system

Abstract

Provide a system. [Solution] a means for monitoring the user's computer operations in real time and transmitting the collected operation data to a server; a means for analyzing the operation data in the server and evaluating the user's business performance; A means for generating and notifying specific feedback to users based on the analysis results; A means of linking with different business tools and collecting and analyzing data from business tools, A system including:

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, desk workers are required to possess a variety of skills in order to carry out their daily work efficiently and effectively. However, once employees leave their entry-level roles, opportunities for guidance and feedback through traditional mentoring systems decrease, resulting in a lack of support for individual employees to continuously improve their skills. As a result, many employees are missing out on opportunities to improve their skills.

[0005] Furthermore, the wide variety of tools used by employees makes it difficult to provide comprehensive, centralized support for improving skills, which not only hinders efficient work execution but also affects the quality of work. [Means for solving the problem]

[0006] The present invention solves the above-mentioned problems by providing a system that monitors user computer operations in real time, collects operation data, and transmits the collected data to a server. This system includes the following components.

[0007] The system is provided with means for monitoring the user's computer operations in real time and transmitting the collected operation data to a server.

[0008] The server includes means for analyzing the operation data and evaluating the user's business performance.

[0009] Based on the analysis results, it has the means to generate and notify specific feedback to users.

[0010] Furthermore, it includes means for linking with different business tools and collecting and analyzing data from the business tools.

[0011] This configuration allows users to effectively and sustainably improve their skills, leading to improved work efficiency and quality. For example, they can receive specific feedback on basic business skills such as email, document creation, and project management. Furthermore, integration with project management tools, messaging tools, and document creation tools enables comprehensive skill improvement support.

[0012] "User" refers to a desk worker who uses the system and performs various tasks.

[0013] "Computer operation" refers to all actions and commands a user performs on a computer.

[0014] "Real time" refers to a situation in which the system processes information as the user's computer operations are performed.

[0015] "Operational data" refers to data that includes detailed information such as the actions a user performs on a computer, the operations they perform, and the time they perform the operations.

[0016] "Server" refers to the central management system for analyzing collected operation data and providing feedback to users.

[0017] "Analysis" refers to the process of analyzing collected operational data and evaluating users' work performance and skill level.

[0018] "Business performance" refers to the results of an evaluation of the efficiency, accuracy, quality, etc., of a user's work when carrying out their tasks.

[0019] "Feedback" refers to information such as advice, improvements, and evaluations provided to users based on the analysis results.

[0020] "Notification" refers to the act of communicating feedback to the user, including pop-up messages, emails, in-app notifications, etc.

[0021] "Business tools" refers to software and applications that users use to carry out their work, including project management tools, messaging tools, and document creation tools.

[0022] "Integration" refers to the act of a system exchanging information with other business tools, enabling comprehensive data collection and analysis. [Brief explanation of the drawings]

[0023] [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 showing 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

[0024] 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.

[0025] First, the terms used in the following description will be explained.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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."

[0031] [First embodiment]

[0032] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0033] 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.

[0034] 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).

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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.

[0039] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0040] 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.

[0041] 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.

[0042] 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.

[0043] 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."

[0044] This invention is an "AI personal mentor" system designed to improve the basic business skills of desk workers. The system monitors the user's computer operations in real time, collects operational data, and sends it to a server that analyzes the data and provides feedback. This feedback includes specific advice and suggestions for improvement to improve the user's work skills.

[0045] Server-side processing

[0046] The server receives data on the user's operations. The user's computer operations are collected in real time, and the necessary data is sent to the server. The server then analyzes the received data. The analysis uses the artificial intelligence model "Gemini (registered trademark)" to evaluate the user's work performance. This evaluation includes, for example, email sending and receiving speed, document creation accuracy, and project management ability.

[0047] The server then generates specific feedback based on the analysis results. This feedback includes suggestions and advice for improvement, helping users improve their work skills. For example, specific advice such as "make email subject lines more specific" may be included. The server also integrates with various work tools, collecting data from project management tools, messaging tools, document creation tools, and more, and adding this data to the analysis to provide a comprehensive skill assessment.

[0048] Terminal side processing

[0049] The user's device monitors computer operations in real time. The operation data generated by this monitoring is sent to a server at regular intervals. When the device detects a specific operation (such as sending an email or creating a document), it records that information. The collected data is sent to the server and used for analysis.

