system

Abstract

Provide a system. A means for acquiring a user's business records; means for transmitting the business records to a server; means for analyzing the business records received in the server and identifying business processes; a means for generating efficiency recommendations based on the analysis results; a means for feeding back the generated efficiency proposals to the user; The system includes means for communicating said feedback to a user.

Description

[Technical Field]

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

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

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

[0004] In today's business environment, many companies and individuals are overwhelmed with an ever-increasing workload. As a result, work efficiency is declining, creating a need for improved productivity. In particular, because a large amount of time is spent on repetitive tasks and administrative work, there is a need for methods to streamline these tasks and reduce work time. Current systems lack the mechanisms to centrally manage and analyze individual work records and automatically generate specific efficiency proposals, so this project aims to solve these issues. [Means for solving the problem]

[0005] The present invention provides a system that includes a means for acquiring a user's business records, a means for transmitting the business records to a server, a means for analyzing the received business records and identifying business processes in the server, a means for generating efficiency proposals based on the analysis results, a means for providing feedback on the generated efficiency proposals to the user, and a means for exchanging the feedback with the user. The analysis means can identify the duration, frequency, and repetitive tasks of a business, and the efficiency proposal generation means can include suggestions for robotic process automation (RPA), the use of voice input, and the use of generative artificial intelligence (AI) for automation. This system enables users to visualize their own business processes and receive and implement specific and practical efficiency proposals.

[0006] "Business records" refers to information such as the start and end times of various tasks performed by a user, as well as specific work content.

[0007] A "server" is a computing system that receives, stores, and analyzes data over a network.

[0008] "Analysis" is the process of extracting patterns and specific information from received data to derive meaningful results and insights.

[0009] "Efficiency proposals" refer to proposals for specific methods and means to carry out business operations more efficiently based on the results of analysis.

[0010] "Feedback" is the process of providing information such as efficiency suggestions to users and collecting their reactions and responses.

[0011] "Robotic Process Automation (RPA)" is a technology that uses software robots to automate repetitive business processes.

[0012] "Voice input" is a technology that recognizes voice as text data and performs input processing.

[0013] "Generative artificial intelligence (AI)" is an artificial intelligence technology that uses machine learning and natural language generation techniques to generate and analyze information in a manner similar to that of a human. [Brief explanation of the drawings]

[0014] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram illustrating a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0015] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0018] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0019] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0020] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0022] [First embodiment]

[0023] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0024] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0027] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0030] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0034] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0035] The present invention provides a system for efficient business management and proposal making. Specific embodiments will be described below.

[0036] Overall system overview

[0037] In this system, users collect daily work records and send them to a server, which then analyzes the data, generates efficiency improvement proposals, and provides feedback to the users.

[0038] Data Collection Phase

[0039] When a user starts a task using a dedicated application, the start time and content are recorded. For example, "09:00 - Started checking email" or "09:30 - Ended checking email." The device also records activity logs during the task (keystrokes, application usage time, browser history, etc.).

[0040] The collected business record data is automatically sent from the terminal to the server at regular intervals (e.g., every hour), allowing data to be collected without any user intervention.

[0041] Data analysis phase

[0042] The server receives business record data sent from the terminal. After receiving it, it stores it in a database and first performs pre-processing. In pre-processing, it checks for inconsistencies and missing values ​​and performs data cleansing. For example, if there is a discrepancy between the start time and end time, this is corrected.

[0043] The server then analyzes the data using analytical algorithms to identify the duration, frequency, and repetitiveness of each task, such as the amount of time spent checking email or writing reports.

[0044] Proposal of efficiency methods

[0045] Based on the analysis, the server generates efficiency suggestions, which may include:

[0046] Automation using RPA: For example, a proposal to automate tasks such as replying to emails according to specific templates.

[0047] Voice input: If you are taking too long to type a sentence, we suggest you use a voice input tool.

[0048] Generative AI: Suggestions for automatically generating reports from templates.

[0049] These suggestions are personalized to suit your work style.

[0050] Providing feedback

[0051] The server compiles the generated efficiency proposals in the form of a dashboard. The proposals and their implementation procedures are presented to the user in a visually easy-to-read format. The server also notifies the user that the dashboard has been updated and provides feedback.

[0052] The device displays the dashboard for users to review and refine their own business processes, and implement the proposed improvements.

[0053] Implementing and reassessing efficiencies

[0054] The user reviews their business processes based on the dashboard and implements the proposed efficiency measures, such as setting up a new RPA bot or introducing a voice input tool. The device then records the user's business actions and transfers them to the server.

[0055] The server re-analyzes the data based on the new data, evaluates the effectiveness of the proposals, and makes further improvement proposals as needed, allowing users to continuously optimize their operations.

[0056] The processing flow will be explained below.

[0057] Step 1: User Business Records

[0058] When a user starts a task, they use a dedicated application to record the start and end of the task. For example, "09:00 - Start checking email" and "09:30 - End checking email." At that time, the device records an operation log (keystrokes, app usage time, browser history, etc.) in the background.

[0059] Step 2: Sending data

[0060] The terminal automatically sends collected business record data to the server at regular intervals (e.g., every hour), reducing the user's workload and making data collection more efficient.

[0061] Step 3: Receiving the data

[0062] The server receives the business record data sent from the terminal and stores it in a database, at which point it checks the consistency and integrity of the data.

[0063] Step 4: Preprocessing the data

[0064] The server pre-processes the data it receives, checking for inconsistencies and missing values ​​and performing data cleansing, for example correcting discrepancies between the start and end times of records.

[0065] Step 5: Analyze business processes

[0066] The server analyzes the data using analytical algorithms, identifying the duration, frequency, and repetitiveness of each task and assessing potential for efficiency improvements. For example, it identifies that checking email takes an average of 30 minutes, but involves many manual steps.

[0067] Step 6: Generate efficiency proposals

[0068] The server generates efficiency proposals based on the analysis results, including automation using RPA, the use of voice input, and the use of generative artificial intelligence (AI).

[0069] Step 7: Personalize your suggestions

[0070] The server customizes efficiency suggestions for each user based on their work style and past data, providing more practical suggestions.

[0071] Step 8: Generate a dashboard as feedback

[0072] The server compiles the generated efficiency suggestions into a visually appealing dashboard and prepares feedback with details of the suggestions and implementation steps.

[0073] Step 9: Notify users

[0074] The server notifies the user of updates to the dashboard, after which the user can check the detailed proposals.

[0075] Step 10: View the dashboard

[0076] The device will then present the user with a dashboard where they can review the recommendations, and the user will receive feedback to help them implement specific efficiency measures.

[0077] Step 11: Implement efficiency techniques

[0078] Users can follow the dashboard's suggestions to review their business processes and implement the suggested efficiency measures, such as setting up a new RPA bot or introducing a voice input tool.

[0079] Step 12: Collect new data

[0080] The device then records the user's work activities again and transfers the new data to the server, allowing the effectiveness of the efficiency improvement measures to be evaluated.

[0081] Step 13: Reanalyze and update proposals

[0082] The server reanalyzes the data based on the new data, evaluates the effectiveness of previous suggestions, and makes new suggestions for improvement as needed, allowing users to continuously optimize their operations.

[0083] Example 1

[0084] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0085] Modern business environments demand efficient business management. It is particularly important to automate the entire process, from acquiring business records to analyzing data and proposing efficiency improvements, to reduce user workload. However, previous systems lacked sufficient data preprocessing, cleansing, and efficient feedback, placing a heavy burden on users. Furthermore, efficiency improvements often lacked practicality, as they were not adapted to individual business styles. The objective of this invention is to solve these problems and provide a more effective business management system.

[0086] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0087] In this invention, the server includes means for preprocessing a user's business records and cleansing the data, means for analyzing the preprocessed data and identifying business processes, and means for providing generated efficiency suggestions as feedback to the user in the form of a dashboard. This improves the accuracy of the analysis of business records and the practicality of the efficient feedback and suggestions to the user, thereby enabling business optimization.

[0088] "User" means an individual or organization that uses the System to enter and review business records.

[0089] "Business records" refers to data such as the start time, end time, and content of business operations entered by the user.

[0090] "Server" refers to a computer system that receives business record data and performs pre-processing, analysis, and generation of efficiency recommendations.

[0091] "Preprocessing" refers to the process of checking for inconsistencies and missing values ​​in received business record data and cleansing the data.

[0092] "Data cleansing" refers to the process of correcting inconsistencies and missing values ​​in data and preparing it for analysis.

[0093] "Analysis" refers to the process of identifying business processes based on preprocessed data and analyzing the time, frequency, repetitive tasks, etc. of each task.

[0094] "Efficiency proposals" refer to proposing methods and tools to improve business efficiency based on the analysis results.

[0095] "Dashboard format" refers to an interface format that provides information to users in a visually easy-to-read format.

[0096] "Feedback" refers to the process of providing the generated efficiency proposals to the user, encouraging the implementation of the proposals through interaction with the user, and evaluating their effectiveness.

[0097] A "prompt" refers to a textual instruction that is input to a generative AI model.

[0098] A "generative AI model" refers to an algorithm that uses artificial intelligence to automatically generate efficiency suggestions based on prompt text.

[0099] "Robotic Process Automation (RPA)" refers to a software tool for automating routine tasks.

[0100] "Voice input" refers to a technique that allows a user to input voice as text data.

[0101] "Automated report generation" refers to the process of using generative AI models or other tools to automatically generate standard reports.

[0102] This invention provides a system for efficient business management and proposals. Specific embodiments are described below. In this system, a user acquires daily business records and sends them to a server, which analyzes the data, generates efficiency proposals, and provides feedback to the user.

[0103] Data Collection Phase

[0104] When a user starts a task using a dedicated application, the start time and task content are recorded. For example, it is recorded as "09:00 - Started checking email" and "09:30 - Ended checking email." The device also records activity logs during the task (keystrokes, application usage time, browser history, etc.). This information is automatically sent from the device to the server at regular intervals (e.g., every hour). This procedure allows data to be collected without the user having to perform any special operations.

[0105] Data analysis phase

[0106] The server receives the business record data sent from the device and stores it in a database. It then performs preprocessing, checking for inconsistencies and missing values ​​and cleansing the data. For example, it corrects discrepancies between start and end times. It then uses analytical algorithms to analyze the data and identify the duration, frequency, and repetitive tasks of each task. For example, it identifies the time spent checking email or writing reports.

[0107] Generate efficiency proposals

[0108] Based on the analysis, the server generates efficiency suggestions, which may include:

[0109] Automation using RPA: For example, a proposal to automate tasks such as replying to emails according to specific templates.

[0110] Voice input: If you are taking too long to type a sentence, we suggest you use a voice input tool.

[0111] Generative AI: Suggestions for automatically generating reports from templates.

[0112] These suggestions are personalized to suit the user's work style. For example, suggestions might include, "Use the RPA task automation tool to reduce the time spent checking emails," or "Introduce a voice input tool to create reports."

[0113] Providing feedback

[0114] The server compiles the generated efficiency improvement proposals in the form of a dashboard. The proposals and their implementation procedures are provided to the user in a visually easy-to-read format. The server notifies the user that the dashboard has been updated, and the contents of the dashboard are displayed on the user's device. The user can then review their own business processes and implement the proposed efficiency improvements.

[0115] Implementing and reassessing efficiencies

[0116] Users review their business processes based on the dashboard and implement the proposed efficiency improvements. For example, they can set up a new RPA bot or introduce a voice input tool. The device then records the user's work actions again and transfers them to the server. The server then reanalyzes the new data, evaluates the effectiveness of the proposals, and makes further improvement proposals as necessary. This allows users to continuously optimize their work.

[0117] Specific Examples

[0118] Example prompt: Analyze the following work log and generate efficiency suggestions: "09:00 - Start checking email", "09:30 - Finish checking email", "10:00 - Start writing report".

[0119] This prompt allows the generative AI model to generate efficiency suggestions based on the input data.

[0120] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0121] Step 1:

[0122] When a user starts a task by launching a dedicated application, the start time and task content are recorded. The user inputs something like "09:00 - Start checking email." Based on this input, the device generates data on the task start time and content, and this data is temporarily stored in memory.

[0123] Step 2:

[0124] While the user continues to work, the device records activity logs such as keystrokes, application usage time, and browser history. Specifically, software built into the device monitors the user's keyboard input and mouse clicks and records this information in a log file. The log file contains timestamps and the details of the actions taken.

[0125] Step 3:

[0126] The terminal automatically sends recorded business data and operation logs to the server at regular intervals (e.g., every hour). A specific API endpoint is used for sending. The input is business data and operation logs, and the output is a JSON-formatted data packet containing this data sent via the API.

[0127] Step 4:

[0128] The server stores the received business data and operation logs in a database. After storing, the server performs preprocessing, checks for inconsistencies and missing values, and cleanses the data. Specifically, it queries the data from the database and corrects inconsistent time information or missing information. The input is the received data, and the output is the cleansed data.

[0129] Step 5:

[0130] The server analyzes the cleansed data. It uses an analysis algorithm to identify the duration, frequency, and repetitive tasks of each task. Specifically, the server aggregates the data, counts frequency, and analyzes whether the same tasks are repeated. The input for the analysis is the cleansed data, and the output is an analysis of the duration, frequency, and repetitive tasks for each task.

[0131] Step 6:

[0132] The server generates efficiency improvement proposals based on the analysis results. Using a generative AI model, specific prompts are input and efficiency proposals are generated. For example, a specific proposal such as "Please set up RPA to reduce the time spent checking emails" is generated. The inputs are the analysis results and the prompt text, and the output is the text of the efficiency proposal.

[0133] Step 7:

[0134] The server compiles the generated efficiency suggestions in the form of a dashboard and provides it to the user. The server then sends a notification to the user that the dashboard has been updated, and the device displays the dashboard content. Specifically, the dashboard generation algorithm organizes the suggestions in a visually easy-to-read format, and the notification system notifies the user of updates. The efficiency suggestions are input, and a dashboard and notification are generated as output.

[0135] Step 8:

[0136] Users review their business processes based on the dashboard and implement the proposed efficiency measures. Specifically, users configure a new RPA bot and install a voice input tool, which improves business efficiency. The input is the efficiency suggestions on the dashboard, and the output is the implemented efficiency measures.

[0137] Step 9:

[0138] The device then records the user's work activities again and transfers them to the server. The server then reanalyzes the new data and evaluates the effectiveness of the proposals. If necessary, it makes further improvement proposals. The input is updated work data, and the output is evaluation results and new efficiency proposals. This allows the user to continuously optimize their work.

[0139] (Application example 1)

[0140] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0141] Conventional factory robot operation management systems lacked the functionality to automatically generate and provide feedback on efficiency and optimization proposals. This made it difficult for factory managers to grasp the robot's operating status in real time and operate efficiently. In particular, the lack of a means to effectively manage robot operating hours, task completion times, and the number of errors made it difficult to quickly make appropriate efficiency proposals.

[0142] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0143] In this invention, the server includes: means for acquiring user business records; means for transmitting the business records to the server; means for analyzing the business records received by the server and identifying business processes; means for generating efficiency improvement proposals based on the analysis results; means for feeding back the generated efficiency improvement proposals to the user; means for exchanging the feedback with the user; means for recording the operating time, task completion time, and number of error occurrences of robots in the factory; means for collecting the recorded data and transmitting it to the server at regular intervals; means for generating efficiency improvement proposals for the factory robots based on the collected data; and means for feeding back the robot proposals to a manager. This makes it possible to automate business management and efficiency improvement proposals for factory robots and provide managers with feedback for effective business operations in real time.

[0144] Definitions of important words

[0145] "User" means any individual or legal entity that uses the System and obtains Business Records.

[0146] "Business records" are data related to a user's business, and include information such as operating hours, task completion times, and the number of errors that have occurred.

[0147] "Server" means a central processing unit that receives, analyzes, stores, and provides feedback on data on the cloud or on-premise.

[0148] The "analysis means" refers to algorithms or software that analyzes the business record data received by the server and identifies the business process.

[0149] "Efficiency proposals" are proposals for optimization based on the duration, frequency, and repetitive tasks of work, generated using analytical tools.

[0150] The "feedback means" is a means for providing the generated efficiency improvement proposals to the user, and for reviewing and optimizing the business.

[0151] A "factory robot" is an automated mechanical device that performs various tasks in a factory.

[0152] "Operating time" refers to data that indicates the total amount of time that a factory robot is actually active.

[0153] "Task completion time" refers to the time it takes to complete a particular task or operation.

[0154] "Number of errors" refers to data that counts the number of errors and breakdowns that occur when a factory robot is working.

[0155] The "collection means" refers to the hardware and software used to collect business record data from factory robots at regular intervals and send it to a server.

[0156] An "administrator" is a person or role that monitors the operation of factory robots and optimizes business operations based on efficiency suggestions.

[0157] MODE FOR CARRYING OUT THE INVENTION

[0158] The present invention is a system aimed at managing the work of factory robots and improving their efficiency. Specifically, it collects users' work records, analyzes them on a server, generates efficiency proposals, and provides feedback.

[0159] Overall system configuration

[0160] The system mainly includes the following means:

[0161] 1. Data collection method: The user records the operating time of the factory robot, the time to complete a task, and the number of errors that occur.

[0162] 2. Data transmission means: The collected business record data is automatically transmitted from the terminal to the server at regular intervals.

[0163] 3. Data analysis means: The server analyzes the received business record data, identifies the business process, and generates efficiency proposals based on the analysis results.

[0164] 4. Feedback means: The generated efficiency proposals are fed back to the administrator (user) by the server and displayed in the form of a visual dashboard.

[0165] Data collection methods

[0166] Users can use dedicated software to obtain the work records of factory robots. This software has the function of recording the robot's operating time, task completion time, and number of errors in real time. For example, the operating time (10:00 - 10:30) and number of errors (5) of a welding robot can be recorded.

[0167] Data transmission method

[0168] The collected data is sent to a server at regular intervals (e.g., every hour) via the smartphone, allowing data to be collected and transferred to the server without any user intervention.

[0169] Data Analysis Methods

[0170] The server analyzes the received data using algorithms to identify the duration, frequency, and repetitive tasks of tasks (for example, how many times a particular task is repeated per day and how long it takes), and uses generative AI models to create prompts that generate efficiency suggestions.

[0171] Efficiency proposal generation method

[0172] The server uses generative AI models to generate efficiency recommendations, including the adoption of robotic process automation (RPA), voice input, and generative artificial intelligence (AI). For example, it might suggest the adoption of RPA to automate a specific welding task.

[0173] Feedback Methods

[0174] The generated efficiency proposals are fed back to the manager in the form of a dashboard by the server, who can then view the dashboard on their smartphone to review and optimize business processes.

[0175] Prompt Sentence Examples

[0176] Analyze the operating data of factory robots and generate efficiency improvement proposals.

[0177] Data content: { "robot_id": "robot_1", "timestamp": 1672522560, "operation": "welding", "duration": 1200, "error_count": 0}

[0178] Output format: JSON

[0179] Proposal content: Introduction of RPA for automation, optimization of work steps, maintenance proposals to reduce error rates, etc.

[0180] In this way, the present invention can effectively support the management and efficiency of factory robot operations and provide real-time feedback.

[0181] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0182] Program processing flow

[0183] Step 1:

[0184] Users use specialized software to record factory robot operating times, task completion times, and error occurrences in real time.

[0185] Input: Factory robot operating status

[0186] Output: Operating time, task completion time, number of errors

[0187] Specific operation: The user starts the software and presses the "Start" button when the robot starts working and the "End" button when it finishes. If an error occurs during this process, the details of the error are also recorded.

[0188] Step 2:

[0189] The collected data is automatically sent to a server via smartphone at regular intervals (e.g., every hour).

[0190] Input: Data on operation time, task completion time, and number of errors

[0191] Output: Data sent to the server

[0192] Specific operation: Collected data is sent to the server via HTTP at regular intervals. The data is in JSON format and includes uptime and error information.

[0193] Step 3:

[0194] The server analyzes the received data and identifies the business process.

[0195] Input: Data received by the server

[0196] Output: Analysis results (identification of business processes)

[0197] What it does: The server stores the received data in a database and uses analytical algorithms to analyze the data, for example, to identify the duration, frequency, and repetitive tasks of each task. It also cleanses the data using AI models as needed.

[0198] Step 4:

[0199] The server generates efficiency suggestions based on the analysis results.

[0200] Input: Analysis results (business processes stored in the database)

[0201] Output: Efficiency proposals

[0202] Specific operation: A prompt sentence is input into the generative AI model to generate efficiency improvement proposals. An example of a prompt sentence is, "Analyze the operation data of factory robots and generate efficiency improvement proposals." The generated efficiency proposals include RPA implementation, optimization of work steps, and maintenance proposals to reduce error rates.

