the output is a visual business improvement suggestion for the user. The business flow is presented in an easy-to-understand format, with areas requiring improvement highlighted.

[0716] Step 6:

[0717] The server notifies the user of the generated business improvement suggestions. The input consists of a visualized business flow and improvement suggestions, which are then output and notified in a format suitable for the user's device. The user can then receive these suggestions and use them to review and streamline their business processes.

[0718] (Application Example 2)

[0719] 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".

[0720] In modern industrial settings, optimizing worker-machine interaction is essential for increasing production efficiency. However, stress and confusion during the production process contribute to decreased work efficiency, necessitating their identification and improvement. A challenge with existing methods is that they do not fully utilize operational history and emotional feedback.

[0721] 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.

[0722] In this invention, the server includes means for collecting operation records, means for analyzing the collected operation records to generate work processes, an emotion analysis device for analyzing the user's emotions, and means for optimizing work efficiency improvement proposals based on the user's emotion data. This makes it possible to identify stress factors in the production process and generate optimal improvement proposals to improve work efficiency.

[0723] "Operation records" refer to data collected from a user's history of a series of operations.

[0724] A "business process" refers to a series of business procedures or flows generated by analyzing operational records.

[0725] A "business efficiency improvement proposal" is a suggestion to improve efficiency based on business processes.

[0726] A "generation device" is a device that has the function of generating business processes and suggestions for improving business efficiency based on operation records.

[0727] An "emotion analysis device" is a device that has the function of analyzing a user's emotions.

[0728] "Information aggregation" refers to the process of accumulating analyzed operation records and emotional data.

[0729] "Visual representation" refers to a method of displaying generated business processes visually.

[0730] In implementing this invention, a system is primarily employed in which a server, a terminal, and a user work together as a unified entity. The server plays a central role, processing operation records and emotional data in an integrated manner. The terminal collects data in real time via wearable devices such as smart glasses. The user utilizes the resulting suggestions for improving work processes to contribute to increased productivity.

[0731] The server utilizes analysis libraries based on programming languages ​​such as Python and R to perform advanced data analysis, analyzing operation records and emotion data. During this process, emotions are extracted from image and audio data using OpenCV and the Google Cloud Speech-to-Text API. The analyzed data is stored in an information aggregation system, which then optimizes the production process.

[0732] As a concrete example, if an employee encounters difficulty with a new operating procedure while working in a factory, their feelings of confusion are captured through smart glasses and immediately analyzed on a server. Based on this analysis, suggestions for improving work efficiency are made, such as providing training videos to alleviate the employee's frustration or modifying the operating procedure.

[0733] By utilizing generative AI models, prompts like the following can be used: "Design a program to suggest improvements to work efficiency by integrating employee emotional data and operation records on a production line. The emotional data is to be obtained from smart glasses."

[0734] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0735] Step 1:

[0736] The device collects user operation records and emotion data in real time through smart glasses. Operation records include log data detailing the user's actions, while emotion data extracts feelings such as joy, anger, sadness, and happiness from facial recognition and voice analysis. This input data is bundled into packets and sent to a server via a communication module.

[0737] Step 2:

[0738] The server receives operation logs and sentiment data sent from the terminal. Upon receipt, it checks the data's integrity and standardizes the format for storage in the database. Specifically, it organizes the data based on timestamps and user IDs and associates operations with sentiment. This results in a consistent dataset necessary for analysis.

[0739] Step 3:

[0740] The server analyzes operation records based on accumulated data and extracts patterns in business processes. During this process, data mining algorithms are used to identify areas where problems frequently occur and areas where efficiency improvements are expected. The results of this analysis are output as business processes and used to generate efficiency improvements in the next step.

[0741] Step 4:

[0742] The server integrates patterns of work processes and emotional data, and uses a generative AI model to generate suggestions for improving work efficiency. The generative AI model prioritizes suggesting improvements for areas with significant emotional fluctuations or where specific operations cause stress. The output obtained here is then fed back to the user as suggestions.

[0743] Step 5:

[0744] Users receive notifications from the server regarding suggestions for improving work efficiency. These notifications are displayed on the terminal's screen or smart glasses screen. The suggestions may also include specific operating instructions and additional training materials, allowing users to adjust their work methods and improve their operations based on these suggestions.