[0050] The device also displays real-time feedback from the server, which is displayed to the user as a pop-up message or notification, allowing them to take immediate action. For example, if a user is composing an email, a pop-up message will immediately tell them to be more specific in the subject line.

[0051] User operations

[0052] Users can benefit from the system by simply performing their daily tasks as usual. No special operations or settings are required. When a user logs in, the system automatically collects operational data and sends it to the server. The user receives feedback from the server, checks it, and reflects it in their next task. For example, if the feedback advises them to "improve the structure of the document," they will keep that advice in mind the next time they create a document.

[0053] For example, if a user manages tasks using a project management tool, the server collects and analyzes the task completion status and comments, allowing it to provide detailed feedback on the user's project management capabilities. For example, it may provide advice such as "improve task prioritization."

[0054] In this way, the system of the present invention can continuously improve the user's basic business skills by monitoring and analyzing the user's business data in real time and providing feedback.

[0055] The processing flow will be explained below.

[0056] Program processing flow (detailed explanation for each step)

[0057] Server-side processing

[0058] Step 1: Collect data

[0059] The server receives operational data sent in real time from the user's device. The user's operations (e.g., sending and receiving emails, creating documents, and operations in project management tools) are recorded and sent to the server. The server stores this data in temporary storage.

[0060] Step 2: Analyze the data

[0061] The server analyzes the received operation data. This is where the multimodal AI "Gemini" comes into play. The AI ​​analyzes the content of each operation and evaluates business performance. For example, evaluation criteria include email response speed, document structure, and task prioritization.

[0062] Step 3: Generate feedback

[0063] The server generates feedback based on the analysis results, including suggestions for improvement and specific advice for the user, such as "You should be more specific in your email subject lines."

[0064] Step 4: Integration with business tools

[0065] The server connects to business tools such as project management tools, messaging tools, and document creation tools. This allows it to collect operational data from each tool and add it to the analysis. For example, it obtains task completion status and comment contents from the project management tool.

[0066] Step 5: Feedback notification

[0067] The server sends the generated feedback to the user's device via pop-up, email, in-app notification, etc. The user can check the feedback in real time and apply it to their work.

[0068] Terminal side processing

[0069] Step 1: Monitoring user actions

[0070] The device monitors the user's computer operations in real time, detects specific operations (e.g., sending emails, creating files), records the operations, and sends the data to the server at regular intervals.

[0071] Step 2: Sending data

[0072] The terminal sends the collected operation data to the server. The data is sent periodically and synchronized in real time, allowing the server to always have the latest operation data.

[0073] Step 3: Receive and view feedback

[0074] The device receives feedback from the server, which is then displayed to the user in real time, such as a pop-up notification saying, "Change the subject of the email to be more specific."

[0075] User behavior

[0076] Step 1: Getting the job done

[0077] Users can carry out their normal work. No special operations or settings are required, and they can use the system while performing their daily work.

[0078] Step 2: Review and respond to feedback

[0079] The user checks the feedback displayed on the device, and if necessary, reviews their work methods and applies the advice to their next work. For example, if the feedback suggests "improving the document headings," the user can apply that advice the next time they create a document.

[0080] In this way, the system of the present invention can monitor and analyze users' business data in real time and provide specific feedback, thereby improving users' basic business skills.

[0081] Example 1

[0082] 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."

[0083] Conventional work support systems for desk workers often do not fully utilize user operation data and are unable to provide real-time feedback. As a result, there are issues with users' work skill improvement being delayed and work efficiency not improving. In addition, they lack the functionality to integrate and analyze data from multiple business applications, making comprehensive skill evaluation difficult.

[0084] 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.

[0085] In this invention, the server includes means for monitoring a user's computer operations in real time and transmitting the collected operation data to an information processing device, means for analyzing the operation data in the information processing device and evaluating the user's work performance, and means for generating and notifying specific feedback to the user based on the analysis results. This makes it possible to provide immediate feedback using the user's operation data. In addition, by integrating data from multiple business applications and analyzing it using an artificial intelligence model, it becomes possible to provide comprehensive skill evaluations and specific improvements and advice.

[0086] "User" refers to an individual or organization that uses the System to carry out business.