[0203] Step 5:

[0204] The server provides feedback of the generated efficiency proposals to the administrator (user).

[0205] Input: Efficiency proposal

[0206] Output: Feedback dashboard displayed on the administrator's smartphone

[0207] Specific operation: The server converts the generated proposals into a visually easy-to-read dashboard format and sends a notification to the user's smartphone. The user can then view the dashboard on their smartphone and review the details of the efficiency proposals.

[0208] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0209] This invention combines an emotion engine with a system that acquires and analyzes a user's work records to generate efficiency suggestions and provide feedback to the user. This system makes it possible to provide suggestions that not only improve work efficiency but also take into account the user's emotional state.

[0210] Overall system overview

[0211] This system acquires the user's work records, sends them to a server for analysis, and then uses an emotion engine to identify the user's emotional state and generate efficiency suggestions.These suggestions are then fed back to the user to optimize their work.

[0212] Data Collection Phase

[0213] When a user starts a task using a dedicated application, the start time and content are recorded. For example, "09:00 - Started checking email," "09:30 - Ended checking email," etc. In addition, the device records operation logs (keystrokes, app usage time, browser history, etc.) in the background.

[0214] The collected business record data is automatically sent from the device to the server at regular intervals (e.g., every hour), reducing the user's workload and streamlining data collection.

[0215] Data analysis phase

[0216] The server receives the business record data sent from the device and stores it in a database. It then performs preprocessing, checks for inconsistencies and missing values, and performs data cleansing, such as correcting discrepancies between start and end times.

[0217] The server then analyzes the data using analytical algorithms to identify the duration, frequency, and repetitive tasks of each task and assess potential efficiency improvements.

[0218] Emotion Engine

[0219] The system adds an emotion engine that analyzes user input and behavioral data to identify the user's emotional state. For example, it estimates the user's current emotional state (e.g., stress, fatigue, joy, etc.) from keystroke speed, speech tone, and past behavioral patterns.

[0220] The emotional state identified by the emotion engine is integrated into the server's analysis results and influences efficiency suggestions. For example, if a user is in a high-stress state, the system will suggest ways to reduce workload or become more relaxed.

[0221] Proposal of efficiency methods

[0222] Based on the analysis results and the output of the emotion engine, the server generates efficiency suggestions, which may include:

[0223] Automation using RPA: For example, automating tasks such as replying to emails according to specific templates.

[0224] Voice input: If you are taking too long to type a sentence, we suggest you use a voice input tool.

[0225] Generative AI: Suggestions for automatically generating reports from templates.

[0226] Suggesting breaks to reduce stress: Suggesting appropriate breaks depending on your emotional state.

[0227] Providing feedback

[0228] The server compiles the generated efficiency improvement proposals in the form of a dashboard. The proposals and their implementation procedures are presented to the user in a visually easy-to-read format. The server notifies the user of updates to the dashboard.

[0229] The device displays a dashboard to the user, allowing them to review the recommendations, allowing them to review their own business processes and implement the efficiency recommendations.

[0230] Implementing and reassessing efficiencies

[0231] Users can review their business processes based on the dashboard's suggestions and implement the suggested efficiency measures, such as setting up a new RPA bot or introducing a voice input tool.

[0232] The device then records the user's work activities again and transfers the new data to the server. The server then reanalyzes the data, evaluates the effectiveness of the suggestions, and makes further suggestions for improvement as necessary. This allows the user to continuously optimize their work.

[0233] Specific examples

[0234] For example, if the emotion engine detects that a user is feeling high stress while checking email, the server can suggest automating that task and suggest taking a moderate break.It can also provide instructions to set up automatic email replies using RPA, further improving work efficiency.

[0235] The processing flow will be explained below.

[0236] Step 1: Enter business records

[0237] A user uses a dedicated application to record the start and end of work, for example, "09:00 - Start checking email" and "09:30 - End checking email."

[0238] Step 2: Collecting operational logs

[0239] The device collects operation logs in the background (keystrokes, application usage time, web browser history, etc.), allowing detailed business data to be obtained.

[0240] Step 3: Send business records

[0241] The terminal automatically sends collected business record data to the server at regular intervals (e.g., every hour).

[0242] Step 4: Receiving and storing data

[0243] The server receives the business record data sent from the terminal and stores it in a database.

[0244] Step 5: Preprocessing the data

[0245] The server performs pre-processing on the data received, checking for inconsistencies and missing values ​​and cleansing the data as needed, for example, fixing discrepancies between start and end times.

[0246] Step 6: Analyze business processes

[0247] The server uses analytical algorithms to analyze the work record data, identifying the duration, frequency, and repetitive tasks of each task and identifying areas where work can be streamlined.

[0248] Step 7: Collect emotion data

[0249] The device uses an emotion engine to recognize the user's emotional state based on their input and behavioral data, such as analyzing keystroke speed and voice input tone.

[0250] Step 8: Emotion Engine Analysis

[0251] The server integrates the output of the emotion engine into the analysis results and performs an analysis that takes into account the user's emotional state (e.g., stress, fatigue, joy).

[0252] Step 9: Generate efficiency proposals

[0253] The server generates efficiency improvement proposals based on the results of business process analysis and the output of the emotion engine. Proposals include automation using RPA, use of voice input, utilization of generative artificial intelligence (AI), and break suggestions.

[0254] Step 10: Personalize your suggestions

[0255] The server customizes the suggestions to suit the user's work style and emotional state, providing the user with the most effective efficiency techniques.

[0256] Step 11: Generate the dashboard

[0257] The server compiles the generated efficiency suggestions into a visually appealing dashboard, including details of the suggestions and steps to implement them.

[0258] Step 12: Notify users

[0259] The server notifies the user of updates to the dashboard, and the user can then review the suggestions.

[0260] Step 13: View the Dashboard

[0261] The device displays a dashboard for users to review and refine efficiency recommendations, allowing them to adjust their business processes based on the recommendations.

[0262] Step 14: Implementing efficiency measures

[0263] Users can follow the dashboard's suggestions to implement specific efficiency measures, such as setting up a new RPA bot or introducing a voice input tool.

[0264] Step 15: Collect new data

[0265] The device then records the user's work activities again and transfers the new data to the server, allowing the effectiveness of the efficiency measures implemented to be evaluated.

[0266] Step 16: Reanalyze and update proposals

[0267] The server re-analyzes the data based on the new data and evaluates the effectiveness of the previous proposal. If necessary, it generates further improvement proposals and provides them as feedback to the user. This allows users to continuously optimize their operations.

[0268] Example 2

[0269] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0270] In today's business environment, improving business efficiency is an important issue. However, simply automating business processes is not enough to achieve efficiency; the user's emotional state must also be taken into consideration. In particular, users who are under high stress or fatigue may not be able to enjoy the benefits of efficiency unless appropriate suggestions are made. Therefore, it is necessary to generate efficiency suggestions that take the user's emotional state into account.

[0271] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0272] In this invention, the server includes means for acquiring a user's work records, means for transmitting the work records to the server, means for analyzing the received work records and identifying work processes in the server, means for generating efficiency suggestions based on the analysis results and emotion analysis results, means for feeding back the generated efficiency suggestions to the user, means for exchanging the feedback with the user, and means for analyzing the emotional state of the user. This makes it possible to not only improve the efficiency of the user's work processes, but also to suggest stress reduction and timely breaks according to the user's emotional state.

[0273] A "user" is an individual or organization that uses the system to carry out business.

[0274] "Business records" are log information such as the start and end times and contents of tasks performed by a user during business hours.

[0275] The "server" is a central management system that receives business records, analyzes them, generates efficiency suggestions, and provides feedback to users.

[0276] "Sentiment analysis" is the process of identifying a user's emotional state based on their input and behavioral data.

[0277] "Efficiency proposals" are proposals that show ways to automate tasks, simplify work, and reduce stress.

[0278] "Robotic Process Automation (RPA)" is a technology for automating routine business processes.

[0279] "Voice input" is a method in which a user inputs information by voice using a microphone.

[0280] "Generative artificial intelligence (AI)" is an artificial intelligence technology that automatically generates new information and suggestions based on data.

[0281] A "dashboard" is a user interface that visually displays proposal content and data status.

[0282] A "database" is a collection of data that allows a system to efficiently store, manage, and retrieve data.

[0283] "Data cleansing" is the process of correcting inconsistencies and missing values ​​to ensure data consistency and accuracy.

[0284] This invention combines a system that acquires a user's work records, analyzes them, generates efficiency suggestions, and provides feedback to the user, with an emotion engine that identifies the user's emotional state. This system can provide suggestions that not only improve work efficiency but also take the user's emotional state into consideration.

[0285] Supported Hardware and Software

[0286] The server receives the user's business record data and emotional state data, analyzes them, and generates efficiency suggestions. The server mainly operates using a database (e.g., SQL server), analysis algorithms (e.g., Python's Pandas or Scikit-learn), and emotion engines (e.g., NLP technology). The server displays the generated suggestions in a dashboard format and notifies the user.

[0287] The device collects business records and operation logs when the user performs work, and transmits the data to a server at regular intervals. The device receives user input using a dedicated application (e.g., a business record application), and records keystroke speed, application usage time, browser history, etc. in the background.

[0288] Users use a dedicated application to input the start and end times of their work and the details of their work, then confirm and implement the efficiency suggestions. The emotion engine also analyzes the user's operations, input speed, and tone of voice to identify their emotional state.

[0289] Examples of specific examples and prompts

[0290] For example, a user can enter a task record such as "09:00 - Started checking email" into the app, and the device will simultaneously record the user's typing speed and the time spent using the app. This data is automatically sent to the server, which analyzes the task record to determine what tasks the user is spending time on and the associated emotional state (e.g., stress or fatigue).

[0291] Based on the analysis results, the server generates efficiency proposals, including improvement measures such as "automating email replies with RPA," "introducing a voice input tool," and "taking breaks at appropriate times." These proposals are displayed on a dashboard so that users can check them.

[0292] Examples of prompts are:

[0293] "Generate efficiency suggestions based on the user's work record data and emotional state."

[0294] "Analyze your work processes with algorithms that offer suggestions for automating tasks and reducing stress."

[0295] These prompts allow the generative AI model to be appropriately utilized to support the user in improving work efficiency. This system is effective as a method for integrating work efficiency and emotional state management.

[0296] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0297] Step 1: Data collection

[0298] The user launches a dedicated application and enters the start and end times and details of their work. For example, they might enter "09:00 - Start checking email." In addition, the device records keystrokes, app usage time, browser history, and other operation logs in the background. The input data (work records and operation logs) is saved on the device.

[0299] Step 2: Send data

[0300] The terminal sends the collected business record data and operation logs to the server in batches at regular intervals (for example, every hour). This sending process is automated to reduce the user's workload. The input data is sent from the terminal to the server and stored in a database.

[0301] Step 3: Data storage and cleansing

[0302] The server receives data from the terminal and stores it in a database. It then checks the stored data for inconsistencies and missing values, and performs data cleansing to correct any discrepancies, such as discrepancies between start and end times. Input data (business records and operation logs) is filtered and stored as clean data.

[0303] Step 4: Data analysis

[0304] The server analyzes the clean data. The analysis identifies the time, frequency, and repetitive tasks for each task. For example, it evaluates whether employees spend 30 minutes checking email every day. The input data (clean data) is analyzed by an analytical algorithm to evaluate the possibility of improving the efficiency of business processes. The output is the business analysis results.

[0305] Step 5: Sentiment Analysis

[0306] The server uses an emotion engine to analyze the user's emotional state. For example, it can estimate whether the user is feeling stressed based on their keystroke speed, operation patterns, and past behavioral data. Similarly, when using voice input, it can also analyze emotions from the tone of their speech. The input data (operation logs and past behavioral data) is analyzed by an analysis algorithm to obtain emotion analysis results.

[0307] Step 6: Generate efficiency proposals

[0308] The server integrates the results of task analysis and emotion analysis to generate efficiency suggestions. For example, if a user is in a high-stress state, it may suggest simplifying tasks, introducing RPA tools, using voice input, or taking breaks. The input data (task analysis results and emotion analysis results) is analyzed, and efficiency suggestions are generated.

[0309] Step 7: Feedback on proposal

[0310] The server compiles the generated efficiency proposals in a dashboard format and provides feedback to the user. The dashboard displays the proposals in a visually easy-to-read format, making it easier for the user to confirm the proposals. The input data (efficiency proposals) is displayed on the dashboard and notified to the user.

[0311] Step 8: Implement and reassess efficiency

[0312] The user reviews their business processes according to the dashboard's suggestions and implements the proposed efficiency measures. For example, they set up a new RPA bot and introduce a voice input tool. The device re-records the user's new business process and sends the new data to the server. The server re-analyzes the new data, evaluates the effectiveness of the proposals, and makes further improvement proposals as necessary. The input data (new business records) is re-analyzed, and an evaluation of the effectiveness of the proposals is obtained.

[0313] (Application example 2)

[0314] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0315] Conventional work efficiency systems make efficiency improvement suggestions without considering the user's emotional state, which can result in suggestions that ignore stress and fatigue. Furthermore, there is a lack of analysis of the emotional state of factory workers and real-time work automation support, making it difficult to achieve both occupational safety and work efficiency. The purpose of this invention is to solve these problems and realize efficiency improvement suggestions and work automation that take the user's emotional state into account.

[0316] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0317] In this invention, the server includes means for acquiring a user's work records, means for transmitting the work records to the server, means for analyzing the work records received by the server and identifying work processes, means for generating efficiency improvement proposals based on the analysis results and the user's emotional state, means for feeding back the generated efficiency improvement proposals to the user, means for exchanging the feedback with the user, means for acquiring worker status data (e.g., heart rate, sweat rate), means for analyzing the worker's emotional state, means for generating work automation instructions for a work robot based on the emotional state and the analyzed data, and means for transmitting the proposed work automation instructions to the work robot and having it execute them. This enables efficiency improvement proposals that reflect the user's emotional state and real-time work automation support that corresponds to the emotional states of workers in a factory.

[0318] The "means for acquiring user's work records" refers to a device or system that has the function of recording and collecting information such as the type of work performed by the user, the start time, and the end time.

[0319] The "means for transmitting the business records to the server" refers to a device or system that includes communication functions and protocols for transferring information from the user's terminal to the data center.

[0320] The "means for analyzing the business records received on the server and identifying the business process" refers to software or hardware that has the function of processing the business record data received on the server and analyzing the business content, its progress, etc.

[0321] "Means for generating efficiency proposals based on analysis results and the user's emotional state" refers to algorithms or systems that propose methods for improving work efficiency and reducing workload based on business data and emotion analysis results.

[0322] The "means for providing feedback of the generated efficiency improvement proposals to the user" refers to an interface or device for notifying the user of the results of the efficiency improvement proposals and conveying them by display or voice.

[0323] The "means for exchanging feedback with the user" is an interface with a two-way communication function that allows the user to check the proposal content and respond or make changes as necessary.

[0324] "Means for acquiring worker status data (e.g., heart rate, sweat rate)" refers to sensors or devices that measure the worker's biometric information in real time and collect it as data.

[0325] The "means for analyzing the worker's emotional state" refers to an analytical algorithm or system for estimating the worker's emotions, such as stress and fatigue, based on collected biometric information.

[0326] The "means for generating work automation instructions for a work robot based on emotional state and analytical data" refers to software and hardware that combines emotional state and business data to create optimal work plans and automation instructions.

[0327] The "means for transmitting the proposed work automation instructions to the work robot and having it execute them" refers to the communication means and control system for sending the created automation instructions to the robot and starting the actual work.

[0328] This invention is a "factory smart assistant" system that makes suggestions based on work efficiency and the emotional state of the worker. This system acquires the user's work records, generates efficiency suggestions based on the analysis results and the worker's emotional state, and also issues instructions to a work robot to automate the work.

[0329] System configuration

[0330] 1. User Device:

[0331] Data collection function: The user device records the start and end times of work, as well as the details of the work. In addition, smart glasses are used to collect biometric information (heart rate, sweat rate, etc.) while the user is working.

[0332] Communication function: Collected data is sent to a server at regular intervals via Wi-Fi or Bluetooth.

[0333] 2. Server:

[0334] Data analysis function: The server analyzes the received business records and identifies the business processes. The analysis includes data cleansing.

[0335] Emotion engine: Analyzes the worker's biometric information and estimates their emotional state (stress, fatigue, etc.). This emotion engine uses machine learning algorithms.

[0336] Efficiency suggestion generation function: Generates efficiency and safety suggestions based on the analysis results and emotional state. Suggestions include using RPA, suggesting voice input, suggesting breaks, etc.

[0337] Work robot control function: Transmits the proposed work automation instructions to the work robot and controls the robot to perform the appropriate work.

[0338] 3. Feedback function:

[0339] User interface: The generated proposals are displayed to the user in a dashboard format, allowing them to review and modify the proposals as needed.

[0340] Data processing and calculation

[0341] Data recording and collection:

[0342] The smart glasses collect biometric information (heart rate, sweat rate) and work records of workers wearing them in real time.

[0343] Sending data:

[0344] The collected data is sent to a server via Wi-Fi or Bluetooth.

[0345] Data analysis and sentiment estimation:

[0346] The server cleanses the received data and identifies business processes using analysis algorithms. The emotion engine then estimates the emotional state based on biometric information. This is done using machine learning algorithms using Python (e.g., TENSORFLOW (registered trademark), Scikit-learn).

[0347] Generate efficiency suggestions:

[0348] Based on the results of the analysis and sentiment estimation, a generative AI model is used to generate efficiency recommendations, such as using RPA or recommending breaks.

[0349] Instructions to the working robot:

[0350] Based on the efficiency improvement proposals, we generate automation instructions for the work robots and transmit them via wireless communication. We use PLCs (Programmable Logic Controllers) and ROS (Robotics Operating System) to instruct the robots to automatically execute specific tasks.

[0351] Specific examples

[0352] For example, when a user wears smart glasses, the server performs the following analyses based on the work records and biometric data collected from the start of work in the morning until the end of the workday:

[0353] The worker's heart rate exceeded 90 bpm between 10:00 and 11:00, indicating that he was in a state of high stress.

[0354] Based on work records and emotional state, the server suggests taking a break and leaving repetitive tasks to a robot.

[0355] Example prompt for a generative AI model:

[0356] Generate efficiency suggestions based on the following work records and sensor data:

[0357] Work record: Start time 09:00, Work content Maintenance

[0358] Sensor data: Heart rate 90, slight sweating

[0359] Proposal details:

[0360] High stress: Proposal to delegate repetitive tasks to robots

[0361] If fatigue is detected: Advise appropriate rest

[0362] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0363] Step 1:

[0364] The device acquires the user's work records. When the user starts a task, the device records the start time, work content, and end time of the task. It also collects biometric information (heart rate, sweat rate, etc.) in real time through the smart glasses. The input is the user's operation log and sensor data, and the output is the recorded work data and biometric information. Specifically, the device acquires data such as heart rate and sweat rate from the smart glasses' sensors and stores it in the device's memory.

[0365] Step 2:

[0366] The device sends the collected business record data and biometric information to the server at regular intervals. The input is the recorded business data and biometric information, and the output is the data sent to the server. Specifically, the device executes a process to send data to the server via Wi-Fi or Bluetooth communication.

[0367] Step 3:

[0368] The server analyzes the received business record data. The input is the business data and biometric information sent from the terminal, and the output is the analyzed business process data. Specific operations include data cleansing, time series analysis, and frequency analysis to identify task times, frequency, and repetitive tasks.

[0369] Step 4:

[0370] The server uses an emotion engine to analyze the worker's emotional state. The input is biometric information (heart rate, sweat rate, etc.), and the output is an estimated emotional state (stress, fatigue, etc.). Specifically, it uses a machine learning algorithm to classify and analyze the emotional state based on the biometric information.

[0371] Step 5:

[0372] The server generates efficiency improvement proposals based on the analysis results and emotional state. The inputs are business process data and emotional state information, and the output is efficiency improvement proposals. Specifically, the server combines business data and emotional data, inputs prompts to the generative AI model, and generates proposals.

[0373] Step 6:

[0374] The server then feeds back the generated efficiency suggestions to the user. The input is the efficiency suggestions, and the output is a feedback notification to the user. Specifically, the server visually displays the suggestions on a dashboard and sends a notification to the user's device.

[0375] Step 7:

[0376] The server generates work automation instructions for the work robot based on the emotional state and analysis data. The input is business process data and emotional state information, and the output is automation instructions. Specifically, if the emotional state is highly stressful, the server generates and sends instructions to delegate repetitive work to the robot.