[0745] 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.

[0746] 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.

[0747] 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.

[0748] 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.

[0749] 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.

[0750] 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.

[0751] 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.

[0752] 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.

[0753] 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."

[0754] 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.

[0755] 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.

[0756] 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.

[0757] 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.

[0758] 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.

[0759] 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.

[0760] 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.

[0761] 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.

[0762] 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.

[0763] 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.

[0764] 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.

[0765] 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.

[0766] The following is further disclosed regarding the embodiments described above.

[0767] (Claim 1)

[0768] Means for collecting operational data,

[0769] A means of generating business processes by analyzing collected operational data,

[0770] A generation device that generates business improvement proposals based on the generated business processes,

[0771] A means of notifying the generated business improvement proposals,

[0772] A system that includes this.

[0773] (Claim 2)

[0774] The system according to claim 1, further comprising means for accumulating and creating a database of the analyzed operation data.

[0775] (Claim 3)

[0776] The system according to claim 1, further comprising means for visualizing and displaying the generated business processes.

[0777] "Example 1"

[0778] (Claim 1)

[0779] A means for recording operational actions,

[0780] A means of analyzing recorded data to identify functional procedures,

[0781] A generation device that generates efficiency improvement proposals based on the generated functional procedures,

[0782] A means of notifying users of the generated efficiency improvement suggestions,

[0783] A system that includes this.

[0784] (Claim 2)

[0785] The system according to claim 1, further comprising means for accumulating and storing the analyzed data in an information repository.

[0786] (Claim 3)

[0787] The system according to claim 1, further comprising means for displaying and visually indicating the generated functional procedure.

[0788] "Application Example 1"

[0789] (Claim 1)

[0790] Means for collecting operational data,

[0791] A means of generating business processes by analyzing collected operational data,

[0792] An information processing device for generating business improvement proposals based on the generated business processes,

[0793] A means of notifying the generated business improvement proposals,

[0794] A knowledge generation device for collecting operational data and proposing efficient operational procedures,

[0795] A visual display device for showing and confirming the proposed improvement measures,

[0796] A system that includes this.

[0797] (Claim 2)

[0798] The system according to claim 1, further comprising means for accumulating analyzed operation data and creating a database using an information management device.

[0799] (Claim 3)

[0800] The system according to claim 1, further comprising means for visualizing and displaying the generated business processes and operational optimization proposals.

[0801] "Example 2 of combining an emotion engine"

[0802] (Claim 1)

[0803] Means for collecting information about the operation,

[0804] A means for analyzing collected information on operations to generate business procedures,

[0805] A generation device for generating business improvement proposals based on generated business procedures,

[0806] A means of analyzing user emotions and incorporating that feedback into suggestions for improving operations,

[0807] A means of notifying the generated improvement suggestions,

[0808] A system that includes this.

[0809] (Claim 2)

[0810] The system according to claim 1, further comprising means for aggregating and combining information related to the analyzed operations and emotional information.

[0811] (Claim 3)

[0812] The system according to claim 1, further comprising means for visualizing and displaying the generated business procedures.

[0813] "Application example 2 when combining with an emotional engine"

[0814] (Claim 1)

[0815] Means for collecting operation records,

[0816] A means of generating business processes by analyzing collected operation records,

[0817] A generation device that generates business efficiency improvement proposals based on the generated business processes,

[0818] An emotion analysis device that analyzes the user's emotions,

[0819] A method for optimizing business efficiency improvements based on user sentiment data,

[0820] A means of notifying optimized business efficiency improvements,

[0821] A system that includes this.

[0822] (Claim 2)

[0823] The system according to claim 1, further comprising means for accumulating and aggregating analyzed operation records and emotion data.

[0824] (Claim 3)

[0825] The system according to claim 1, further comprising means for visually representing and visually displaying the generated business processes. [Explanation of Symbols]

[0826] 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. Means for collecting operational data, A means of generating business processes by analyzing collected operational data, A generation device that generates business improvement proposals based on the generated business processes, A means of notifying the generated business improvement proposals, A system that includes this.

2. The system according to claim 1, further comprising means for accumulating and creating a database of the analyzed operation data.

3. The system according to claim 1, further comprising means for visualizing and displaying the generated business processes.

Images