[0087] "Computer operation" refers to a series of operations or actions that a user performs on a computer.

[0088] "Real-time" refers to near-simultaneous processing and feedback, with minimal delay.

[0089] "Operational data" refers to information about operations performed by a user on a computer, including, for example, data on sending and receiving emails, document creation, and project management.

[0090] "Information processing device" refers to a server or computer system for analyzing collected data and providing feedback to the user.

[0091] "Analysis" refers to the process of evaluating collected operational data to determine user performance.

[0092] "Feedback" refers to specific advice and improvements provided to users based on the analysis results.

[0093] "Business Applications" refers to software or tools that users use to perform their jobs, such as project management tools or messaging tools.

[0094] "Bundling" refers to aggregating multiple pieces of operation data over a fixed period of time and putting them together in a format that can be transmitted.

[0095] "Database" refers to an information management system for storing received operational data.

[0096] "Artificial Intelligence Model" refers to a machine learning or natural language processing algorithm used to analyze operational data and evaluate business performance.

[0097] "Areas for improvement" refers to specific areas that users should pay attention to in order to improve their work performance.

[0098] "Notification" refers to messages sent to users to convey analysis results and feedback.

[0099] This invention is a "work performance support system" for improving users' work skills. This system monitors users' computer operations in real time, transmits the collected operation data to a server, and provides specific feedback to users based on the analysis results.

[0100] Server-side processing

[0101] The server receives and stores operation data sent by the user. Specifically, the server, which functions as an information processing device, stores the operation data in a database. Next, this data is analyzed using an artificial intelligence model (for example, a machine learning algorithm or a natural language processing algorithm). The AI ​​model used is a generative AI model such as "Gemini." This analysis quantifies and evaluates the user's work performance. The server then generates specific feedback based on the analysis results and sends it to the user's device. This feedback includes advice and areas for improvement to improve work skills.

[0102] Terminal side processing

[0103] The user's terminal is installed with software that monitors computer operations in real time. This software detects user operation events (e.g., sending and receiving emails, document creation, project management, etc.) and records the operation data. The recorded data is bundled at regular intervals and sent to the server via an encrypted communication channel. In addition, the terminal receives feedback from the server in real time and displays it to the user as a pop-up message or notification. For example, while the user is typing the subject of an email, the feedback "Please make the subject of the email more specific" is displayed.

[0104] User operations

[0105] Users can simply carry out their work as usual without any special operations or settings. When they log in to the system, operation data is automatically collected and sent to the server. Users can check the feedback sent from the server and reflect it in their next work. For example, when creating a document, they can receive feedback such as "improve the document structure."

[0106] Specific examples

[0107] As a concrete example, consider the case where a user manages tasks using a project management tool. The server collects and analyzes the task completion status and comments, and provides detailed feedback on the user's project management skills. For example, advice such as "improve task prioritization" may be presented as feedback. In this way, by monitoring and analyzing the user's business data in real time and providing immediate feedback, the user's basic business skills are continuously improved.

[0108] Prompt Sentence Examples

[0109] "Please display the following feedback appropriately: The user is creating a document. The rating is 'The document is difficult to understand.'"

[0110] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0111] Step 1:

[0112] Start monitoring user computer operations

[0113] The user's terminal runs software for monitoring computer operations. This software detects and records the user's operation events (e.g., sending emails, creating documents, etc.) in real time. The input is the user's operation event, and the output is the recorded operation data. For example, when a user creates a new email, the subject, body, and recipient information of that email are recorded.

[0114] Step 2:

[0115] Bundling and sending operational data

[0116] The device bundles the operation data collected at regular intervals and aggregates it into a format that can be sent. This data is sent to the server via an encrypted communication channel. The input is the collected operation data, and the output is the bundled data. Specifically, all operations performed by the user over the course of one hour are compiled into a data set.

[0117] Step 3:

[0118] Receiving and storing data

[0119] The server receives bundled data sent from the terminal. The received data is stored in a database and later used for analysis. The input is the bundled data sent from the terminal, and the output is the data stored in the database. Specifically, the server stores the email transmission data it receives in a specified database.