[0377] Step 8:

[0378] The robot receives and executes the automation instructions proposed by the server. The input is the automation instructions from the server, and the output is the actual work performed. In terms of specific operations, the robot performs appropriate actions based on the received instructions to complete the work.

[0379] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0380] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0381] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0382] [Second embodiment]

[0383] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0384] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0385] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0386] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0387] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0388] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0389] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0390] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0391] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0392] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0393] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0394] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0395] The present invention provides a system for efficient business management and proposal making. Specific embodiments will be described below.

[0396] Overall system overview

[0397] In this system, users collect daily work records and send them to a server, which then analyzes the data, generates efficiency improvement proposals, and provides feedback to the users.

[0398] Data Collection Phase

[0399] When a user starts a task using a dedicated application, the start time and content are recorded. For example, "09:00 - Started checking email" or "09:30 - Ended checking email." The device also records activity logs during the task (keystrokes, application usage time, browser history, etc.).

[0400] The collected business record data is automatically sent from the terminal to the server at regular intervals (e.g., every hour), allowing data to be collected without any user intervention.

[0401] Data analysis phase

[0402] The server receives business record data sent from the terminal. After receiving it, it stores it in a database and first performs pre-processing. In pre-processing, it checks for inconsistencies and missing values ​​and performs data cleansing. For example, if there is a discrepancy between the start time and end time, this is corrected.

[0403] The server then analyzes the data using analytical algorithms to identify the duration, frequency, and repetitiveness of each task, such as the amount of time spent checking email or writing reports.

[0404] Proposal of efficiency methods

[0405] Based on the analysis, the server generates efficiency suggestions, which may include:

[0406] Automation using RPA: For example, a proposal to automate tasks such as replying to emails according to specific templates.

[0407] Voice input: If you are taking too long to type a sentence, we suggest you use a voice input tool.

[0408] Generative AI: Suggestions for automatically generating reports from templates.

[0409] These suggestions are personalized to suit your work style.

[0410] Providing feedback

[0411] The server compiles the generated efficiency proposals in the form of a dashboard. The proposals and their implementation procedures are presented to the user in a visually easy-to-read format. The server also notifies the user that the dashboard has been updated and provides feedback.

[0412] The device displays the dashboard for users to review and refine their own business processes, and implement the proposed improvements.

[0413] Implementing and reassessing efficiencies

[0414] The user reviews their business processes based on the dashboard and implements the proposed efficiency measures, such as setting up a new RPA bot or introducing a voice input tool. The device then records the user's business actions and transfers them to the server.

[0415] The server re-analyzes the data based on the new data, evaluates the effectiveness of the proposals, and makes further improvement proposals as needed, allowing users to continuously optimize their operations.

[0416] The processing flow will be explained below.

[0417] Step 1: User Business Records

[0418] When a user starts a task, they use a dedicated application to record the start and end of the task. For example, "09:00 - Start checking email" and "09:30 - End checking email." At that time, the device records an operation log (keystrokes, app usage time, browser history, etc.) in the background.

[0419] Step 2: Sending data

[0420] The terminal automatically sends collected business record data to the server at regular intervals (e.g., every hour), reducing the user's workload and making data collection more efficient.

[0421] Step 3: Receiving the data

[0422] The server receives the business record data sent from the terminal and stores it in a database, at which point it checks the consistency and integrity of the data.

[0423] Step 4: Preprocessing the data

[0424] The server pre-processes the data it receives, checking for inconsistencies and missing values ​​and performing data cleansing, for example correcting discrepancies between the start and end times of records.

[0425] Step 5: Analyze business processes

[0426] The server analyzes the data using analytical algorithms, identifying the duration, frequency, and repetitiveness of each task and assessing potential for efficiency improvements. For example, it identifies that checking email takes an average of 30 minutes, but involves many manual steps.

[0427] Step 6: Generate efficiency proposals

[0428] The server generates efficiency proposals based on the analysis results, including automation using RPA, the use of voice input, and the use of generative artificial intelligence (AI).

[0429] Step 7: Personalize your suggestions

[0430] The server customizes efficiency suggestions for each user based on their work style and past data, providing more practical suggestions.

[0431] Step 8: Generate a dashboard as feedback

[0432] The server compiles the generated efficiency suggestions into a visually appealing dashboard and prepares feedback with details of the suggestions and implementation steps.

[0433] Step 9: Notify users

[0434] The server notifies the user of updates to the dashboard, after which the user can check the detailed proposals.

[0435] Step 10: View the dashboard

[0436] The device will then present the user with a dashboard where they can review the recommendations, and the user will receive feedback to help them implement specific efficiency measures.

[0437] Step 11: Implement efficiency techniques

[0438] Users can follow the dashboard's suggestions to review their business processes and implement the suggested efficiency measures, such as setting up a new RPA bot or introducing a voice input tool.

[0439] Step 12: Collect new data

[0440] The device then records the user's work activities again and transfers the new data to the server, allowing the effectiveness of the efficiency improvement measures to be evaluated.

[0441] Step 13: Reanalyze and update proposals

[0442] The server reanalyzes the data based on the new data, evaluates the effectiveness of previous suggestions, and makes new suggestions for improvement as needed, allowing users to continuously optimize their operations.

[0443] Example 1

[0444] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0445] Modern business environments demand efficient business management. It is particularly important to automate the entire process, from acquiring business records to analyzing data and proposing efficiency improvements, to reduce user workload. However, previous systems lacked sufficient data preprocessing, cleansing, and efficient feedback, placing a heavy burden on users. Furthermore, efficiency improvements often lacked practicality, as they were not adapted to individual business styles. The objective of this invention is to solve these problems and provide a more effective business management system.

[0446] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0447] In this invention, the server includes means for preprocessing a user's business records and cleansing the data, means for analyzing the preprocessed data and identifying business processes, and means for providing generated efficiency suggestions as feedback to the user in the form of a dashboard. This improves the accuracy of the analysis of business records and the practicality of the efficient feedback and suggestions to the user, thereby enabling business optimization.

[0448] "User" means an individual or organization that uses the System to enter and review business records.

[0449] "Business records" refers to data such as the start time, end time, and content of business operations entered by the user.

[0450] "Server" refers to a computer system that receives business record data and performs pre-processing, analysis, and generation of efficiency recommendations.

[0451] "Preprocessing" refers to the process of checking for inconsistencies and missing values ​​in received business record data and cleansing the data.

[0452] "Data cleansing" refers to the process of correcting inconsistencies and missing values ​​in data and preparing it for analysis.

[0453] "Analysis" refers to the process of identifying business processes based on preprocessed data and analyzing the time, frequency, repetitive tasks, etc. of each task.

[0454] "Efficiency proposals" refer to proposing methods and tools to improve business efficiency based on the analysis results.

[0455] "Dashboard format" refers to an interface format that provides information to users in a visually easy-to-read format.

[0456] "Feedback" refers to the process of providing the generated efficiency proposals to the user, encouraging the implementation of the proposals through interaction with the user, and evaluating their effectiveness.

[0457] A "prompt" refers to a textual instruction that is input to a generative AI model.

[0458] A "generative AI model" refers to an algorithm that uses artificial intelligence to automatically generate efficiency suggestions based on prompt text.

[0459] "Robotic Process Automation (RPA)" refers to a software tool for automating routine tasks.

[0460] "Voice input" refers to a technique that allows a user to input voice as text data.

[0461] "Automated report generation" refers to the process of using generative AI models or other tools to automatically generate standard reports.

[0462] This invention provides a system for efficient business management and proposals. Specific embodiments are described below. In this system, a user acquires daily business records and sends them to a server, which analyzes the data, generates efficiency proposals, and provides feedback to the user.

[0463] Data Collection Phase

[0464] When a user starts a task using a dedicated application, the start time and task content are recorded. For example, it is recorded as "09:00 - Started checking email" and "09:30 - Ended checking email." The device also records activity logs during the task (keystrokes, application usage time, browser history, etc.). This information is automatically sent from the device to the server at regular intervals (e.g., every hour). This procedure allows data to be collected without the user having to perform any special operations.

[0465] Data analysis phase

[0466] The server receives the business record data sent from the device and stores it in a database. It then performs preprocessing, checking for inconsistencies and missing values ​​and cleansing the data. For example, it corrects discrepancies between start and end times. It then uses analytical algorithms to analyze the data and identify the duration, frequency, and repetitive tasks of each task. For example, it identifies the time spent checking email or writing reports.

[0467] Generate efficiency proposals

[0468] Based on the analysis, the server generates efficiency suggestions, which may include:

[0469] Automation using RPA: For example, a proposal to automate tasks such as replying to emails according to specific templates.

[0470] Voice input: If you are taking too long to type a sentence, we suggest you use a voice input tool.

[0471] Generative AI: Suggestions for automatically generating reports from templates.

[0472] These suggestions are personalized to suit the user's work style. For example, suggestions might include, "Use the RPA task automation tool to reduce the time spent checking emails," or "Introduce a voice input tool to create reports."

[0473] Providing feedback

[0474] The server compiles the generated efficiency improvement proposals in the form of a dashboard. The proposals and their implementation procedures are provided to the user in a visually easy-to-read format. The server notifies the user that the dashboard has been updated, and the contents of the dashboard are displayed on the user's device. The user can then review their own business processes and implement the proposed efficiency improvements.

[0475] Implementing and reassessing efficiencies

[0476] Users review their business processes based on the dashboard and implement the proposed efficiency improvements. For example, they can set up a new RPA bot or introduce a voice input tool. The device then records the user's work actions again and transfers them to the server. The server then reanalyzes the new data, evaluates the effectiveness of the proposals, and makes further improvement proposals as necessary. This allows users to continuously optimize their work.

[0477] Specific Examples

[0478] Example prompt: Analyze the following work log and generate efficiency suggestions: "09:00 - Start checking email", "09:30 - Finish checking email", "10:00 - Start writing report".

[0479] This prompt allows the generative AI model to generate efficiency suggestions based on the input data.

[0480] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0481] Step 1:

[0482] When a user starts a task by launching a dedicated application, the start time and task content are recorded. The user inputs something like "09:00 - Start checking email." Based on this input, the device generates data on the task start time and content, and this data is temporarily stored in memory.

[0483] Step 2:

[0484] While the user continues to work, the device records activity logs such as keystrokes, application usage time, and browser history. Specifically, software built into the device monitors the user's keyboard input and mouse clicks and records this information in a log file. The log file contains timestamps and the details of the actions taken.

[0485] Step 3:

[0486] The terminal automatically sends recorded business data and operation logs to the server at regular intervals (e.g., every hour). A specific API endpoint is used for sending. The input is business data and operation logs, and the output is a JSON-formatted data packet containing this data sent via the API.

[0487] Step 4:

[0488] The server stores the received business data and operation logs in a database. After storing, the server performs preprocessing, checks for inconsistencies and missing values, and cleanses the data. Specifically, it queries the data from the database and corrects inconsistent time information or missing information. The input is the received data, and the output is the cleansed data.

[0489] Step 5:

[0490] The server analyzes the cleansed data. It uses an analysis algorithm to identify the duration, frequency, and repetitive tasks of each task. Specifically, the server aggregates the data, counts frequency, and analyzes whether the same tasks are repeated. The input for the analysis is the cleansed data, and the output is an analysis of the duration, frequency, and repetitive tasks for each task.

[0491] Step 6:

[0492] The server generates efficiency improvement proposals based on the analysis results. Using a generative AI model, specific prompts are input and efficiency proposals are generated. For example, a specific proposal such as "Please set up RPA to reduce the time spent checking emails" is generated. The inputs are the analysis results and the prompt text, and the output is the text of the efficiency proposal.

[0493] Step 7:

[0494] The server compiles the generated efficiency suggestions in the form of a dashboard and provides it to the user. The server then sends a notification to the user that the dashboard has been updated, and the device displays the dashboard content. Specifically, the dashboard generation algorithm organizes the suggestions in a visually easy-to-read format, and the notification system notifies the user of updates. The efficiency suggestions are input, and a dashboard and notification are generated as output.

[0495] Step 8:

[0496] Users review their business processes based on the dashboard and implement the proposed efficiency measures. Specifically, users configure a new RPA bot and install a voice input tool, which improves business efficiency. The input is the efficiency suggestions on the dashboard, and the output is the implemented efficiency measures.

[0497] Step 9:

[0498] The device then records the user's work activities again and transfers them to the server. The server then reanalyzes the new data and evaluates the effectiveness of the proposals. If necessary, it makes further improvement proposals. The input is updated work data, and the output is evaluation results and new efficiency proposals. This allows the user to continuously optimize their work.

[0499] (Application example 1)

[0500] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0501] Conventional factory robot operation management systems lacked the functionality to automatically generate and provide feedback on efficiency and optimization proposals. This made it difficult for factory managers to grasp the robot's operating status in real time and operate efficiently. In particular, the lack of a means to effectively manage robot operating hours, task completion times, and the number of errors made it difficult to quickly make appropriate efficiency proposals.

[0502] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0503] In this invention, the server includes: means for acquiring user business records; means for transmitting the business records to the server; means for analyzing the business records received by the server and identifying business processes; means for generating efficiency improvement proposals based on the analysis results; means for feeding back the generated efficiency improvement proposals to the user; means for exchanging the feedback with the user; means for recording the operating time, task completion time, and number of error occurrences of robots in the factory; means for collecting the recorded data and transmitting it to the server at regular intervals; means for generating efficiency improvement proposals for the factory robots based on the collected data; and means for feeding back the robot proposals to a manager. This makes it possible to automate business management and efficiency improvement proposals for factory robots and provide managers with feedback for effective business operations in real time.

[0504] Definitions of important words

[0505] "User" means any individual or legal entity that uses the System and obtains Business Records.

[0506] "Business records" are data related to a user's business, and include information such as operating hours, task completion times, and the number of errors that have occurred.

[0507] "Server" means a central processing unit that receives, analyzes, stores, and provides feedback on data on the cloud or on-premise.

[0508] The "analysis means" refers to algorithms or software that analyzes the business record data received by the server and identifies the business process.

[0509] "Efficiency proposals" are proposals for optimization based on the duration, frequency, and repetitive tasks of work, generated using analytical tools.

[0510] The "feedback means" is a means for providing the generated efficiency improvement proposals to the user, and for reviewing and optimizing the business.

[0511] A "factory robot" is an automated mechanical device that performs various tasks in a factory.

[0512] "Operating time" refers to data that indicates the total amount of time that a factory robot is actually active.

[0513] "Task completion time" refers to the time it takes to complete a particular task or operation.

[0514] "Number of errors" refers to data that counts the number of errors and breakdowns that occur when a factory robot is working.

[0515] The "collection means" refers to the hardware and software used to collect business record data from factory robots at regular intervals and send it to a server.

[0516] An "administrator" is a person or role that monitors the operation of factory robots and optimizes business operations based on efficiency suggestions.

[0517] MODE FOR CARRYING OUT THE INVENTION

[0518] The present invention is a system aimed at managing the work of factory robots and improving their efficiency. Specifically, it collects users' work records, analyzes them on a server, generates efficiency proposals, and provides feedback.

[0519] Overall system configuration

[0520] The system mainly includes the following means:

[0521] 1. Data collection method: The user records the operating time of the factory robot, the time to complete a task, and the number of errors that occur.

[0522] 2. Data transmission means: The collected business record data is automatically transmitted from the terminal to the server at regular intervals.

[0523] 3. Data analysis means: The server analyzes the received business record data, identifies the business process, and generates efficiency proposals based on the analysis results.

[0524] 4. Feedback means: The generated efficiency proposals are fed back to the administrator (user) by the server and displayed in the form of a visual dashboard.

[0525] Data collection methods

[0526] Users can use dedicated software to obtain the work records of factory robots. This software has the function of recording the robot's operating time, task completion time, and number of errors in real time. For example, the operating time (10:00 - 10:30) and number of errors (5) of a welding robot can be recorded.

[0527] Data transmission method

[0528] The collected data is sent to a server at regular intervals (e.g., every hour) via the smartphone, allowing data to be collected and transferred to the server without any user intervention.

[0529] Data Analysis Methods

[0530] The server analyzes the received data using algorithms to identify the duration, frequency, and repetitive tasks of tasks (for example, how many times a particular task is repeated per day and how long it takes), and uses generative AI models to create prompts that generate efficiency suggestions.

[0531] Efficiency proposal generation method

[0532] The server uses generative AI models to generate efficiency recommendations, including the adoption of robotic process automation (RPA), voice input, and generative artificial intelligence (AI). For example, it might suggest the adoption of RPA to automate a specific welding task.

[0533] Feedback Methods

[0534] The generated efficiency proposals are fed back to the manager in the form of a dashboard by the server, who can then view the dashboard on their smartphone to review and optimize business processes.

[0535] Prompt Sentence Examples

[0536] Analyze the operating data of factory robots and generate efficiency improvement proposals.

[0537] Data content: { "robot_id": "robot_1", "timestamp": 1672522560, "operation": "welding", "duration": 1200, "error_count": 0}

[0538] Output format: JSON

[0539] Proposal content: Introduction of RPA for automation, optimization of work steps, maintenance proposals to reduce error rates, etc.

[0540] In this way, the present invention can effectively support the management and efficiency of factory robot operations and provide real-time feedback.

[0541] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0542] Program processing flow

[0543] Step 1:

[0544] Users use specialized software to record factory robot operating times, task completion times, and error occurrences in real time.

[0545] Input: Factory robot operating status

[0546] Output: Operating time, task completion time, number of errors

[0547] Specific operation: The user starts the software and presses the "Start" button when the robot starts working and the "End" button when it finishes. If an error occurs during this process, the details of the error are also recorded.

[0548] Step 2:

[0549] The collected data is automatically sent to a server via smartphone at regular intervals (e.g., every hour).

[0550] Input: Data on operation time, task completion time, and number of errors

[0551] Output: Data sent to the server

[0552] Specific operation: Collected data is sent to the server via HTTP at regular intervals. The data is in JSON format and includes uptime and error information.

[0553] Step 3:

[0554] The server analyzes the received data and identifies the business process.

[0555] Input: Data received by the server

[0556] Output: Analysis results (identification of business processes)

[0557] What it does: The server stores the received data in a database and uses analytical algorithms to analyze the data, for example, to identify the duration, frequency, and repetitive tasks of each task. It also cleanses the data using AI models as needed.

[0558] Step 4:

[0559] The server generates efficiency suggestions based on the analysis results.

[0560] Input: Analysis results (business processes stored in the database)

[0561] Output: Efficiency proposals

[0562] Specific operation: A prompt sentence is input into the generative AI model to generate efficiency improvement proposals. An example of a prompt sentence is, "Analyze the operation data of factory robots and generate efficiency improvement proposals." The generated efficiency proposals include RPA implementation, optimization of work steps, and maintenance proposals to reduce error rates.

[0563] Step 5:

[0564] The server provides feedback of the generated efficiency proposals to the administrator (user).

[0565] Input: Efficiency proposal

[0566] Output: Feedback dashboard displayed on the administrator's smartphone

[0567] Specific operation: The server converts the generated proposals into a visually easy-to-read dashboard format and sends a notification to the user's smartphone. The user can then view the dashboard on their smartphone and review the details of the efficiency proposals.

[0568] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0569] This invention combines an emotion engine with a system that acquires and analyzes a user's work records to generate efficiency suggestions and provide feedback to the user. This system makes it possible to provide suggestions that not only improve work efficiency but also take into account the user's emotional state.

[0570] Overall system overview

[0571] This system acquires the user's work records, sends them to a server for analysis, and then uses an emotion engine to identify the user's emotional state and generate efficiency suggestions.These suggestions are then fed back to the user to optimize their work.

[0572] Data Collection Phase

[0573] When a user starts a task using a dedicated application, the start time and content are recorded. For example, "09:00 - Started checking email," "09:30 - Ended checking email," etc. In addition, the device records operation logs (keystrokes, app usage time, browser history, etc.) in the background.

[0574] The collected business record data is automatically sent from the device to the server at regular intervals (e.g., every hour), reducing the user's workload and streamlining data collection.

[0575] Data analysis phase

[0576] The server receives the business record data sent from the device and stores it in a database. It then performs preprocessing, checks for inconsistencies and missing values, and performs data cleansing, such as correcting discrepancies between start and end times.

[0577] The server then analyzes the data using analytical algorithms to identify the duration, frequency, and repetitive tasks of each task and assess potential efficiency improvements.

[0578] Emotion Engine

[0579] The system adds an emotion engine that analyzes user input and behavioral data to identify the user's emotional state. For example, it estimates the user's current emotional state (e.g., stress, fatigue, joy, etc.) from keystroke speed, speech tone, and past behavioral patterns.

[0580] The emotional state identified by the emotion engine is integrated into the server's analysis results and influences efficiency suggestions. For example, if a user is in a high-stress state, the system will suggest ways to reduce workload or become more relaxed.

[0581] Proposal of efficiency methods

[0582] Based on the analysis results and the output of the emotion engine, the server generates efficiency suggestions, which may include:

[0583] Automation using RPA: For example, automating tasks such as replying to emails according to specific templates.

[0584] Voice input: If you are taking too long to type a sentence, we suggest you use a voice input tool.

[0585] Generative AI: Suggestions for automatically generating reports from templates.

[0586] Suggesting breaks to reduce stress: Suggesting appropriate breaks depending on your emotional state.

[0587] Providing feedback

[0588] The server compiles the generated efficiency improvement proposals in the form of a dashboard. The proposals and their implementation procedures are presented to the user in a visually easy-to-read format. The server notifies the user of updates to the dashboard.

[0589] The device displays a dashboard to the user, allowing them to review the recommendations, allowing them to review their own business processes and implement the efficiency recommendations.

[0590] Implementing and reassessing efficiencies

[0591] Users can review their business processes based on the dashboard's suggestions and implement the suggested efficiency measures, such as setting up a new RPA bot or introducing a voice input tool.

[0592] The device then records the user's work activities again and transfers the new data to the server. The server then reanalyzes the data, evaluates the effectiveness of the suggestions, and makes further suggestions for improvement as necessary. This allows the user to continuously optimize their work.

[0593] Specific examples

[0594] For example, if the emotion engine detects that a user is feeling high stress while checking email, the server can suggest automating that task and suggest taking a moderate break.It can also provide instructions to set up automatic email replies using RPA, further improving work efficiency.

[0595] The processing flow will be explained below.

[0596] Step 1: Enter business records

[0597] A user uses a dedicated application to record the start and end of work, for example, "09:00 - Start checking email" and "09:30 - End checking email."

[0598] Step 2: Collecting operational logs

[0599] The device collects operation logs in the background (keystrokes, application usage time, web browser history, etc.), allowing detailed business data to be obtained.

[0600] Step 3: Send business records

[0601] The terminal automatically sends collected business record data to the server at regular intervals (e.g., every hour).

[0602] Step 4: Receiving and storing data

[0603] The server receives the business record data sent from the terminal and stores it in a database.

[0604] Step 5: Preprocessing the data

[0605] The server performs pre-processing on the data received, checking for inconsistencies and missing values ​​and cleansing the data as needed, for example, fixing discrepancies between start and end times.

[0606] Step 6: Analyze business processes

[0607] The server uses analytical algorithms to analyze the work record data, identifying the duration, frequency, and repetitive tasks of each task and identifying areas where work can be streamlined.

[0608] Step 7: Collect emotion data

[0609] The device uses an emotion engine to recognize the user's emotional state based on their input and behavioral data, such as analyzing keystroke speed and voice input tone.

[0610] Step 8: Emotion Engine Analysis

[0611] The server integrates the output of the emotion engine into the analysis results and performs an analysis that takes into account the user's emotional state (e.g., stress, fatigue, joy).

[0612] Step 9: Generate efficiency proposals

[0613] The server generates efficiency improvement proposals based on the results of business process analysis and the output of the emotion engine. Proposals include automation using RPA, use of voice input, utilization of generative artificial intelligence (AI), and break suggestions.

[0614] Step 10: Personalize your suggestions

[0615] The server customizes the suggestions to suit the user's work style and emotional state, providing the user with the most effective efficiency techniques.

[0616] Step 11: Generate the dashboard

[0617] The server compiles the generated efficiency suggestions into a visually appealing dashboard, including details of the suggestions and steps to implement them.

[0618] Step 12: Notify users

[0619] The server notifies the user of updates to the dashboard, and the user can then review the suggestions.

[0620] Step 13: View the Dashboard

[0621] The device displays a dashboard for users to review and refine efficiency recommendations, allowing them to adjust their business processes based on the recommendations.

[0622] Step 14: Implementing efficiency measures

[0623] Users can follow the dashboard's suggestions to implement specific efficiency measures, such as setting up a new RPA bot or introducing a voice input tool.

[0624] Step 15: Collect new data

[0625] The device then records the user's work activities again and transfers the new data to the server, allowing the effectiveness of the efficiency measures implemented to be evaluated.

[0626] Step 16: Reanalyze and update proposals

[0627] The server re-analyzes the data based on the new data and evaluates the effectiveness of the previous proposal. If necessary, it generates further improvement proposals and provides them as feedback to the user. This allows users to continuously optimize their operations.

[0628] Example 2

[0629] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0630] In today's business environment, improving business efficiency is an important issue. However, simply automating business processes is not enough to achieve efficiency; the user's emotional state must also be taken into consideration. In particular, users who are under high stress or fatigue may not be able to enjoy the benefits of efficiency unless appropriate suggestions are made. Therefore, it is necessary to generate efficiency suggestions that take the user's emotional state into account.

[0631] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0632] In this invention, the server includes means for acquiring a user's work records, means for transmitting the work records to the server, means for analyzing the received work records and identifying work processes in the server, means for generating efficiency suggestions based on the analysis results and emotion analysis results, means for feeding back the generated efficiency suggestions to the user, means for exchanging the feedback with the user, and means for analyzing the emotional state of the user. This makes it possible to not only improve the efficiency of the user's work processes, but also to suggest stress reduction and timely breaks according to the user's emotional state.

[0633] A "user" is an individual or organization that uses the system to carry out business.

[0634] "Business records" are log information such as the start and end times and contents of tasks performed by a user during business hours.

[0635] The "server" is a central management system that receives business records, analyzes them, generates efficiency suggestions, and provides feedback to users.

[0636] "Sentiment analysis" is the process of identifying a user's emotional state based on their input and behavioral data.

[0637] "Efficiency proposals" are proposals that show ways to automate tasks, simplify work, and reduce stress.

[0638] "Robotic Process Automation (RPA)" is a technology for automating routine business processes.

[0639] "Voice input" is a method in which a user inputs information by voice using a microphone.

[0640] "Generative artificial intelligence (AI)" is an artificial intelligence technology that automatically generates new information and suggestions based on data.

[0641] A "dashboard" is a user interface that visually displays proposal content and data status.

[0642] A "database" is a collection of data that allows a system to efficiently store, manage, and retrieve data.

[0643] "Data cleansing" is the process of correcting inconsistencies and missing values ​​to ensure data consistency and accuracy.

[0644] This invention combines a system that acquires a user's work records, analyzes them, generates efficiency suggestions, and provides feedback to the user, with an emotion engine that identifies the user's emotional state. This system can provide suggestions that not only improve work efficiency but also take the user's emotional state into consideration.

[0645] Supported Hardware and Software

[0646] The server receives the user's business record data and emotional state data, analyzes them, and generates efficiency suggestions. The server mainly operates using a database (e.g., SQL server), analysis algorithms (e.g., Python's Pandas or Scikit-learn), and emotion engines (e.g., NLP technology). The server displays the generated suggestions in a dashboard format and notifies the user.

[0647] The device collects business records and operation logs when the user performs work, and transmits the data to a server at regular intervals. The device receives user input using a dedicated application (e.g., a business record application), and records keystroke speed, application usage time, browser history, etc. in the background.

[0648] Users use a dedicated application to input the start and end times of their work and the details of their work, then confirm and implement the efficiency suggestions. The emotion engine also analyzes the user's operations, input speed, and tone of voice to identify their emotional state.

[0649] Examples of specific examples and prompts

[0650] For example, a user can enter a task record such as "09:00 - Started checking email" into the app, and the device will simultaneously record the user's typing speed and the time spent using the app. This data is automatically sent to the server, which analyzes the task record to determine what tasks the user is spending time on and the associated emotional state (e.g., stress or fatigue).

[0651] Based on the analysis results, the server generates efficiency proposals, including improvement measures such as "automating email replies with RPA," "introducing a voice input tool," and "taking breaks at appropriate times." These proposals are displayed on a dashboard so that users can check them.

[0652] Examples of prompts are:

[0653] "Generate efficiency suggestions based on the user's work record data and emotional state."

[0654] "Analyze your work processes with algorithms that offer suggestions for automating tasks and reducing stress."

[0655] These prompts allow the generative AI model to be appropriately utilized to support the user in improving work efficiency. This system is effective as a method for integrating work efficiency and emotional state management.

[0656] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0657] Step 1: Data collection

[0658] The user launches a dedicated application and enters the start and end times and details of their work. For example, they might enter "09:00 - Start checking email." In addition, the device records keystrokes, app usage time, browser history, and other operation logs in the background. The input data (work records and operation logs) is saved on the device.

[0659] Step 2: Send data

[0660] The terminal sends the collected business record data and operation logs to the server in batches at regular intervals (for example, every hour). This sending process is automated to reduce the user's workload. The input data is sent from the terminal to the server and stored in a database.

[0661] Step 3: Data storage and cleansing

[0662] The server receives data from the terminal and stores it in a database. It then checks the stored data for inconsistencies and missing values, and performs data cleansing to correct any discrepancies, such as discrepancies between start and end times. Input data (business records and operation logs) is filtered and stored as clean data.

[0663] Step 4: Data analysis

[0664] The server analyzes the clean data. The analysis identifies the time, frequency, and repetitive tasks for each task. For example, it evaluates whether employees spend 30 minutes checking email every day. The input data (clean data) is analyzed by an analytical algorithm to evaluate the possibility of improving the efficiency of business processes. The output is the business analysis results.

[0665] Step 5: Sentiment Analysis

[0666] The server uses an emotion engine to analyze the user's emotional state. For example, it can estimate whether the user is feeling stressed based on their keystroke speed, operation patterns, and past behavioral data. Similarly, when using voice input, it can also analyze emotions from the tone of their speech. The input data (operation logs and past behavioral data) is analyzed by an analysis algorithm to obtain emotion analysis results.

[0667] Step 6: Generate efficiency proposals

[0668] The server integrates the results of task analysis and emotion analysis to generate efficiency suggestions. For example, if a user is in a high-stress state, it may suggest simplifying tasks, introducing RPA tools, using voice input, or taking breaks. The input data (task analysis results and emotion analysis results) is analyzed, and efficiency suggestions are generated.

[0669] Step 7: Feedback on proposal

[0670] The server compiles the generated efficiency proposals in a dashboard format and provides feedback to the user. The dashboard displays the proposals in a visually easy-to-read format, making it easier for the user to confirm the proposals. The input data (efficiency proposals) is displayed on the dashboard and notified to the user.

[0671] Step 8: Implement and reassess efficiency

[0672] The user reviews their business processes according to the dashboard's suggestions and implements the proposed efficiency measures. For example, they set up a new RPA bot and introduce a voice input tool. The device re-records the user's new business process and sends the new data to the server. The server re-analyzes the new data, evaluates the effectiveness of the proposals, and makes further improvement proposals as necessary. The input data (new business records) is re-analyzed, and an evaluation of the effectiveness of the proposals is obtained.

[0673] (Application example 2)

[0674] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0675] Conventional work efficiency systems make efficiency improvement suggestions without considering the user's emotional state, which can result in suggestions that ignore stress and fatigue. Furthermore, there is a lack of analysis of the emotional state of factory workers and real-time work automation support, making it difficult to achieve both occupational safety and work efficiency. The purpose of this invention is to solve these problems and realize efficiency improvement suggestions and work automation that take the user's emotional state into account.

[0676] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0677] In this invention, the server includes means for acquiring a user's work records, means for transmitting the work records to the server, means for analyzing the work records received by the server and identifying work processes, means for generating efficiency improvement proposals based on the analysis results and the user's emotional state, means for feeding back the generated efficiency improvement proposals to the user, means for exchanging the feedback with the user, means for acquiring worker status data (e.g., heart rate, sweat rate), means for analyzing the worker's emotional state, means for generating work automation instructions for a work robot based on the emotional state and the analyzed data, and means for transmitting the proposed work automation instructions to the work robot and having it execute them. This enables efficiency improvement proposals that reflect the user's emotional state and real-time work automation support that corresponds to the emotional states of workers in a factory.

[0678] The "means for acquiring user's work records" refers to a device or system that has the function of recording and collecting information such as the type of work performed by the user, the start time, and the end time.

[0679] The "means for transmitting the business records to the server" refers to a device or system that includes communication functions and protocols for transferring information from the user's terminal to the data center.

[0680] The "means for analyzing the business records received on the server and identifying the business process" refers to software or hardware that has the function of processing the business record data received on the server and analyzing the business content, its progress, etc.

[0681] "Means for generating efficiency proposals based on analysis results and the user's emotional state" refers to algorithms or systems that propose methods for improving work efficiency and reducing workload based on business data and emotion analysis results.

[0682] The "means for providing feedback of the generated efficiency improvement proposals to the user" refers to an interface or device for notifying the user of the results of the efficiency improvement proposals and conveying them by display or voice.

[0683] The "means for exchanging feedback with the user" is an interface with a two-way communication function that allows the user to check the proposal content and respond or make changes as necessary.

[0684] "Means for acquiring worker status data (e.g., heart rate, sweat rate)" refers to sensors or devices that measure the worker's biometric information in real time and collect it as data.

[0685] The "means for analyzing the worker's emotional state" refers to an analytical algorithm or system for estimating the worker's emotions, such as stress and fatigue, based on collected biometric information.

[0686] The "means for generating work automation instructions for a work robot based on emotional state and analytical data" refers to software and hardware that combines emotional state and business data to create optimal work plans and automation instructions.

[0687] The "means for transmitting the proposed work automation instructions to the work robot and having it execute them" refers to the communication means and control system for sending the created automation instructions to the robot and starting the actual work.

[0688] This invention is a "factory smart assistant" system that makes suggestions based on work efficiency and the emotional state of the worker. This system acquires the user's work records, generates efficiency suggestions based on the analysis results and the worker's emotional state, and also issues instructions to a work robot to automate the work.

[0689] System configuration

[0690] 1. User Device:

[0691] Data collection function: The user device records the start and end times of work, as well as the details of the work. In addition, smart glasses are used to collect biometric information (heart rate, sweat rate, etc.) while the user is working.

[0692] Communication function: Collected data is sent to a server at regular intervals via Wi-Fi or Bluetooth.

[0693] 2. Server:

[0694] Data analysis function: The server analyzes the received business records and identifies the business processes. The analysis includes data cleansing.

[0695] Emotion engine: Analyzes the worker's biometric information and estimates their emotional state (stress, fatigue, etc.). This emotion engine uses machine learning algorithms.

[0696] Efficiency suggestion generation function: Generates efficiency and safety suggestions based on the analysis results and emotional state. Suggestions include using RPA, suggesting voice input, suggesting breaks, etc.

[0697] Work robot control function: Transmits the proposed work automation instructions to the work robot and controls the robot to perform the appropriate work.

[0698] 3. Feedback function:

[0699] User interface: The generated proposals are displayed to the user in a dashboard format, allowing them to review and modify the proposals as needed.

[0700] Data processing and calculation

[0701] Data recording and collection:

[0702] The smart glasses collect biometric information (heart rate, sweat rate) and work records of workers wearing them in real time.

[0703] Sending data:

[0704] The collected data is sent to a server via Wi-Fi or Bluetooth.

[0705] Data analysis and sentiment estimation:

[0706] The server cleanses the received data and uses analysis algorithms to identify business processes. The emotion engine then estimates the emotional state based on biometric information. This is done using machine learning algorithms using Python (e.g., TensorFlow, Scikit-learn).

[0707] Generate efficiency suggestions:

[0708] Based on the results of the analysis and sentiment estimation, a generative AI model is used to generate efficiency recommendations, such as using RPA or recommending breaks.

[0709] Instructions to the working robot:

[0710] Based on the efficiency improvement proposals, we generate automation instructions for the work robots and transmit them via wireless communication. We use PLCs (Programmable Logic Controllers) and ROS (Robotics Operating System) to instruct the robots to automatically execute specific tasks.

[0711] Specific examples

[0712] For example, when a user wears smart glasses, the server performs the following analyses based on the work records and biometric data collected from the start of work in the morning until the end of the workday:

[0713] The worker's heart rate exceeded 90 bpm between 10:00 and 11:00, indicating that he was in a state of high stress.

[0714] Based on work records and emotional state, the server suggests taking a break and leaving repetitive tasks to a robot.

[0715] Example prompt for a generative AI model:

[0716] Generate efficiency suggestions based on the following work records and sensor data:

[0717] Work record: Start time 09:00, Work content Maintenance

[0718] Sensor data: Heart rate 90, slight sweating

[0719] Proposal details:

[0720] High stress: Proposal to delegate repetitive tasks to robots

[0721] If fatigue is detected: Advise appropriate rest

[0722] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0723] Step 1:

[0724] The device acquires the user's work records. When the user starts a task, the device records the start time, work content, and end time of the task. It also collects biometric information (heart rate, sweat rate, etc.) in real time through the smart glasses. The input is the user's operation log and sensor data, and the output is the recorded work data and biometric information. Specifically, the device acquires data such as heart rate and sweat rate from the smart glasses' sensors and stores it in the device's memory.

[0725] Step 2:

[0726] The device sends the collected business record data and biometric information to the server at regular intervals. The input is the recorded business data and biometric information, and the output is the data sent to the server. Specifically, the device executes a process to send data to the server via Wi-Fi or Bluetooth communication.

[0727] Step 3:

[0728] The server analyzes the received business record data. The input is the business data and biometric information sent from the terminal, and the output is the analyzed business process data. Specific operations include data cleansing, time series analysis, and frequency analysis to identify task times, frequency, and repetitive tasks.

[0729] Step 4:

[0730] The server uses an emotion engine to analyze the worker's emotional state. The input is biometric information (heart rate, sweat rate, etc.), and the output is an estimated emotional state (stress, fatigue, etc.). Specifically, it uses a machine learning algorithm to classify and analyze the emotional state based on the biometric information.

[0731] Step 5:

[0732] The server generates efficiency improvement proposals based on the analysis results and emotional state. The inputs are business process data and emotional state information, and the output is efficiency improvement proposals. Specifically, the server combines business data and emotional data, inputs prompts to the generative AI model, and generates proposals.

[0733] Step 6:

[0734] The server then feeds back the generated efficiency suggestions to the user. The input is the efficiency suggestions, and the output is a feedback notification to the user. Specifically, the server visually displays the suggestions on a dashboard and sends a notification to the user's device.

[0735] Step 7:

[0736] The server generates work automation instructions for the work robot based on the emotional state and analysis data. The input is business process data and emotional state information, and the output is automation instructions. Specifically, if the emotional state is highly stressful, the server generates and sends instructions to delegate repetitive work to the robot.

[0737] Step 8:

[0738] The robot receives and executes the automation instructions proposed by the server. The input is the automation instructions from the server, and the output is the actual work performed. In terms of specific operations, the robot performs appropriate actions based on the received instructions to complete the work.

[0739] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0740] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0741] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0742] [Third embodiment]

[0743] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0744] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0745] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0746] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0747] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0748] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0749] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0750] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0751] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0752] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0753] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0754] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0755] The present invention provides a system for efficient business management and proposal making. Specific embodiments will be described below.

[0756] Overall system overview

[0757] In this system, users collect daily work records and send them to a server, which then analyzes the data, generates efficiency improvement proposals, and provides feedback to the users.

[0758] Data Collection Phase

[0759] When a user starts a task using a dedicated application, the start time and content are recorded. For example, "09:00 - Started checking email" or "09:30 - Ended checking email." The device also records activity logs during the task (keystrokes, application usage time, browser history, etc.).

[0760] The collected business record data is automatically sent from the terminal to the server at regular intervals (e.g., every hour), allowing data to be collected without any user intervention.

[0761] Data analysis phase

[0762] The server receives business record data sent from the terminal. After receiving it, it stores it in a database and first performs pre-processing. In pre-processing, it checks for inconsistencies and missing values ​​and performs data cleansing. For example, if there is a discrepancy between the start time and end time, this is corrected.

[0763] The server then analyzes the data using analytical algorithms to identify the duration, frequency, and repetitiveness of each task, such as the amount of time spent checking email or writing reports.

[0764] Proposal of efficiency methods

[0765] Based on the analysis, the server generates efficiency suggestions, which may include:

[0766] Automation using RPA: For example, a proposal to automate tasks such as replying to emails according to specific templates.

[0767] Voice input: If you are taking too long to type a sentence, we suggest you use a voice input tool.

[0768] Generative AI: Suggestions for automatically generating reports from templates.

[0769] These suggestions are personalized to suit your work style.