[0120] Step 4:

[0121] Start of analysis using "generative AI models"

[0122] The server inputs the operation data stored in the database into the "generative AI model" and begins analysis. This model quantifies and evaluates the user's work performance. The input is the operation data in the database, and the output is the analysis results. Specifically, the generative AI model outputs evaluation values ​​for email sending speed and document accuracy.

[0123] Step 5:

[0124] Generate feedback based on analysis results

[0125] The server generates specific feedback based on the analysis results of the generative AI model. This feedback includes specific advice and areas for improvement to improve work skills. The input is the analysis results, and the output is the generated feedback. Specifically, this includes feedback such as "Make the subject line of the email more specific."

[0126] Step 6:

[0127] Sending feedback to users

[0128] The server sends the generated feedback to the user's device. The sent feedback is encrypted and notified to the user. The input is the generated feedback, and the output is the feedback sent to the user's device. As a specific operation, while the user is composing an email, a notification pops up urging the user to "make the subject more specific."

[0129] Step 7:

[0130] Reviewing user feedback and incorporating it into work

[0131] The user checks the feedback displayed on the device and reflects it in their next work. The input is the feedback displayed on the device, and the output is improved work performance. Specifically, the next time the user composes an email, they will be more specific in the subject line.

[0132] (Application example 1)

[0133] 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."

[0134] Improving the operational efficiency and work performance of robots in factories is a critical issue for workers. Conventional systems have struggled to monitor robot operation data in real time, analyze that data, and provide specific feedback. This has made it difficult for factory operators and engineers to continuously improve their robot operation skills and prevent errors before they occur. Therefore, there is a need for a system that can collect and analyze robot operation data in real time and provide specific feedback.

[0135] 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.

[0136] In this invention, the server includes: a means for monitoring user operations in real time and transmitting collected operation data to the server; a means for analyzing the operation data and evaluating the user's performance in the server; a means for generating and notifying specific feedback to the user based on the analysis results; a means for linking with different tools and collecting and analyzing data from the tools; a means for collecting robot operation data and periodically transmitting it to the server; and a means for displaying feedback from the server in real time on a terminal. This allows operators to receive specific improvements and advice on their robot operation in real time. This is expected to improve the work performance of factory operators and engineers and enable efficient robot operation.

[0137] "User" means a person or entity that utilizes the system to provide operational data and receive feedback therefrom.

[0138] "Operation data" is information about a series of actions performed by a robot or tool and the results of those actions.

[0139] A "server" is a central processing unit for receiving operational data, performing analysis, and generating feedback.

[0140] "Performance" refers to the efficiency and accuracy of a task performed by a user or robot.

[0141] "Real time" refers to a time frame in which events are processed as they occur.

[0142] "Tools" refers to the software and hardware used by users and robots to perform operations.

[0143] "Feedback" refers to specific advice and improvements generated based on the analysis results.

[0144] "Notifications" are a means of communicating feedback and other information to users.

[0145] The term "robot" refers to an automated mechanical device operated by an operator in a factory or the like.

[0146] "Data collection" is the process of collecting data about the robot and user operations.

[0147] "Analysis" is the process of evaluating collected data and measuring performance.

[0148] "Evaluation" is the act of quantitatively judging the performance of a user or robot based on the analysis results.

[0149] "Device" means the electronic device (e.g., tablet, smart glasses) used by the user to receive feedback.

[0150] This invention is a system designed to improve the operational efficiency and business performance of factory robots. This system includes a series of processes that monitors robot operation data in real time, analyzes the data, and generates feedback. An embodiment of this system will be described in detail below.

[0151] Hardware and Software Configuration

[0152] Server-side configuration

[0153] The server consists of the following hardware and software:

[0154] Hardware: High-performance central processing units (e.g., AWS EC2 instances, GCP Compute Engine)

[0155] Software: Artificial intelligence model for data analysis (generative AI model) (e.g., implemented with TENSORFLOW (registered trademark) or PyTorch), analysis script (e.g., Python), database (e.g., PostgreSQL)

[0156] The server receives and analyzes the collected operation data. The analysis uses data such as the robot's movement data, error occurrence status, and task completion time. This data is processed by a "generative AI model" to evaluate the robot's operation performance. Appropriate feedback is generated from the analysis results and notified to the user's device.