[0770] Providing feedback

[0771] The server compiles the generated efficiency proposals in the form of a dashboard. The proposals and their implementation procedures are presented to the user in a visually easy-to-read format. The server also notifies the user that the dashboard has been updated and provides feedback.

[0772] The device displays the dashboard for users to review and refine their own business processes, and implement the proposed improvements.

[0773] Implementing and reassessing efficiencies

[0774] The user reviews their business processes based on the dashboard and implements the proposed efficiency measures, such as setting up a new RPA bot or introducing a voice input tool. The device then records the user's business actions and transfers them to the server.

[0775] The server re-analyzes the data based on the new data, evaluates the effectiveness of the proposals, and makes further improvement proposals as needed, allowing users to continuously optimize their operations.

[0776] The processing flow will be explained below.

[0777] Step 1: User Business Records

[0778] When a user starts a task, they use a dedicated application to record the start and end of the task. For example, "09:00 - Start checking email" and "09:30 - End checking email." At that time, the device records an operation log (keystrokes, app usage time, browser history, etc.) in the background.

[0779] Step 2: Sending data

[0780] The terminal automatically sends collected business record data to the server at regular intervals (e.g., every hour), reducing the user's workload and making data collection more efficient.

[0781] Step 3: Receiving the data

[0782] The server receives the business record data sent from the terminal and stores it in a database, at which point it checks the consistency and integrity of the data.

[0783] Step 4: Preprocessing the data

[0784] The server pre-processes the data it receives, checking for inconsistencies and missing values ​​and performing data cleansing, for example correcting discrepancies between the start and end times of records.

[0785] Step 5: Analyze business processes

[0786] The server analyzes the data using analytical algorithms, identifying the duration, frequency, and repetitiveness of each task and assessing potential for efficiency improvements. For example, it identifies that checking email takes an average of 30 minutes, but involves many manual steps.

[0787] Step 6: Generate efficiency proposals

[0788] The server generates efficiency proposals based on the analysis results, including automation using RPA, the use of voice input, and the use of generative artificial intelligence (AI).

[0789] Step 7: Personalize your suggestions

[0790] The server customizes efficiency suggestions for each user based on their work style and past data, providing more practical suggestions.

[0791] Step 8: Generate a dashboard as feedback

[0792] The server compiles the generated efficiency suggestions into a visually appealing dashboard and prepares feedback with details of the suggestions and implementation steps.

[0793] Step 9: Notify users

[0794] The server notifies the user of updates to the dashboard, after which the user can check the detailed proposals.

[0795] Step 10: View the dashboard

[0796] The device will then present the user with a dashboard where they can review the recommendations, and the user will receive feedback to help them implement specific efficiency measures.

[0797] Step 11: Implement efficiency techniques

[0798] Users can follow the dashboard's suggestions to review their business processes and implement the suggested efficiency measures, such as setting up a new RPA bot or introducing a voice input tool.

[0799] Step 12: Collect new data

[0800] The device then records the user's work activities again and transfers the new data to the server, allowing the effectiveness of the efficiency improvement measures to be evaluated.

[0801] Step 13: Reanalyze and update proposals

[0802] The server reanalyzes the data based on the new data, evaluates the effectiveness of previous suggestions, and makes new suggestions for improvement as needed, allowing users to continuously optimize their operations.

[0803] Example 1

[0804] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0805] Modern business environments demand efficient business management. It is particularly important to automate the entire process, from acquiring business records to analyzing data and proposing efficiency improvements, to reduce user workload. However, previous systems lacked sufficient data preprocessing, cleansing, and efficient feedback, placing a heavy burden on users. Furthermore, efficiency improvements often lacked practicality, as they were not adapted to individual business styles. The objective of this invention is to solve these problems and provide a more effective business management system.

[0806] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0807] In this invention, the server includes means for preprocessing a user's business records and cleansing the data, means for analyzing the preprocessed data and identifying business processes, and means for providing generated efficiency suggestions as feedback to the user in the form of a dashboard. This improves the accuracy of the analysis of business records and the practicality of the efficient feedback and suggestions to the user, thereby enabling business optimization.

[0808] "User" means an individual or organization that uses the System to enter and review business records.

[0809] "Business records" refers to data such as the start time, end time, and content of business operations entered by the user.

[0810] "Server" refers to a computer system that receives business record data and performs pre-processing, analysis, and generation of efficiency recommendations.

[0811] "Preprocessing" refers to the process of checking for inconsistencies and missing values ​​in received business record data and cleansing the data.

[0812] "Data cleansing" refers to the process of correcting inconsistencies and missing values ​​in data and preparing it for analysis.

[0813] "Analysis" refers to the process of identifying business processes based on preprocessed data and analyzing the time, frequency, repetitive tasks, etc. of each task.

[0814] "Efficiency proposals" refer to proposing methods and tools to improve business efficiency based on the analysis results.

[0815] "Dashboard format" refers to an interface format that provides information to users in a visually easy-to-read format.

[0816] "Feedback" refers to the process of providing the generated efficiency proposals to the user, encouraging the implementation of the proposals through interaction with the user, and evaluating their effectiveness.

[0817] A "prompt" refers to a textual instruction that is input to a generative AI model.

[0818] A "generative AI model" refers to an algorithm that uses artificial intelligence to automatically generate efficiency suggestions based on prompt text.

[0819] "Robotic Process Automation (RPA)" refers to a software tool for automating routine tasks.

[0820] "Voice input" refers to a technique that allows a user to input voice as text data.

[0821] "Automated report generation" refers to the process of using generative AI models or other tools to automatically generate standard reports.

[0822] This invention provides a system for efficient business management and proposals. Specific embodiments are described below. In this system, a user acquires daily business records and sends them to a server, which analyzes the data, generates efficiency proposals, and provides feedback to the user.

[0823] Data Collection Phase

[0824] When a user starts a task using a dedicated application, the start time and task content are recorded. For example, it is recorded as "09:00 - Started checking email" and "09:30 - Ended checking email." The device also records activity logs during the task (keystrokes, application usage time, browser history, etc.). This information is automatically sent from the device to the server at regular intervals (e.g., every hour). This procedure allows data to be collected without the user having to perform any special operations.

[0825] Data analysis phase

[0826] The server receives the business record data sent from the device and stores it in a database. It then performs preprocessing, checking for inconsistencies and missing values ​​and cleansing the data. For example, it corrects discrepancies between start and end times. It then uses analytical algorithms to analyze the data and identify the duration, frequency, and repetitive tasks of each task. For example, it identifies the time spent checking email or writing reports.

[0827] Generate efficiency proposals

[0828] Based on the analysis, the server generates efficiency suggestions, which may include:

[0829] Automation using RPA: For example, a proposal to automate tasks such as replying to emails according to specific templates.

[0830] Voice input: If you are taking too long to type a sentence, we suggest you use a voice input tool.

[0831] Generative AI: Suggestions for automatically generating reports from templates.

[0832] These suggestions are personalized to suit the user's work style. For example, suggestions might include, "Use the RPA task automation tool to reduce the time spent checking emails," or "Introduce a voice input tool to create reports."

[0833] Providing feedback

[0834] The server compiles the generated efficiency improvement proposals in the form of a dashboard. The proposals and their implementation procedures are provided to the user in a visually easy-to-read format. The server notifies the user that the dashboard has been updated, and the contents of the dashboard are displayed on the user's device. The user can then review their own business processes and implement the proposed efficiency improvements.

[0835] Implementing and reassessing efficiencies

[0836] Users review their business processes based on the dashboard and implement the proposed efficiency improvements. For example, they can set up a new RPA bot or introduce a voice input tool. The device then records the user's work actions again and transfers them to the server. The server then reanalyzes the new data, evaluates the effectiveness of the proposals, and makes further improvement proposals as necessary. This allows users to continuously optimize their work.

[0837] Specific Examples

[0838] Example prompt: Analyze the following work log and generate efficiency suggestions: "09:00 - Start checking email", "09:30 - Finish checking email", "10:00 - Start writing report".

[0839] This prompt allows the generative AI model to generate efficiency suggestions based on the input data.

[0840] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0841] Step 1:

[0842] When a user starts a task by launching a dedicated application, the start time and task content are recorded. The user inputs something like "09:00 - Start checking email." Based on this input, the device generates data on the task start time and content, and this data is temporarily stored in memory.

[0843] Step 2:

[0844] While the user continues to work, the device records activity logs such as keystrokes, application usage time, and browser history. Specifically, software built into the device monitors the user's keyboard input and mouse clicks and records this information in a log file. The log file contains timestamps and the details of the actions taken.

[0845] Step 3:

[0846] The terminal automatically sends recorded business data and operation logs to the server at regular intervals (e.g., every hour). A specific API endpoint is used for sending. The input is business data and operation logs, and the output is a JSON-formatted data packet containing this data sent via the API.

[0847] Step 4:

[0848] The server stores the received business data and operation logs in a database. After storing, the server performs preprocessing, checks for inconsistencies and missing values, and cleanses the data. Specifically, it queries the data from the database and corrects inconsistent time information or missing information. The input is the received data, and the output is the cleansed data.

[0849] Step 5:

[0850] The server analyzes the cleansed data. It uses an analysis algorithm to identify the duration, frequency, and repetitive tasks of each task. Specifically, the server aggregates the data, counts frequency, and analyzes whether the same tasks are repeated. The input for the analysis is the cleansed data, and the output is an analysis of the duration, frequency, and repetitive tasks for each task.

[0851] Step 6:

[0852] The server generates efficiency improvement proposals based on the analysis results. Using a generative AI model, specific prompts are input and efficiency proposals are generated. For example, a specific proposal such as "Please set up RPA to reduce the time spent checking emails" is generated. The inputs are the analysis results and the prompt text, and the output is the text of the efficiency proposal.

[0853] Step 7:

[0854] The server compiles the generated efficiency suggestions in the form of a dashboard and provides it to the user. The server then sends a notification to the user that the dashboard has been updated, and the device displays the dashboard content. Specifically, the dashboard generation algorithm organizes the suggestions in a visually easy-to-read format, and the notification system notifies the user of updates. The efficiency suggestions are input, and a dashboard and notification are generated as output.

[0855] Step 8:

[0856] Users review their business processes based on the dashboard and implement the proposed efficiency measures. Specifically, users configure a new RPA bot and install a voice input tool, which improves business efficiency. The input is the efficiency suggestions on the dashboard, and the output is the implemented efficiency measures.

[0857] Step 9:

[0858] The device then records the user's work activities again and transfers them to the server. The server then reanalyzes the new data and evaluates the effectiveness of the proposals. If necessary, it makes further improvement proposals. The input is updated work data, and the output is evaluation results and new efficiency proposals. This allows the user to continuously optimize their work.

[0859] (Application example 1)

[0860] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0861] Conventional factory robot operation management systems lacked the functionality to automatically generate and provide feedback on efficiency and optimization proposals. This made it difficult for factory managers to grasp the robot's operating status in real time and operate efficiently. In particular, the lack of a means to effectively manage robot operating hours, task completion times, and the number of errors made it difficult to quickly make appropriate efficiency proposals.

[0862] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0863] In this invention, the server includes: means for acquiring user business records; means for transmitting the business records to the server; means for analyzing the business records received by the server and identifying business processes; means for generating efficiency improvement proposals based on the analysis results; means for feeding back the generated efficiency improvement proposals to the user; means for exchanging the feedback with the user; means for recording the operating time, task completion time, and number of error occurrences of robots in the factory; means for collecting the recorded data and transmitting it to the server at regular intervals; means for generating efficiency improvement proposals for the factory robots based on the collected data; and means for feeding back the robot proposals to a manager. This makes it possible to automate business management and efficiency improvement proposals for factory robots and provide managers with feedback for effective business operations in real time.

[0864] Definitions of important words

[0865] "User" means any individual or legal entity that uses the System and obtains Business Records.

[0866] "Business records" are data related to a user's business, and include information such as operating hours, task completion times, and the number of errors that have occurred.

[0867] "Server" means a central processing unit that receives, analyzes, stores, and provides feedback on data on the cloud or on-premise.

[0868] The "analysis means" refers to algorithms or software that analyzes the business record data received by the server and identifies the business process.

[0869] "Efficiency proposals" are proposals for optimization based on the duration, frequency, and repetitive tasks of work, generated using analytical tools.

[0870] The "feedback means" is a means for providing the generated efficiency improvement proposals to the user, and for reviewing and optimizing the business.

[0871] A "factory robot" is an automated mechanical device that performs various tasks in a factory.

[0872] "Operating time" refers to data that indicates the total amount of time that a factory robot is actually active.

[0873] "Task completion time" refers to the time it takes to complete a particular task or operation.

[0874] "Number of errors" refers to data that counts the number of errors and breakdowns that occur when a factory robot is working.

[0875] The "collection means" refers to the hardware and software used to collect business record data from factory robots at regular intervals and send it to a server.

[0876] An "administrator" is a person or role that monitors the operation of factory robots and optimizes business operations based on efficiency suggestions.

[0877] MODE FOR CARRYING OUT THE INVENTION

[0878] The present invention is a system aimed at managing the work of factory robots and improving their efficiency. Specifically, it collects users' work records, analyzes them on a server, generates efficiency proposals, and provides feedback.

[0879] Overall system configuration

[0880] The system mainly includes the following means:

[0881] 1. Data collection method: The user records the operating time of the factory robot, the time to complete a task, and the number of errors that occur.

[0882] 2. Data transmission means: The collected business record data is automatically transmitted from the terminal to the server at regular intervals.

[0883] 3. Data analysis means: The server analyzes the received business record data, identifies the business process, and generates efficiency proposals based on the analysis results.

[0884] 4. Feedback means: The generated efficiency proposals are fed back to the administrator (user) by the server and displayed in the form of a visual dashboard.

[0885] Data collection methods

[0886] Users can use dedicated software to obtain the work records of factory robots. This software has the function of recording the robot's operating time, task completion time, and number of errors in real time. For example, the operating time (10:00 - 10:30) and number of errors (5) of a welding robot can be recorded.

[0887] Data transmission method

[0888] The collected data is sent to a server at regular intervals (e.g., every hour) via the smartphone, allowing data to be collected and transferred to the server without any user intervention.

[0889] Data Analysis Methods

[0890] The server analyzes the received data using algorithms to identify the duration, frequency, and repetitive tasks of tasks (for example, how many times a particular task is repeated per day and how long it takes), and uses generative AI models to create prompts that generate efficiency suggestions.

[0891] Efficiency proposal generation method

[0892] The server uses generative AI models to generate efficiency recommendations, including the adoption of robotic process automation (RPA), voice input, and generative artificial intelligence (AI). For example, it might suggest the adoption of RPA to automate a specific welding task.

[0893] Feedback Methods

[0894] The generated efficiency proposals are fed back to the manager in the form of a dashboard by the server, who can then view the dashboard on their smartphone to review and optimize business processes.

[0895] Prompt Sentence Examples

[0896] Analyze the operating data of factory robots and generate efficiency improvement proposals.

[0897] Data content: { "robot_id": "robot_1", "timestamp": 1672522560, "operation": "welding", "duration": 1200, "error_count": 0}

[0898] Output format: JSON

[0899] Proposal content: Introduction of RPA for automation, optimization of work steps, maintenance proposals to reduce error rates, etc.

[0900] In this way, the present invention can effectively support the management and efficiency of factory robot operations and provide real-time feedback.

[0901] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0902] Program processing flow

[0903] Step 1:

[0904] Users use specialized software to record factory robot operating times, task completion times, and error occurrences in real time.

[0905] Input: Factory robot operating status

[0906] Output: Operating time, task completion time, number of errors

[0907] Specific operation: The user starts the software and presses the "Start" button when the robot starts working and the "End" button when it finishes. If an error occurs during this process, the details of the error are also recorded.

[0908] Step 2:

[0909] The collected data is automatically sent to a server via smartphone at regular intervals (e.g., every hour).

[0910] Input: Data on operation time, task completion time, and number of errors

[0911] Output: Data sent to the server

[0912] Specific operation: Collected data is sent to the server via HTTP at regular intervals. The data is in JSON format and includes uptime and error information.

[0913] Step 3:

[0914] The server analyzes the received data and identifies the business process.

[0915] Input: Data received by the server

[0916] Output: Analysis results (identification of business processes)

[0917] What it does: The server stores the received data in a database and uses analytical algorithms to analyze the data, for example, to identify the duration, frequency, and repetitive tasks of each task. It also cleanses the data using AI models as needed.

[0918] Step 4:

[0919] The server generates efficiency suggestions based on the analysis results.

[0920] Input: Analysis results (business processes stored in the database)

[0921] Output: Efficiency proposals

[0922] Specific operation: A prompt sentence is input into the generative AI model to generate efficiency improvement proposals. An example of a prompt sentence is, "Analyze the operation data of factory robots and generate efficiency improvement proposals." The generated efficiency proposals include RPA implementation, optimization of work steps, and maintenance proposals to reduce error rates.

[0923] Step 5:

[0924] The server provides feedback of the generated efficiency proposals to the administrator (user).

[0925] Input: Efficiency proposal

[0926] Output: Feedback dashboard displayed on the administrator's smartphone

[0927] Specific operation: The server converts the generated proposals into a visually easy-to-read dashboard format and sends a notification to the user's smartphone. The user can then view the dashboard on their smartphone and review the details of the efficiency proposals.

[0928] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0929] This invention combines an emotion engine with a system that acquires and analyzes a user's work records to generate efficiency suggestions and provide feedback to the user. This system makes it possible to provide suggestions that not only improve work efficiency but also take into account the user's emotional state.

[0930] Overall system overview

[0931] This system acquires the user's work records, sends them to a server for analysis, and then uses an emotion engine to identify the user's emotional state and generate efficiency suggestions.These suggestions are then fed back to the user to optimize their work.

[0932] Data Collection Phase

[0933] When a user starts a task using a dedicated application, the start time and content are recorded. For example, "09:00 - Started checking email," "09:30 - Ended checking email," etc. In addition, the device records operation logs (keystrokes, app usage time, browser history, etc.) in the background.

[0934] The collected business record data is automatically sent from the device to the server at regular intervals (e.g., every hour), reducing the user's workload and streamlining data collection.

[0935] Data analysis phase

[0936] The server receives the business record data sent from the device and stores it in a database. It then performs preprocessing, checks for inconsistencies and missing values, and performs data cleansing, such as correcting discrepancies between start and end times.

[0937] The server then analyzes the data using analytical algorithms to identify the duration, frequency, and repetitive tasks of each task and assess potential efficiency improvements.

[0938] Emotion Engine

[0939] The system adds an emotion engine that analyzes user input and behavioral data to identify the user's emotional state. For example, it estimates the user's current emotional state (e.g., stress, fatigue, joy, etc.) from keystroke speed, speech tone, and past behavioral patterns.

[0940] The emotional state identified by the emotion engine is integrated into the server's analysis results and influences efficiency suggestions. For example, if a user is in a high-stress state, the system will suggest ways to reduce workload or become more relaxed.

[0941] Proposal of efficiency methods

[0942] Based on the analysis results and the output of the emotion engine, the server generates efficiency suggestions, which may include:

[0943] Automation using RPA: For example, automating tasks such as replying to emails according to specific templates.

[0944] Voice input: If you are taking too long to type a sentence, we suggest you use a voice input tool.

[0945] Generative AI: Suggestions for automatically generating reports from templates.

[0946] Suggesting breaks to reduce stress: Suggesting appropriate breaks depending on your emotional state.

[0947] Providing feedback

[0948] The server compiles the generated efficiency improvement proposals in the form of a dashboard. The proposals and their implementation procedures are presented to the user in a visually easy-to-read format. The server notifies the user of updates to the dashboard.

[0949] The device displays a dashboard to the user, allowing them to review the recommendations, allowing them to review their own business processes and implement the efficiency recommendations.

[0950] Implementing and reassessing efficiencies

[0951] Users can review their business processes based on the dashboard's suggestions and implement the suggested efficiency measures, such as setting up a new RPA bot or introducing a voice input tool.

[0952] The device then records the user's work activities again and transfers the new data to the server. The server then reanalyzes the data, evaluates the effectiveness of the suggestions, and makes further suggestions for improvement as necessary. This allows the user to continuously optimize their work.

[0953] Specific examples

[0954] For example, if the emotion engine detects that a user is feeling high stress while checking email, the server can suggest automating that task and suggest taking a moderate break.It can also provide instructions to set up automatic email replies using RPA, further improving work efficiency.

[0955] The processing flow will be explained below.

[0956] Step 1: Enter business records

[0957] A user uses a dedicated application to record the start and end of work, for example, "09:00 - Start checking email" and "09:30 - End checking email."

[0958] Step 2: Collecting operational logs

[0959] The device collects operation logs in the background (keystrokes, application usage time, web browser history, etc.), allowing detailed business data to be obtained.

[0960] Step 3: Send business records

[0961] The terminal automatically sends collected business record data to the server at regular intervals (e.g., every hour).

[0962] Step 4: Receiving and storing data

[0963] The server receives the business record data sent from the terminal and stores it in a database.

[0964] Step 5: Preprocessing the data

[0965] The server performs pre-processing on the data received, checking for inconsistencies and missing values ​​and cleansing the data as needed, for example, fixing discrepancies between start and end times.

[0966] Step 6: Analyze business processes

[0967] The server uses analytical algorithms to analyze the work record data, identifying the duration, frequency, and repetitive tasks of each task and identifying areas where work can be streamlined.

[0968] Step 7: Collect emotion data

[0969] The device uses an emotion engine to recognize the user's emotional state based on their input and behavioral data, such as analyzing keystroke speed and voice input tone.

[0970] Step 8: Emotion Engine Analysis

[0971] The server integrates the output of the emotion engine into the analysis results and performs an analysis that takes into account the user's emotional state (e.g., stress, fatigue, joy).

[0972] Step 9: Generate efficiency proposals

[0973] The server generates efficiency improvement proposals based on the results of business process analysis and the output of the emotion engine. Proposals include automation using RPA, use of voice input, utilization of generative artificial intelligence (AI), and break suggestions.

[0974] Step 10: Personalize your suggestions

[0975] The server customizes the suggestions to suit the user's work style and emotional state, providing the user with the most effective efficiency techniques.

[0976] Step 11: Generate the dashboard

[0977] The server compiles the generated efficiency suggestions into a visually appealing dashboard, including details of the suggestions and steps to implement them.

[0978] Step 12: Notify users

[0979] The server notifies the user of updates to the dashboard, and the user can then review the suggestions.

[0980] Step 13: View the Dashboard

[0981] The device displays a dashboard for users to review and refine efficiency recommendations, allowing them to adjust their business processes based on the recommendations.

[0982] Step 14: Implementing efficiency measures

[0983] Users can follow the dashboard's suggestions to implement specific efficiency measures, such as setting up a new RPA bot or introducing a voice input tool.

[0984] Step 15: Collect new data

[0985] The device then records the user's work activities again and transfers the new data to the server, allowing the effectiveness of the efficiency measures implemented to be evaluated.

[0986] Step 16: Reanalyze and update proposals

[0987] The server re-analyzes the data based on the new data and evaluates the effectiveness of the previous proposal. If necessary, it generates further improvement proposals and provides them as feedback to the user. This allows users to continuously optimize their operations.

[0988] Example 2

[0989] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0990] In today's business environment, improving business efficiency is an important issue. However, simply automating business processes is not enough to achieve efficiency; the user's emotional state must also be taken into consideration. In particular, users who are under high stress or fatigue may not be able to enjoy the benefits of efficiency unless appropriate suggestions are made. Therefore, it is necessary to generate efficiency suggestions that take the user's emotional state into account.

[0991] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0992] In this invention, the server includes means for acquiring a user's work records, means for transmitting the work records to the server, means for analyzing the received work records and identifying work processes in the server, means for generating efficiency suggestions based on the analysis results and emotion analysis results, means for feeding back the generated efficiency suggestions to the user, means for exchanging the feedback with the user, and means for analyzing the emotional state of the user. This makes it possible to not only improve the efficiency of the user's work processes, but also to suggest stress reduction and timely breaks according to the user's emotional state.

[0993] A "user" is an individual or organization that uses the system to carry out business.

[0994] "Business records" are log information such as the start and end times and contents of tasks performed by a user during business hours.

[0995] The "server" is a central management system that receives business records, analyzes them, generates efficiency suggestions, and provides feedback to users.

[0996] "Sentiment analysis" is the process of identifying a user's emotional state based on their input and behavioral data.

[0997] "Efficiency proposals" are proposals that show ways to automate tasks, simplify work, and reduce stress.

[0998] "Robotic Process Automation (RPA)" is a technology for automating routine business processes.

[0999] "Voice input" is a method in which a user inputs information by voice using a microphone.

[1000] "Generative artificial intelligence (AI)" is an artificial intelligence technology that automatically generates new information and suggestions based on data.

[1001] A "dashboard" is a user interface that visually displays proposal content and data status.

[1002] A "database" is a collection of data that allows a system to efficiently store, manage, and retrieve data.

[1003] "Data cleansing" is the process of correcting inconsistencies and missing values ​​to ensure data consistency and accuracy.

[1004] This invention combines a system that acquires a user's work records, analyzes them, generates efficiency suggestions, and provides feedback to the user, with an emotion engine that identifies the user's emotional state. This system can provide suggestions that not only improve work efficiency but also take the user's emotional state into consideration.

[1005] Supported Hardware and Software

[1006] The server receives the user's business record data and emotional state data, analyzes them, and generates efficiency suggestions. The server mainly operates using a database (e.g., SQL server), analysis algorithms (e.g., Python's Pandas or Scikit-learn), and emotion engines (e.g., NLP technology). The server displays the generated suggestions in a dashboard format and notifies the user.

[1007] The device collects business records and operation logs when the user performs work, and transmits the data to a server at regular intervals. The device receives user input using a dedicated application (e.g., a business record application), and records keystroke speed, application usage time, browser history, etc. in the background.

[1008] Users use a dedicated application to input the start and end times of their work and the details of their work, then confirm and implement the efficiency suggestions. The emotion engine also analyzes the user's operations, input speed, and tone of voice to identify their emotional state.

[1009] Examples of specific examples and prompts

[1010] For example, a user can enter a task record such as "09:00 - Started checking email" into the app, and the device will simultaneously record the user's typing speed and the time spent using the app. This data is automatically sent to the server, which analyzes the task record to determine what tasks the user is spending time on and the associated emotional state (e.g., stress or fatigue).

[1011] Based on the analysis results, the server generates efficiency proposals, including improvement measures such as "automating email replies with RPA," "introducing a voice input tool," and "taking breaks at appropriate times." These proposals are displayed on a dashboard so that users can check them.

[1012] Examples of prompts are:

[1013] "Generate efficiency suggestions based on the user's work record data and emotional state."

[1014] "Analyze your work processes with algorithms that offer suggestions for automating tasks and reducing stress."

[1015] These prompts allow the generative AI model to be appropriately utilized to support the user in improving work efficiency. This system is effective as a method for integrating work efficiency and emotional state management.

[1016] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1017] Step 1: Data collection

[1018] The user launches a dedicated application and enters the start and end times and details of their work. For example, they might enter "09:00 - Start checking email." In addition, the device records keystrokes, app usage time, browser history, and other operation logs in the background. The input data (work records and operation logs) is saved on the device.

[1019] Step 2: Send data

[1020] The terminal sends the collected business record data and operation logs to the server in batches at regular intervals (for example, every hour). This sending process is automated to reduce the user's workload. The input data is sent from the terminal to the server and stored in a database.

[1021] Step 3: Data storage and cleansing

[1022] The server receives data from the terminal and stores it in a database. It then checks the stored data for inconsistencies and missing values, and performs data cleansing to correct any discrepancies, such as discrepancies between start and end times. Input data (business records and operation logs) is filtered and stored as clean data.

[1023] Step 4: Data analysis

[1024] The server analyzes the clean data. The analysis identifies the time, frequency, and repetitive tasks for each task. For example, it evaluates whether employees spend 30 minutes checking email every day. The input data (clean data) is analyzed by an analytical algorithm to evaluate the possibility of improving the efficiency of business processes. The output is the business analysis results.

[1025] Step 5: Sentiment Analysis

[1026] The server uses an emotion engine to analyze the user's emotional state. For example, it can estimate whether the user is feeling stressed based on their keystroke speed, operation patterns, and past behavioral data. Similarly, when using voice input, it can also analyze emotions from the tone of their speech. The input data (operation logs and past behavioral data) is analyzed by an analysis algorithm to obtain emotion analysis results.

[1027] Step 6: Generate efficiency proposals

[1028] The server integrates the results of task analysis and emotion analysis to generate efficiency suggestions. For example, if a user is in a high-stress state, it may suggest simplifying tasks, introducing RPA tools, using voice input, or taking breaks. The input data (task analysis results and emotion analysis results) is analyzed, and efficiency suggestions are generated.

[1029] Step 7: Feedback on proposal

[1030] The server compiles the generated efficiency proposals in a dashboard format and provides feedback to the user. The dashboard displays the proposals in a visually easy-to-read format, making it easier for the user to confirm the proposals. The input data (efficiency proposals) is displayed on the dashboard and notified to the user.

[1031] Step 8: Implement and reassess efficiency

[1032] The user reviews their business processes according to the dashboard's suggestions and implements the proposed efficiency measures. For example, they set up a new RPA bot and introduce a voice input tool. The device re-records the user's new business process and sends the new data to the server. The server re-analyzes the new data, evaluates the effectiveness of the proposals, and makes further improvement proposals as necessary. The input data (new business records) is re-analyzed, and an evaluation of the effectiveness of the proposals is obtained.

[1033] (Application example 2)

[1034] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1035] Conventional work efficiency systems make efficiency improvement suggestions without considering the user's emotional state, which can result in suggestions that ignore stress and fatigue. Furthermore, there is a lack of analysis of the emotional state of factory workers and real-time work automation support, making it difficult to achieve both occupational safety and work efficiency. The purpose of this invention is to solve these problems and realize efficiency improvement suggestions and work automation that take the user's emotional state into account.

[1036] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1037] In this invention, the server includes means for acquiring a user's work records, means for transmitting the work records to the server, means for analyzing the work records received by the server and identifying work processes, means for generating efficiency improvement proposals based on the analysis results and the user's emotional state, means for feeding back the generated efficiency improvement proposals to the user, means for exchanging the feedback with the user, means for acquiring worker status data (e.g., heart rate, sweat rate), means for analyzing the worker's emotional state, means for generating work automation instructions for a work robot based on the emotional state and the analyzed data, and means for transmitting the proposed work automation instructions to the work robot and having it execute them. This enables efficiency improvement proposals that reflect the user's emotional state and real-time work automation support that corresponds to the emotional states of workers in a factory.

[1038] The "means for acquiring user's work records" refers to a device or system that has the function of recording and collecting information such as the type of work performed by the user, the start time, and the end time.

[1039] The "means for transmitting the business records to the server" refers to a device or system that includes communication functions and protocols for transferring information from the user's terminal to the data center.

[1040] The "means for analyzing the business records received on the server and identifying the business process" refers to software or hardware that has the function of processing the business record data received on the server and analyzing the business content, its progress, etc.

[1041] "Means for generating efficiency proposals based on analysis results and the user's emotional state" refers to algorithms or systems that propose methods for improving work efficiency and reducing workload based on business data and emotion analysis results.

[1042] The "means for providing feedback of the generated efficiency improvement proposals to the user" refers to an interface or device for notifying the user of the results of the efficiency improvement proposals and conveying them by display or voice.

[1043] The "means for exchanging feedback with the user" is an interface with a two-way communication function that allows the user to check the proposal content and respond or make changes as necessary.

[1044] "Means for acquiring worker status data (e.g., heart rate, sweat rate)" refers to sensors or devices that measure the worker's biometric information in real time and collect it as data.

[1045] The "means for analyzing the worker's emotional state" refers to an analytical algorithm or system for estimating the worker's emotions, such as stress and fatigue, based on collected biometric information.

[1046] The "means for generating work automation instructions for a work robot based on emotional state and analytical data" refers to software and hardware that combines emotional state and business data to create optimal work plans and automation instructions.

[1047] The "means for transmitting the proposed work automation instructions to the work robot and having it execute them" refers to the communication means and control system for sending the created automation instructions to the robot and starting the actual work.

[1048] This invention is a "factory smart assistant" system that makes suggestions based on work efficiency and the emotional state of the worker. This system acquires the user's work records, generates efficiency suggestions based on the analysis results and the worker's emotional state, and also issues instructions to a work robot to automate the work.

[1049] System configuration

[1050] 1. User Device:

[1051] Data collection function: The user device records the start and end times of work, as well as the details of the work. In addition, smart glasses are used to collect biometric information (heart rate, sweat rate, etc.) while the user is working.

[1052] Communication function: Collected data is sent to a server at regular intervals via Wi-Fi or Bluetooth.

[1053] 2. Server:

[1054] Data analysis function: The server analyzes the received business records and identifies the business processes. The analysis includes data cleansing.

[1055] Emotion engine: Analyzes the worker's biometric information and estimates their emotional state (stress, fatigue, etc.). This emotion engine uses machine learning algorithms.

[1056] Efficiency suggestion generation function: Generates efficiency and safety suggestions based on the analysis results and emotional state. Suggestions include using RPA, suggesting voice input, suggesting breaks, etc.

[1057] Work robot control function: Transmits the proposed work automation instructions to the work robot and controls the robot to perform the appropriate work.

[1058] 3. Feedback function:

[1059] User interface: The generated proposals are displayed to the user in a dashboard format, allowing them to review and modify the proposals as needed.

[1060] Data processing and calculation

[1061] Data recording and collection:

[1062] The smart glasses collect biometric information (heart rate, sweat rate) and work records of workers wearing them in real time.

[1063] Sending data:

[1064] The collected data is sent to a server via Wi-Fi or Bluetooth.

[1065] Data analysis and sentiment estimation:

[1066] The server cleanses the received data and uses analysis algorithms to identify business processes. The emotion engine then estimates the emotional state based on biometric information. This is done using machine learning algorithms using Python (e.g., TensorFlow, Scikit-learn).

[1067] Generate efficiency suggestions:

[1068] Based on the results of the analysis and sentiment estimation, a generative AI model is used to generate efficiency recommendations, such as using RPA or recommending breaks.

[1069] Instructions to the working robot:

[1070] Based on the efficiency improvement proposals, we generate automation instructions for the work robots and transmit them via wireless communication. We use PLCs (Programmable Logic Controllers) and ROS (Robotics Operating System) to instruct the robots to automatically execute specific tasks.

[1071] Specific examples

[1072] For example, when a user wears smart glasses, the server performs the following analyses based on the work records and biometric data collected from the start of work in the morning until the end of the workday:

[1073] The worker's heart rate exceeded 90 bpm between 10:00 and 11:00, indicating that he was in a state of high stress.

[1074] Based on work records and emotional state, the server suggests taking a break and leaving repetitive tasks to a robot.

[1075] Example prompt for a generative AI model:

[1076] Generate efficiency suggestions based on the following work records and sensor data:

[1077] Work record: Start time 09:00, Work content Maintenance

[1078] Sensor data: Heart rate 90, slight sweating

[1079] Proposal details:

[1080] High stress: Proposal to delegate repetitive tasks to robots

[1081] If fatigue is detected: Advise appropriate rest

[1082] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1083] Step 1:

[1084] The device acquires the user's work records. When the user starts a task, the device records the start time, work content, and end time of the task. It also collects biometric information (heart rate, sweat rate, etc.) in real time through the smart glasses. The input is the user's operation log and sensor data, and the output is the recorded work data and biometric information. Specifically, the device acquires data such as heart rate and sweat rate from the smart glasses' sensors and stores it in the device's memory.

[1085] Step 2:

[1086] The device sends the collected business record data and biometric information to the server at regular intervals. The input is the recorded business data and biometric information, and the output is the data sent to the server. Specifically, the device executes a process to send data to the server via Wi-Fi or Bluetooth communication.

[1087] Step 3:

[1088] The server analyzes the received business record data. The input is the business data and biometric information sent from the terminal, and the output is the analyzed business process data. Specific operations include data cleansing, time series analysis, and frequency analysis to identify task times, frequency, and repetitive tasks.

[1089] Step 4:

[1090] The server uses an emotion engine to analyze the worker's emotional state. The input is biometric information (heart rate, sweat rate, etc.), and the output is an estimated emotional state (stress, fatigue, etc.). Specifically, it uses a machine learning algorithm to classify and analyze the emotional state based on the biometric information.

[1091] Step 5:

[1092] The server generates efficiency improvement proposals based on the analysis results and emotional state. The inputs are business process data and emotional state information, and the output is efficiency improvement proposals. Specifically, the server combines business data and emotional data, inputs prompts to the generative AI model, and generates proposals.

[1093] Step 6:

[1094] The server then feeds back the generated efficiency suggestions to the user. The input is the efficiency suggestions, and the output is a feedback notification to the user. Specifically, the server visually displays the suggestions on a dashboard and sends a notification to the user's device.

[1095] Step 7:

[1096] The server generates work automation instructions for the work robot based on the emotional state and analysis data. The input is business process data and emotional state information, and the output is automation instructions. Specifically, if the emotional state is highly stressful, the server generates and sends instructions to delegate repetitive work to the robot.

[1097] Step 8:

[1098] The robot receives and executes the automation instructions proposed by the server. The input is the automation instructions from the server, and the output is the actual work performed. In terms of specific operations, the robot performs appropriate actions based on the received instructions to complete the work.

[1099] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1100] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1101] 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 the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1102] [Fourth embodiment]

[1103] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1104] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1105] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1106] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1107] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1109] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1110] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1111] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1112] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[1113] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1114] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1115] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1116] The present invention provides a system for efficient business management and proposal making. Specific embodiments will be described below.

[1117] Overall system overview

[1118] In this system, users collect daily work records and send them to a server, which then analyzes the data, generates efficiency improvement proposals, and provides feedback to the users.

[1119] Data Collection Phase

[1120] When a user starts a task using a dedicated application, the start time and content are recorded. For example, "09:00 - Started checking email" or "09:30 - Ended checking email." The device also records activity logs during the task (keystrokes, application usage time, browser history, etc.).

[1121] The collected business record data is automatically sent from the terminal to the server at regular intervals (e.g., every hour), allowing data to be collected without any user intervention.

[1122] Data analysis phase

[1123] The server receives business record data sent from the terminal. After receiving it, it stores it in a database and first performs pre-processing. In pre-processing, it checks for inconsistencies and missing values ​​and performs data cleansing. For example, if there is a discrepancy between the start time and end time, this is corrected.

[1124] The server then analyzes the data using analytical algorithms to identify the duration, frequency, and repetitiveness of each task, such as the amount of time spent checking email or writing reports.

[1125] Proposal of efficiency methods

[1126] Based on the analysis, the server generates efficiency suggestions, which may include:

[1127] Automation using RPA: For example, a proposal to automate tasks such as replying to emails according to specific templates.

[1128] Voice input: If you are taking too long to type a sentence, we suggest you use a voice input tool.

[1129] Generative AI: Suggestions for automatically generating reports from templates.

[1130] These suggestions are personalized to suit your work style.

[1131] Providing feedback

[1132] The server compiles the generated efficiency proposals in the form of a dashboard. The proposals and their implementation procedures are presented to the user in a visually easy-to-read format. The server also notifies the user that the dashboard has been updated and provides feedback.

[1133] The device displays the dashboard for users to review and refine their own business processes, and implement the proposed improvements.

[1134] Implementing and reassessing efficiencies

[1135] The user reviews their business processes based on the dashboard and implements the proposed efficiency measures, such as setting up a new RPA bot or introducing a voice input tool. The device then records the user's business actions and transfers them to the server.

[1136] The server re-analyzes the data based on the new data, evaluates the effectiveness of the proposals, and makes further improvement proposals as needed, allowing users to continuously optimize their operations.

[1137] The processing flow will be explained below.

[1138] Step 1: User Business Records

[1139] When a user starts a task, they use a dedicated application to record the start and end of the task. For example, "09:00 - Start checking email" and "09:30 - End checking email." At that time, the device records an operation log (keystrokes, app usage time, browser history, etc.) in the background.

[1140] Step 2: Sending data

[1141] The terminal automatically sends collected business record data to the server at regular intervals (e.g., every hour), reducing the user's workload and making data collection more efficient.

[1142] Step 3: Receiving the data

[1143] The server receives the business record data sent from the terminal and stores it in a database, at which point it checks the consistency and integrity of the data.

[1144] Step 4: Preprocessing the data

[1145] The server pre-processes the data it receives, checking for inconsistencies and missing values ​​and performing data cleansing, for example correcting discrepancies between the start and end times of records.

[1146] Step 5: Analyze business processes

[1147] The server analyzes the data using analytical algorithms, identifying the duration, frequency, and repetitiveness of each task and assessing potential for efficiency improvements. For example, it identifies that checking email takes an average of 30 minutes, but involves many manual steps.

[1148] Step 6: Generate efficiency proposals

[1149] The server generates efficiency proposals based on the analysis results, including automation using RPA, the use of voice input, and the use of generative artificial intelligence (AI).