[0157] Terminal configuration

[0158] A user's device consists of the following hardware and software:

[0159] Hardware: Tablets, smart glasses, or other electronic devices

[0160] Software: Applications for feedback notifications (e.g. iOS / ANDROID applications)

[0161] The user's device receives feedback from the server in real time and displays it to the user as a pop-up message or notification, allowing the user to quickly check and respond to the feedback.

[0162] Process example

[0163] For example, consider a situation where a robot is assembling parts in a factory. In this case, the following process is executed:

[0164] 1. Data collection: Sensors and cameras installed on the robot collect operation data and error occurrence status in real time and periodically send it to a server.

[0165] 2. Data analysis: The received data is analyzed using a "generative AI model" to evaluate the robot's operational performance.

[0166] 3. Feedback generation: Based on the analysis results, specific feedback is generated to the user (e.g., "The installation of part A is unstable. Please adjust the motor torque setting").

[0167] 4. Notification: The feedback is notified to the user's device and displayed as a pop-up message.

[0168] Prompt Sentence Examples

[0169] Below are some examples of prompts used in this system:

[0170] Data collected: Robot operation data, error occurrence status, operation time

[0171] AI analysis result: "Part A installation operation is unstable"

[0172] Feedback generation: "Advise to adjust motor torque settings"

[0173] In this way, it becomes possible to provide appropriate feedback to users based on the operation data of factory robots, enabling efficient operation.

[0174] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0175] Step 1:

[0176] The terminal collects operational data (e.g., movement data, error occurrence status, task completion time) through sensors and cameras installed on the robot. This data is measured in real time.

[0177] Input: Robot operation data, error occurrence status, task completion time

[0178] Data processing: Collecting raw data from sensors and cameras

[0179] Output: Structured operation data

[0180] Step 2:

[0181] The terminal transmits the collected operation data to the server at regular intervals (e.g., every minute).

[0182] Input: Collected operational data

[0183] Data processing: Convert to JSON format etc. and send over the network

[0184] Output: Operation data received on the server side

[0185] Step 3:

[0186] The server then inputs the received operational data into a "generative AI model" for analysis, where the data is pre-processed and converted into a format suitable for the model.

[0187] Input: Operation data sent to the server

[0188] Data calculations: performance analysis using generative AI models (e.g., operation accuracy, error rate)

[0189] Output: Analysis results

[0190] Step 4:

[0191] The server generates specific feedback based on the analysis results from the generative AI model, such as advice on areas where errors frequently occur during robot operation and areas for improvement.

[0192] Input: Analysis results

[0193] Data arithmetic: Advice generation based on feedback generation algorithms

[0194] Output: Specific feedback (e.g., advice and improvements)

[0195] Step 5:

[0196] The server sends the generated feedback to the user's device in real time, either through pop-up messages or real-time notifications.

[0197] Input: Specific feedback

[0198] Data processing: generating notification formats (e.g., popup messages)

[0199] Output: Feedback displayed on the terminal

[0200] Step 6:

[0201] The device visualizes the received feedback to the user, allowing them to see improvements to their operation in real time.

[0202] Input: Feedback from the server

[0203] Data processing: Display feedback on the screen

[0204] Output: A feedback message for the user to see.

[0205] Step 7:

[0206] Users can adjust the robot's operation based on feedback received through the device, allowing them to refine their operation in real time.

[0207] Input: Feedback message

[0208] Data processing: Adjusting the robot's operation method

[0209] Output: Improved operation results

[0210] 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.

[0211] This invention is an "AI personal mentor" system designed to improve the basic business skills of desk workers, and is equipped with the ability to recognize and analyze the user's emotions. The system monitors the user's computer operations in real time, collecting operational and emotional data and sending it to a server, which then analyzes the data and provides feedback. This feedback includes specific advice and suggestions for improvement to improve the user's work skills.

[0212] Server-side processing

[0213] Data collection

[0214] The server receives operation data and emotion data sent in real time from the user's device. The user's operation details (e.g., sending and receiving emails, creating documents, operating project management tools) and emotion recognition data (e.g., facial expressions, voice, text input, etc.) are recorded and sent to the server. This data is temporarily stored in storage.

[0215] Data analysis

[0216] The server analyzes the received operation data and emotional data. This is where the multimodal AI "Gemini" and emotion engine come into play. The operation data and emotional data are combined and analyzed to evaluate the user's work performance and emotional state. For example, evaluation criteria include email response speed, document structure, task prioritization, emotional changes, and stress levels.