[1150] Step 7: Personalize your suggestions

[1151] The server customizes efficiency suggestions for each user based on their work style and past data, providing more practical suggestions.

[1152] Step 8: Generate a dashboard as feedback

[1153] The server compiles the generated efficiency suggestions into a visually appealing dashboard and prepares feedback with details of the suggestions and implementation steps.

[1154] Step 9: Notify users

[1155] The server notifies the user of updates to the dashboard, after which the user can check the detailed proposals.

[1156] Step 10: View the dashboard

[1157] The device will then present the user with a dashboard where they can review the recommendations, and the user will receive feedback to help them implement specific efficiency measures.

[1158] Step 11: Implement efficiency techniques

[1159] Users can follow the dashboard's suggestions to review their business processes and implement the suggested efficiency measures, such as setting up a new RPA bot or introducing a voice input tool.

[1160] Step 12: Collect new data

[1161] The device then records the user's work activities again and transfers the new data to the server, allowing the effectiveness of the efficiency improvement measures to be evaluated.

[1162] Step 13: Reanalyze and update proposals

[1163] The server reanalyzes the data based on the new data, evaluates the effectiveness of previous suggestions, and makes new suggestions for improvement as needed, allowing users to continuously optimize their operations.

[1164] Example 1

[1165] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1166] Modern business environments demand efficient business management. It is particularly important to automate the entire process, from acquiring business records to analyzing data and proposing efficiency improvements, to reduce user workload. However, previous systems lacked sufficient data preprocessing, cleansing, and efficient feedback, placing a heavy burden on users. Furthermore, efficiency improvements often lacked practicality, as they were not adapted to individual business styles. The objective of this invention is to solve these problems and provide a more effective business management system.

[1167] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1168] In this invention, the server includes means for preprocessing a user's business records and cleansing the data, means for analyzing the preprocessed data and identifying business processes, and means for providing generated efficiency suggestions as feedback to the user in the form of a dashboard. This improves the accuracy of the analysis of business records and the practicality of the efficient feedback and suggestions to the user, thereby enabling business optimization.

[1169] "User" means an individual or organization that uses the System to enter and review business records.

[1170] "Business records" refers to data such as the start time, end time, and content of business operations entered by the user.

[1171] "Server" refers to a computer system that receives business record data and performs pre-processing, analysis, and generation of efficiency recommendations.

[1172] "Preprocessing" refers to the process of checking for inconsistencies and missing values ​​in received business record data and cleansing the data.

[1173] "Data cleansing" refers to the process of correcting inconsistencies and missing values ​​in data and preparing it for analysis.

[1174] "Analysis" refers to the process of identifying business processes based on preprocessed data and analyzing the time, frequency, repetitive tasks, etc. of each task.

[1175] "Efficiency proposals" refer to proposing methods and tools to improve business efficiency based on the analysis results.

[1176] "Dashboard format" refers to an interface format that provides information to users in a visually easy-to-read format.

[1177] "Feedback" refers to the process of providing the generated efficiency proposals to the user, encouraging the implementation of the proposals through interaction with the user, and evaluating their effectiveness.

[1178] A "prompt" refers to a textual instruction that is input to a generative AI model.

[1179] A "generative AI model" refers to an algorithm that uses artificial intelligence to automatically generate efficiency suggestions based on prompt text.

[1180] "Robotic Process Automation (RPA)" refers to a software tool for automating routine tasks.

[1181] "Voice input" refers to a technique that allows a user to input voice as text data.

[1182] "Automated report generation" refers to the process of using generative AI models or other tools to automatically generate standard reports.

[1183] This invention provides a system for efficient business management and proposals. Specific embodiments are described below. In this system, a user acquires daily business records and sends them to a server, which analyzes the data, generates efficiency proposals, and provides feedback to the user.

[1184] Data Collection Phase

[1185] When a user starts a task using a dedicated application, the start time and task content are recorded. For example, it is recorded as "09:00 - Started checking email" and "09:30 - Ended checking email." The device also records activity logs during the task (keystrokes, application usage time, browser history, etc.). This information is automatically sent from the device to the server at regular intervals (e.g., every hour). This procedure allows data to be collected without the user having to perform any special operations.

[1186] Data analysis phase

[1187] The server receives the business record data sent from the device and stores it in a database. It then performs preprocessing, checking for inconsistencies and missing values ​​and cleansing the data. For example, it corrects discrepancies between start and end times. It then uses analytical algorithms to analyze the data and identify the duration, frequency, and repetitive tasks of each task. For example, it identifies the time spent checking email or writing reports.

[1188] Generate efficiency proposals

[1189] Based on the analysis, the server generates efficiency suggestions, which may include:

[1190] Automation using RPA: For example, a proposal to automate tasks such as replying to emails according to specific templates.

[1191] Voice input: If you are taking too long to type a sentence, we suggest you use a voice input tool.

[1192] Generative AI: Suggestions for automatically generating reports from templates.

[1193] These suggestions are personalized to suit the user's work style. For example, suggestions might include, "Use the RPA task automation tool to reduce the time spent checking emails," or "Introduce a voice input tool to create reports."

[1194] Providing feedback

[1195] The server compiles the generated efficiency improvement proposals in the form of a dashboard. The proposals and their implementation procedures are provided to the user in a visually easy-to-read format. The server notifies the user that the dashboard has been updated, and the contents of the dashboard are displayed on the user's device. The user can then review their own business processes and implement the proposed efficiency improvements.

[1196] Implementing and reassessing efficiencies

[1197] Users review their business processes based on the dashboard and implement the proposed efficiency improvements. For example, they can set up a new RPA bot or introduce a voice input tool. The device then records the user's work actions again and transfers them to the server. The server then reanalyzes the new data, evaluates the effectiveness of the proposals, and makes further improvement proposals as necessary. This allows users to continuously optimize their work.

[1198] Specific Examples

[1199] Example prompt: Analyze the following work log and generate efficiency suggestions: "09:00 - Start checking email", "09:30 - Finish checking email", "10:00 - Start writing report".

[1200] This prompt allows the generative AI model to generate efficiency suggestions based on the input data.

[1201] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1202] Step 1:

[1203] When a user starts a task by launching a dedicated application, the start time and task content are recorded. The user inputs something like "09:00 - Start checking email." Based on this input, the device generates data on the task start time and content, and this data is temporarily stored in memory.

[1204] Step 2:

[1205] While the user continues to work, the device records activity logs such as keystrokes, application usage time, and browser history. Specifically, software built into the device monitors the user's keyboard input and mouse clicks and records this information in a log file. The log file contains timestamps and the details of the actions taken.

[1206] Step 3:

[1207] The terminal automatically sends recorded business data and operation logs to the server at regular intervals (e.g., every hour). A specific API endpoint is used for sending. The input is business data and operation logs, and the output is a JSON-formatted data packet containing this data sent via the API.

[1208] Step 4:

[1209] The server stores the received business data and operation logs in a database. After storing, the server performs preprocessing, checks for inconsistencies and missing values, and cleanses the data. Specifically, it queries the data from the database and corrects inconsistent time information or missing information. The input is the received data, and the output is the cleansed data.

[1210] Step 5:

[1211] The server analyzes the cleansed data. It uses an analysis algorithm to identify the duration, frequency, and repetitive tasks of each task. Specifically, the server aggregates the data, counts frequency, and analyzes whether the same tasks are repeated. The input for the analysis is the cleansed data, and the output is an analysis of the duration, frequency, and repetitive tasks for each task.

[1212] Step 6:

[1213] The server generates efficiency improvement proposals based on the analysis results. Using a generative AI model, specific prompts are input and efficiency proposals are generated. For example, a specific proposal such as "Please set up RPA to reduce the time spent checking emails" is generated. The inputs are the analysis results and the prompt text, and the output is the text of the efficiency proposal.

[1214] Step 7:

[1215] The server compiles the generated efficiency suggestions in the form of a dashboard and provides it to the user. The server then sends a notification to the user that the dashboard has been updated, and the device displays the dashboard content. Specifically, the dashboard generation algorithm organizes the suggestions in a visually easy-to-read format, and the notification system notifies the user of updates. The efficiency suggestions are input, and a dashboard and notification are generated as output.

[1216] Step 8:

[1217] Users review their business processes based on the dashboard and implement the proposed efficiency measures. Specifically, users configure a new RPA bot and install a voice input tool, which improves business efficiency. The input is the efficiency suggestions on the dashboard, and the output is the implemented efficiency measures.

[1218] Step 9:

[1219] The device then records the user's work activities again and transfers them to the server. The server then reanalyzes the new data and evaluates the effectiveness of the proposals. If necessary, it makes further improvement proposals. The input is updated work data, and the output is evaluation results and new efficiency proposals. This allows the user to continuously optimize their work.

[1220] (Application example 1)

[1221] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1222] Conventional factory robot operation management systems lacked the functionality to automatically generate and provide feedback on efficiency and optimization proposals. This made it difficult for factory managers to grasp the robot's operating status in real time and operate efficiently. In particular, the lack of a means to effectively manage robot operating hours, task completion times, and the number of errors made it difficult to quickly make appropriate efficiency proposals.

[1223] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1224] In this invention, the server includes: means for acquiring user business records; means for transmitting the business records to the server; means for analyzing the business records received by the server and identifying business processes; means for generating efficiency improvement proposals based on the analysis results; means for feeding back the generated efficiency improvement proposals to the user; means for exchanging the feedback with the user; means for recording the operating time, task completion time, and number of error occurrences of robots in the factory; means for collecting the recorded data and transmitting it to the server at regular intervals; means for generating efficiency improvement proposals for the factory robots based on the collected data; and means for feeding back the robot proposals to a manager. This makes it possible to automate business management and efficiency improvement proposals for factory robots and provide managers with feedback for effective business operations in real time.

[1225] Definitions of important words

[1226] "User" means any individual or legal entity that uses the System and obtains Business Records.

[1227] "Business records" are data related to a user's business, and include information such as operating hours, task completion times, and the number of errors that have occurred.

[1228] "Server" means a central processing unit that receives, analyzes, stores, and provides feedback on data on the cloud or on-premise.

[1229] The "analysis means" refers to algorithms or software that analyzes the business record data received by the server and identifies the business process.

[1230] "Efficiency proposals" are proposals for optimization based on the duration, frequency, and repetitive tasks of work, generated using analytical tools.

[1231] The "feedback means" is a means for providing the generated efficiency improvement proposals to the user, and for reviewing and optimizing the business.

[1232] A "factory robot" is an automated mechanical device that performs various tasks in a factory.

[1233] "Operating time" refers to data that indicates the total amount of time that a factory robot is actually active.

[1234] "Task completion time" refers to the time it takes to complete a particular task or operation.

[1235] "Number of errors" refers to data that counts the number of errors and breakdowns that occur when a factory robot is working.

[1236] The "collection means" refers to the hardware and software used to collect business record data from factory robots at regular intervals and send it to a server.

[1237] An "administrator" is a person or role that monitors the operation of factory robots and optimizes business operations based on efficiency suggestions.

[1238] MODE FOR CARRYING OUT THE INVENTION

[1239] The present invention is a system aimed at managing the work of factory robots and improving their efficiency. Specifically, it collects users' work records, analyzes them on a server, generates efficiency proposals, and provides feedback.

[1240] Overall system configuration

[1241] The system mainly includes the following means:

[1242] 1. Data collection method: The user records the operating time of the factory robot, the time to complete a task, and the number of errors that occur.

[1243] 2. Data transmission means: The collected business record data is automatically transmitted from the terminal to the server at regular intervals.

[1244] 3. Data analysis means: The server analyzes the received business record data, identifies the business process, and generates efficiency proposals based on the analysis results.

[1245] 4. Feedback means: The generated efficiency proposals are fed back to the administrator (user) by the server and displayed in the form of a visual dashboard.

[1246] Data collection methods

[1247] Users can use dedicated software to obtain the work records of factory robots. This software has the function of recording the robot's operating time, task completion time, and number of errors in real time. For example, the operating time (10:00 - 10:30) and number of errors (5) of a welding robot can be recorded.

[1248] Data transmission method

[1249] The collected data is sent to a server at regular intervals (e.g., every hour) via the smartphone, allowing data to be collected and transferred to the server without any user intervention.

[1250] Data Analysis Methods

[1251] The server analyzes the received data using algorithms to identify the duration, frequency, and repetitive tasks of tasks (for example, how many times a particular task is repeated per day and how long it takes), and uses generative AI models to create prompts that generate efficiency suggestions.

[1252] Efficiency proposal generation method

[1253] The server uses generative AI models to generate efficiency recommendations, including the adoption of robotic process automation (RPA), voice input, and generative artificial intelligence (AI). For example, it might suggest the adoption of RPA to automate a specific welding task.

[1254] Feedback Methods

[1255] The generated efficiency proposals are fed back to the manager in the form of a dashboard by the server, who can then view the dashboard on their smartphone to review and optimize business processes.

[1256] Prompt Sentence Examples

[1257] Analyze the operating data of factory robots and generate efficiency improvement proposals.

[1258] Data content: { "robot_id": "robot_1", "timestamp": 1672522560, "operation": "welding", "duration": 1200, "error_count": 0}

[1259] Output format: JSON

[1260] Proposal content: Introduction of RPA for automation, optimization of work steps, maintenance proposals to reduce error rates, etc.

[1261] In this way, the present invention can effectively support the management and efficiency of factory robot operations and provide real-time feedback.

[1262] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1263] Program processing flow

[1264] Step 1:

[1265] Users use specialized software to record factory robot operating times, task completion times, and error occurrences in real time.

[1266] Input: Factory robot operating status

[1267] Output: Operating time, task completion time, number of errors

[1268] Specific operation: The user starts the software and presses the "Start" button when the robot starts working and the "End" button when it finishes. If an error occurs during this process, the details of the error are also recorded.

[1269] Step 2:

[1270] The collected data is automatically sent to a server via smartphone at regular intervals (e.g., every hour).

[1271] Input: Data on operation time, task completion time, and number of errors

[1272] Output: Data sent to the server

[1273] Specific operation: Collected data is sent to the server via HTTP at regular intervals. The data is in JSON format and includes uptime and error information.

[1274] Step 3:

[1275] The server analyzes the received data and identifies the business process.

[1276] Input: Data received by the server

[1277] Output: Analysis results (identification of business processes)

[1278] What it does: The server stores the received data in a database and uses analytical algorithms to analyze the data, for example, to identify the duration, frequency, and repetitive tasks of each task. It also cleanses the data using AI models as needed.

[1279] Step 4:

[1280] The server generates efficiency suggestions based on the analysis results.

[1281] Input: Analysis results (business processes stored in the database)

[1282] Output: Efficiency proposals

[1283] Specific operation: A prompt sentence is input into the generative AI model to generate efficiency improvement proposals. An example of a prompt sentence is, "Analyze the operation data of factory robots and generate efficiency improvement proposals." The generated efficiency proposals include RPA implementation, optimization of work steps, and maintenance proposals to reduce error rates.

[1284] Step 5:

[1285] The server provides feedback of the generated efficiency proposals to the administrator (user).

[1286] Input: Efficiency proposal

[1287] Output: Feedback dashboard displayed on the administrator's smartphone

[1288] Specific operation: The server converts the generated proposals into a visually easy-to-read dashboard format and sends a notification to the user's smartphone. The user can then view the dashboard on their smartphone and review the details of the efficiency proposals.

[1289] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1290] This invention combines an emotion engine with a system that acquires and analyzes a user's work records to generate efficiency suggestions and provide feedback to the user. This system makes it possible to provide suggestions that not only improve work efficiency but also take into account the user's emotional state.

[1291] Overall system overview

[1292] This system acquires the user's work records, sends them to a server for analysis, and then uses an emotion engine to identify the user's emotional state and generate efficiency suggestions.These suggestions are then fed back to the user to optimize their work.

[1293] Data Collection Phase

[1294] When a user starts a task using a dedicated application, the start time and content are recorded. For example, "09:00 - Started checking email," "09:30 - Ended checking email," etc. In addition, the device records operation logs (keystrokes, app usage time, browser history, etc.) in the background.

[1295] The collected business record data is automatically sent from the device to the server at regular intervals (e.g., every hour), reducing the user's workload and streamlining data collection.

[1296] Data analysis phase

[1297] The server receives the business record data sent from the device and stores it in a database. It then performs preprocessing, checks for inconsistencies and missing values, and performs data cleansing, such as correcting discrepancies between start and end times.

[1298] The server then analyzes the data using analytical algorithms to identify the duration, frequency, and repetitive tasks of each task and assess potential efficiency improvements.

[1299] Emotion Engine

[1300] The system adds an emotion engine that analyzes user input and behavioral data to identify the user's emotional state. For example, it estimates the user's current emotional state (e.g., stress, fatigue, joy, etc.) from keystroke speed, speech tone, and past behavioral patterns.

[1301] The emotional state identified by the emotion engine is integrated into the server's analysis results and influences efficiency suggestions. For example, if a user is in a high-stress state, the system will suggest ways to reduce workload or become more relaxed.

[1302] Proposal of efficiency methods

[1303] Based on the analysis results and the output of the emotion engine, the server generates efficiency suggestions, which may include:

[1304] Automation using RPA: For example, automating tasks such as replying to emails according to specific templates.

[1305] Voice input: If you are taking too long to type a sentence, we suggest you use a voice input tool.

[1306] Generative AI: Suggestions for automatically generating reports from templates.

[1307] Suggesting breaks to reduce stress: Suggesting appropriate breaks depending on your emotional state.

[1308] Providing feedback

[1309] The server compiles the generated efficiency improvement proposals in the form of a dashboard. The proposals and their implementation procedures are presented to the user in a visually easy-to-read format. The server notifies the user of updates to the dashboard.

[1310] The device displays a dashboard to the user, allowing them to review the recommendations, allowing them to review their own business processes and implement the efficiency recommendations.

[1311] Implementing and reassessing efficiencies

[1312] Users can review their business processes based on the dashboard's suggestions and implement the suggested efficiency measures, such as setting up a new RPA bot or introducing a voice input tool.

[1313] The device then records the user's work activities again and transfers the new data to the server. The server then reanalyzes the data, evaluates the effectiveness of the suggestions, and makes further suggestions for improvement as necessary. This allows the user to continuously optimize their work.

[1314] Specific examples

[1315] For example, if the emotion engine detects that a user is feeling high stress while checking email, the server can suggest automating that task and suggest taking a moderate break.It can also provide instructions to set up automatic email replies using RPA, further improving work efficiency.

[1316] The processing flow will be explained below.

[1317] Step 1: Enter business records

[1318] A user uses a dedicated application to record the start and end of work, for example, "09:00 - Start checking email" and "09:30 - End checking email."

[1319] Step 2: Collecting operational logs

[1320] The device collects operation logs in the background (keystrokes, application usage time, web browser history, etc.), allowing detailed business data to be obtained.

[1321] Step 3: Send business records

[1322] The terminal automatically sends collected business record data to the server at regular intervals (e.g., every hour).

[1323] Step 4: Receiving and storing data

[1324] The server receives the business record data sent from the terminal and stores it in a database.

[1325] Step 5: Preprocessing the data

[1326] The server performs pre-processing on the data received, checking for inconsistencies and missing values ​​and cleansing the data as needed, for example, fixing discrepancies between start and end times.

[1327] Step 6: Analyze business processes

[1328] The server uses analytical algorithms to analyze the work record data, identifying the duration, frequency, and repetitive tasks of each task and identifying areas where work can be streamlined.

[1329] Step 7: Collect emotion data

[1330] The device uses an emotion engine to recognize the user's emotional state based on their input and behavioral data, such as analyzing keystroke speed and voice input tone.

[1331] Step 8: Emotion Engine Analysis

[1332] The server integrates the output of the emotion engine into the analysis results and performs an analysis that takes into account the user's emotional state (e.g., stress, fatigue, joy).

[1333] Step 9: Generate efficiency proposals

[1334] The server generates efficiency improvement proposals based on the results of business process analysis and the output of the emotion engine. Proposals include automation using RPA, use of voice input, utilization of generative artificial intelligence (AI), and break suggestions.

[1335] Step 10: Personalize your suggestions

[1336] The server customizes the suggestions to suit the user's work style and emotional state, providing the user with the most effective efficiency techniques.

[1337] Step 11: Generate the dashboard

[1338] The server compiles the generated efficiency suggestions into a visually appealing dashboard, including details of the suggestions and steps to implement them.

[1339] Step 12: Notify users

[1340] The server notifies the user of updates to the dashboard, and the user can then review the suggestions.

[1341] Step 13: View the Dashboard

[1342] The device displays a dashboard for users to review and refine efficiency recommendations, allowing them to adjust their business processes based on the recommendations.

[1343] Step 14: Implementing efficiency measures

[1344] Users can follow the dashboard's suggestions to implement specific efficiency measures, such as setting up a new RPA bot or introducing a voice input tool.

[1345] Step 15: Collect new data

[1346] The device then records the user's work activities again and transfers the new data to the server, allowing the effectiveness of the efficiency measures implemented to be evaluated.

[1347] Step 16: Reanalyze and update proposals

[1348] The server re-analyzes the data based on the new data and evaluates the effectiveness of the previous proposal. If necessary, it generates further improvement proposals and provides them as feedback to the user. This allows users to continuously optimize their operations.

[1349] Example 2

[1350] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1351] In today's business environment, improving business efficiency is an important issue. However, simply automating business processes is not enough to achieve efficiency; the user's emotional state must also be taken into consideration. In particular, users who are under high stress or fatigue may not be able to enjoy the benefits of efficiency unless appropriate suggestions are made. Therefore, it is necessary to generate efficiency suggestions that take the user's emotional state into account.

[1352] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1353] In this invention, the server includes means for acquiring a user's work records, means for transmitting the work records to the server, means for analyzing the received work records and identifying work processes in the server, means for generating efficiency suggestions based on the analysis results and emotion analysis results, means for feeding back the generated efficiency suggestions to the user, means for exchanging the feedback with the user, and means for analyzing the emotional state of the user. This makes it possible to not only improve the efficiency of the user's work processes, but also to suggest stress reduction and timely breaks according to the user's emotional state.

[1354] A "user" is an individual or organization that uses the system to carry out business.

[1355] "Business records" are log information such as the start and end times and contents of tasks performed by a user during business hours.

[1356] The "server" is a central management system that receives business records, analyzes them, generates efficiency suggestions, and provides feedback to users.

[1357] "Sentiment analysis" is the process of identifying a user's emotional state based on their input and behavioral data.

[1358] "Efficiency proposals" are proposals that show ways to automate tasks, simplify work, and reduce stress.

[1359] "Robotic Process Automation (RPA)" is a technology for automating routine business processes.

[1360] "Voice input" is a method in which a user inputs information by voice using a microphone.

[1361] "Generative artificial intelligence (AI)" is an artificial intelligence technology that automatically generates new information and suggestions based on data.

[1362] A "dashboard" is a user interface that visually displays proposal content and data status.

[1363] A "database" is a collection of data that allows a system to efficiently store, manage, and retrieve data.

[1364] "Data cleansing" is the process of correcting inconsistencies and missing values ​​to ensure data consistency and accuracy.

[1365] This invention combines a system that acquires a user's work records, analyzes them, generates efficiency suggestions, and provides feedback to the user, with an emotion engine that identifies the user's emotional state. This system can provide suggestions that not only improve work efficiency but also take the user's emotional state into consideration.

[1366] Supported Hardware and Software

[1367] The server receives the user's business record data and emotional state data, analyzes them, and generates efficiency suggestions. The server mainly operates using a database (e.g., SQL server), analysis algorithms (e.g., Python's Pandas or Scikit-learn), and emotion engines (e.g., NLP technology). The server displays the generated suggestions in a dashboard format and notifies the user.

[1368] The device collects business records and operation logs when the user performs work, and transmits the data to a server at regular intervals. The device receives user input using a dedicated application (e.g., a business record application), and records keystroke speed, application usage time, browser history, etc. in the background.

[1369] Users use a dedicated application to input the start and end times of their work and the details of their work, then confirm and implement the efficiency suggestions. The emotion engine also analyzes the user's operations, input speed, and tone of voice to identify their emotional state.

[1370] Examples of specific examples and prompts

[1371] For example, a user can enter a task record such as "09:00 - Started checking email" into the app, and the device will simultaneously record the user's typing speed and the time spent using the app. This data is automatically sent to the server, which analyzes the task record to determine what tasks the user is spending time on and the associated emotional state (e.g., stress or fatigue).

[1372] Based on the analysis results, the server generates efficiency proposals, including improvement measures such as "automating email replies with RPA," "introducing a voice input tool," and "taking breaks at appropriate times." These proposals are displayed on a dashboard so that users can check them.

[1373] Examples of prompts are:

[1374] "Generate efficiency suggestions based on the user's work record data and emotional state."

[1375] "Analyze your work processes with algorithms that offer suggestions for automating tasks and reducing stress."

[1376] These prompts allow the generative AI model to be appropriately utilized to support the user in improving work efficiency. This system is effective as a method for integrating work efficiency and emotional state management.

[1377] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1378] Step 1: Data collection

[1379] The user launches a dedicated application and enters the start and end times and details of their work. For example, they might enter "09:00 - Start checking email." In addition, the device records keystrokes, app usage time, browser history, and other operation logs in the background. The input data (work records and operation logs) is saved on the device.

[1380] Step 2: Send data

[1381] The terminal sends the collected business record data and operation logs to the server in batches at regular intervals (for example, every hour). This sending process is automated to reduce the user's workload. The input data is sent from the terminal to the server and stored in a database.

[1382] Step 3: Data storage and cleansing

[1383] The server receives data from the terminal and stores it in a database. It then checks the stored data for inconsistencies and missing values, and performs data cleansing to correct any discrepancies, such as discrepancies between start and end times. Input data (business records and operation logs) is filtered and stored as clean data.

[1384] Step 4: Data analysis

[1385] The server analyzes the clean data. The analysis identifies the time, frequency, and repetitive tasks for each task. For example, it evaluates whether employees spend 30 minutes checking email every day. The input data (clean data) is analyzed by an analytical algorithm to evaluate the possibility of improving the efficiency of business processes. The output is the business analysis results.

[1386] Step 5: Sentiment Analysis

[1387] The server uses an emotion engine to analyze the user's emotional state. For example, it can estimate whether the user is feeling stressed based on their keystroke speed, operation patterns, and past behavioral data. Similarly, when using voice input, it can also analyze emotions from the tone of their speech. The input data (operation logs and past behavioral data) is analyzed by an analysis algorithm to obtain emotion analysis results.

[1388] Step 6: Generate efficiency proposals

[1389] The server integrates the results of task analysis and emotion analysis to generate efficiency suggestions. For example, if a user is in a high-stress state, it may suggest simplifying tasks, introducing RPA tools, using voice input, or taking breaks. The input data (task analysis results and emotion analysis results) is analyzed, and efficiency suggestions are generated.

[1390] Step 7: Feedback on proposal

[1391] The server compiles the generated efficiency proposals in a dashboard format and provides feedback to the user. The dashboard displays the proposals in a visually easy-to-read format, making it easier for the user to confirm the proposals. The input data (efficiency proposals) is displayed on the dashboard and notified to the user.

[1392] Step 8: Implement and reassess efficiency

[1393] The user reviews their business processes according to the dashboard's suggestions and implements the proposed efficiency measures. For example, they set up a new RPA bot and introduce a voice input tool. The device re-records the user's new business process and sends the new data to the server. The server re-analyzes the new data, evaluates the effectiveness of the proposals, and makes further improvement proposals as necessary. The input data (new business records) is re-analyzed, and an evaluation of the effectiveness of the proposals is obtained.

[1394] (Application example 2)

[1395] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1396] Conventional work efficiency systems make efficiency improvement suggestions without considering the user's emotional state, which can result in suggestions that ignore stress and fatigue. Furthermore, there is a lack of analysis of the emotional state of factory workers and real-time work automation support, making it difficult to achieve both occupational safety and work efficiency. The purpose of this invention is to solve these problems and realize efficiency improvement suggestions and work automation that take the user's emotional state into account.

[1397] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1398] In this invention, the server includes means for acquiring a user's work records, means for transmitting the work records to the server, means for analyzing the work records received by the server and identifying work processes, means for generating efficiency improvement proposals based on the analysis results and the user's emotional state, means for feeding back the generated efficiency improvement proposals to the user, means for exchanging the feedback with the user, means for acquiring worker status data (e.g., heart rate, sweat rate), means for analyzing the worker's emotional state, means for generating work automation instructions for a work robot based on the emotional state and the analyzed data, and means for transmitting the proposed work automation instructions to the work robot and having it execute them. This enables efficiency improvement proposals that reflect the user's emotional state and real-time work automation support that corresponds to the emotional states of workers in a factory.

[1399] The "means for acquiring user's work records" refers to a device or system that has the function of recording and collecting information such as the type of work performed by the user, the start time, and the end time.

[1400] The "means for transmitting the business records to the server" refers to a device or system that includes communication functions and protocols for transferring information from the user's terminal to the data center.

[1401] The "means for analyzing the business records received on the server and identifying the business process" refers to software or hardware that has the function of processing the business record data received on the server and analyzing the business content, its progress, etc.

[1402] "Means for generating efficiency proposals based on analysis results and the user's emotional state" refers to algorithms or systems that propose methods for improving work efficiency and reducing workload based on business data and emotion analysis results.

[1403] The "means for providing feedback of the generated efficiency improvement proposals to the user" refers to an interface or device for notifying the user of the results of the efficiency improvement proposals and conveying them by display or voice.

[1404] The "means for exchanging feedback with the user" is an interface with a two-way communication function that allows the user to check the proposal content and respond or make changes as necessary.

[1405] "Means for acquiring worker status data (e.g., heart rate, sweat rate)" refers to sensors or devices that measure the worker's biometric information in real time and collect it as data.

[1406] The "means for analyzing the worker's emotional state" refers to an analytical algorithm or system for estimating the worker's emotions, such as stress and fatigue, based on collected biometric information.

[1407] The "means for generating work automation instructions for a work robot based on emotional state and analytical data" refers to software and hardware that combines emotional state and business data to create optimal work plans and automation instructions.

[1408] The "means for transmitting the proposed work automation instructions to the work robot and having it execute them" refers to the communication means and control system for sending the created automation instructions to the robot and starting the actual work.

[1409] This invention is a "factory smart assistant" system that makes suggestions based on work efficiency and the emotional state of the worker. This system acquires the user's work records, generates efficiency suggestions based on the analysis results and the worker's emotional state, and also issues instructions to a work robot to automate the work.

[1410] System configuration

[1411] 1. User Device:

[1412] Data collection function: The user device records the start and end times of work, as well as the details of the work. In addition, smart glasses are used to collect biometric information (heart rate, sweat rate, etc.) while the user is working.

[1413] Communication function: Collected data is sent to a server at regular intervals via Wi-Fi or Bluetooth.

[1414] 2. Server:

[1415] Data analysis function: The server analyzes the received business records and identifies the business processes. The analysis includes data cleansing.

[1416] Emotion engine: Analyzes the worker's biometric information and estimates their emotional state (stress, fatigue, etc.). This emotion engine uses machine learning algorithms.

[1417] Efficiency suggestion generation function: Generates efficiency and safety suggestions based on the analysis results and emotional state. Suggestions include using RPA, suggesting voice input, suggesting breaks, etc.

[1418] Work robot control function: Transmits the proposed work automation instructions to the work robot and controls the robot to perform the appropriate work.

[1419] 3. Feedback function:

[1420] User interface: The generated proposals are displayed to the user in a dashboard format, allowing them to review and modify the proposals as needed.

[1421] Data processing and calculation

[1422] Data recording and collection:

[1423] The smart glasses collect biometric information (heart rate, sweat rate) and work records of workers wearing them in real time.

[1424] Sending data:

[1425] The collected data is sent to a server via Wi-Fi or Bluetooth.

[1426] Data analysis and sentiment estimation:

[1427] The server cleanses the received data and uses analysis algorithms to identify business processes. The emotion engine then estimates the emotional state based on biometric information. This is done using machine learning algorithms using Python (e.g., TensorFlow, Scikit-learn).

[1428] Generate efficiency suggestions:

[1429] Based on the results of the analysis and sentiment estimation, a generative AI model is used to generate efficiency recommendations, such as using RPA or recommending breaks.

[1430] Instructions to the working robot:

[1431] Based on the efficiency improvement proposals, we generate automation instructions for the work robots and transmit them via wireless communication. We use PLCs (Programmable Logic Controllers) and ROS (Robotics Operating System) to instruct the robots to automatically execute specific tasks.

[1432] Specific examples

[1433] For example, when a user wears smart glasses, the server performs the following analyses based on the work records and biometric data collected from the start of work in the morning until the end of the workday:

[1434] The worker's heart rate exceeded 90 bpm between 10:00 and 11:00, indicating that he was in a state of high stress.

[1435] Based on work records and emotional state, the server suggests taking a break and leaving repetitive tasks to a robot.

[1436] Example prompt for a generative AI model:

[1437] Generate efficiency suggestions based on the following work records and sensor data:

[1438] Work record: Start time 09:00, Work content Maintenance

[1439] Sensor data: Heart rate 90, slight sweating

[1440] Proposal details:

[1441] High stress: Proposal to delegate repetitive tasks to robots

[1442] If fatigue is detected: Advise appropriate rest

[1443] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1444] Step 1:

[1445] The device acquires the user's work records. When the user starts a task, the device records the start time, work content, and end time of the task. It also collects biometric information (heart rate, sweat rate, etc.) in real time through the smart glasses. The input is the user's operation log and sensor data, and the output is the recorded work data and biometric information. Specifically, the device acquires data such as heart rate and sweat rate from the smart glasses' sensors and stores it in the device's memory.

[1446] Step 2:

[1447] The device sends the collected business record data and biometric information to the server at regular intervals. The input is the recorded business data and biometric information, and the output is the data sent to the server. Specifically, the device executes a process to send data to the server via Wi-Fi or Bluetooth communication.

[1448] Step 3:

[1449] The server analyzes the received business record data. The input is the business data and biometric information sent from the terminal, and the output is the analyzed business process data. Specific operations include data cleansing, time series analysis, and frequency analysis to identify task times, frequency, and repetitive tasks.

[1450] Step 4:

[1451] The server uses an emotion engine to analyze the worker's emotional state. The input is biometric information (heart rate, sweat rate, etc.), and the output is an estimated emotional state (stress, fatigue, etc.). Specifically, it uses a machine learning algorithm to classify and analyze the emotional state based on the biometric information.

[1452] Step 5:

[1453] The server generates efficiency improvement proposals based on the analysis results and emotional state. The inputs are business process data and emotional state information, and the output is efficiency improvement proposals. Specifically, the server combines business data and emotional data, inputs prompts to the generative AI model, and generates proposals.

[1454] Step 6:

[1455] The server then feeds back the generated efficiency suggestions to the user. The input is the efficiency suggestions, and the output is a feedback notification to the user. Specifically, the server visually displays the suggestions on a dashboard and sends a notification to the user's device.

[1456] Step 7:

[1457] The server generates work automation instructions for the work robot based on the emotional state and analysis data. The input is business process data and emotional state information, and the output is automation instructions. Specifically, if the emotional state is highly stressful, the server generates and sends instructions to delegate repetitive work to the robot.

[1458] Step 8:

[1459] The robot receives and executes the automation instructions proposed by the server. The input is the automation instructions from the server, and the output is the actual work performed. In terms of specific operations, the robot performs appropriate actions based on the received instructions to complete the work.

[1460] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1461] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1462] 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 the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1463] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1464] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1465] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1466] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1467] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1468] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1469] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1470] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1471] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1472] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1473] 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.

[1474] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1475] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1476] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1477] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1478] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1479] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1480] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1481] The following is further disclosed regarding the above embodiment.

[1482] (Claim 1)

[1483] a means for obtaining a user's business records;

[1484] means for transmitting the business records to a server;

[1485] means for analyzing the business records received in the server and identifying business processes;

[1486] a means for generating efficiency recommendations based on the analysis results;

[1487] a means for feeding back the generated efficiency proposals to the user;

[1488] The system includes means for communicating said feedback to a user.

[1489] (Claim 2)

[1490] 2. The system of claim 1, wherein the analysis means identifies the duration, frequency, and repetitive tasks of a task.

[1491] (Claim 3)

[1492] The system of claim 1, wherein the efficiency proposal generation means includes proposals for robotic process automation (RPA) for automation, the use of voice input, and the use of generative artificial intelligence (AI).

[1493] "Example 1"

[1494] (Claim 1)

[1495] a means for obtaining a user's business records;

[1496] means for transmitting the business records to a server;

[1497] means for preprocessing the received business records in the server and performing data cleansing;

[1498] A means for analyzing the preprocessed data and identifying business processes;

[1499] a means for generating efficiency recommendations based on the analysis results;

[1500] A means of providing feedback to users on the generated efficiency proposals in the form of a dashboard; and

[1501] means for communicating said feedback to the user and reassessing the effectiveness of the suggestions;

[1502] A system including:

[1503] (Claim 2)

[1504] 2. The system of claim 1, wherein the analysis means identifies the duration, frequency, and repetitive tasks of a task and updates the analysis results based on newly recorded data.

[1505] (Claim 3)

[1506] The system of claim 1, wherein the efficiency suggestion generation means includes suggestions for automation using a generative AI model based on prompt sentences, the use of robotic process automation, the use of voice input tools, and the utilization of generative artificial intelligence.

[1507] "Application Example 1"

[1508] (Claim 1)

[1509] a means for obtaining a user's business records;

[1510] means for transmitting the business records to a server;

[1511] means for analyzing the business records received in the server and identifying business processes;

[1512] a means for generating efficiency recommendations based on the analysis results;

[1513] a means for feeding back the generated efficiency proposals to the user;

[1514] means for communicating said feedback to a user;

[1515] a means for recording the operating time, task completion time, and error occurrence count of the robot in the factory;

[1516] means for collecting and transmitting the recorded data to a server at regular intervals;

[1517] A means for generating a proposal for improving the efficiency of a factory robot based on the collected data;

[1518] The system includes a means for feeding back suggestions regarding the robot to an administrator.

[1519] (Claim 2)

[1520] 2. The system of claim 1, wherein the analysis means identifies the duration, frequency, and repetitive tasks of a task.

[1521] (Claim 3)

[1522] The system of claim 1, wherein the efficiency proposal generation means includes proposals for robotic process automation (RPA) for automation, the use of voice input, and the use of generative artificial intelligence (AI).

[1523] "Example 2: Combining Emotion Engines"

[1524] (Claim 1)

[1525] a means for obtaining a user's business records;

[1526] means for transmitting the business records to a server;

[1527] means for analyzing the business records received in the server and identifying business processes;

[1528] means for generating efficiency recommendations based on the analysis results and the sentiment analysis results;

[1529] a means for feeding back the generated efficiency proposals to the user;

[1530] means for communicating said feedback to a user;

[1531] A system comprising means for analyzing an emotional state of the user.

[1532] (Claim 2)

[1533] 2. The system according to claim 1, wherein the analysis means identifies the time, frequency, and repetitive tasks of the work and integrates the results of sentiment analysis.

[1534] (Claim 3)

[1535] The system of claim 1, wherein the efficiency suggestion generating means includes robotic process automation (RPA) for automation, use of voice input, utilization of generative artificial intelligence (AI), and suggestions for stress reduction according to emotional state.

[1536] "Application example 2 when combining emotion engines"

[1537] (Claim 1)

[1538] a means for obtaining a user's business records;

[1539] means for transmitting the business records to a server;

[1540] means for analyzing the business records received in the server and identifying business processes;

[1541] means for generating efficiency suggestions based on the analysis results and the user's emotional state;

[1542] a means for feeding back the generated efficiency proposals to the user;

[1543] means for communicating said feedback to a user;

[1544] A means for acquiring worker status data (e.g., heart rate, sweat rate);

[1545] means for analyzing the emotional state of the worker;

[1546] A means for generating work automation instructions for a work robot based on the emotional state and the analysis data;

[1547] a means for transmitting the proposed work automation instructions to the work robot and causing it to execute the instructions;

[1548] A system including:

[1549] (Claim 2)

[1550] 2. The system of claim 1, wherein the analysis means identifies the duration, frequency, and repetitive tasks of a task.

[1551] (Claim 3)

[1552] The system according to claim 1, characterized in that the efficiency suggestion generating means includes suggestions for robotic process automation (RPA) for automation, the use of voice input, the use of generative artificial intelligence (AI), and suggestions for breaks based on the emotional state of the worker and suggestions for work assistance by a work robot. [Explanation of symbols]

[1553] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for obtaining a user's business records; means for transmitting the business records to a server; means for analyzing the business records received in the server and identifying business processes; a means for generating efficiency recommendations based on the analysis results; a means for feeding back the generated efficiency proposals to the user; and means for communicating said feedback to a user.

2. 2. The system of claim 1, wherein the analysis means identifies the time, frequency, and repetitive tasks of a task.

3. The system according to claim 1, wherein the efficiency improvement proposal generating means includes proposals for robotic process automation, use of voice input, and utilization of generative artificial intelligence.

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