[0217] Generate feedback

[0218] The server generates feedback based on the analysis results. This feedback includes suggestions for improvement and specific advice to help users improve their work skills. It also includes a function to provide timely feedback based on emotional data. For example, if a user is feeling stressed, the server generates advice such as "take a break to relax."

[0219] Integration with business tools

[0220] The server connects to business tools such as project management tools, messaging tools, and document creation tools. This allows it to collect operational data from each tool and add it to the analysis. For example, it obtains task completion status and comment contents from the project management tool.

[0221] Feedback Notification

[0222] The server sends the generated feedback to the user's device via pop-up, email, in-app notification, etc. The user can check the feedback in real time and apply it to their work.

[0223] Terminal side processing

[0224] User Action Monitoring

[0225] The device monitors the user's computer operations in real time. It detects specific operations (e.g., sending emails or creating files) and records the details of those operations. At the same time, the device also collects emotional data such as the user's facial expressions, voice, and text input. This data is sent to a server at regular intervals.

[0226] Sending data

[0227] The device sends the collected operation data and emotion data to the server. Data transmission is performed periodically and synchronized in real time, allowing the server to always have the latest operation data and emotion data.

[0228] Receiving and viewing feedback

[0229] The device receives feedback from the server, which is then displayed to the user in real time. For example, a notification pops up telling the user to specifically change the subject of the email. The device also displays advice based on the user's emotional state.

[0230] User behavior

[0231] Business execution

[0232] Users can carry out their normal work. No special operations or settings are required, and they can use the system while performing their daily work.

[0233] Review and respond to feedback

[0234] The user checks the feedback displayed on the device, and if necessary, reviews their work methods and applies the advice to their next task. For example, if the feedback advises them to "improve the document headings," they can apply that advice the next time they create a document. Furthermore, by referring to advice based on emotional data, they can manage stress and control their emotions.

[0235] For example, if a user is using a project management tool to manage tasks, the server collects and analyzes the task completion status and comments. It also collects emotional data as the user progresses through the task and incorporates this data into the analysis. This allows the server to provide detailed feedback on the user's project management capabilities. For example, the server may provide advice such as "improve task prioritization" or "take short breaks when under stress."

[0236] In this way, the system of the present invention monitors and analyzes the user's business data and emotional data in real time and provides comprehensive feedback, thereby enabling the user to continuously improve their basic business skills and emotional management skills.

[0237] The processing flow will be explained below.

[0238] Server-side processing

[0239] Step 1: Collect data

[0240] The server receives operational data and emotional data sent from the user's device in real time. Specifically, the server records the user's operations (e.g., sending and receiving emails, creating documents, and using project management tools) and emotional recognition data (e.g., facial expressions, voice, text input, etc.) and sends them to the server.

[0241] Step 2: Save your data

[0242] The server stores the received operation data and emotion data in temporary storage, which is used in subsequent analysis steps.

[0243] Step 3: Analyze the data

[0244] The server analyzes the operation data and emotional data stored in the storage. This analysis is performed using the multimodal AI "Gemini" and an emotion engine. This evaluates the user's work performance and emotional state. For example, evaluation items include email response speed, document creation accuracy, task prioritization, and the user's stress level.

[0245] Step 4: Generate feedback

[0246] The server generates feedback based on the analysis results. This feedback includes specific advice and areas for improvement to help users improve their work skills. For example, advice such as "You should be more specific in the subject line of your emails" is generated. Feedback is also provided based on emotional data, and if a user is feeling stressed, advice such as "Take a break to relax" is provided.

[0247] Step 5: Integration with business tools

[0248] The server connects to business tools such as project management tools, messaging tools, and document creation tools. This allows it to collect operational data from each tool and add it to the analysis. For example, it obtains task completion status and comment contents from the project management tool.

[0249] Step 6: Notification of feedback

[0250] The server sends the generated feedback to the user's device via pop-up, email, in-app notification, etc. The user can check the feedback in real time and apply it to their work.

[0251] Terminal side processing

[0252] Step 1: Monitoring user actions

[0253] The device monitors the user's computer operations in real time, detecting specific operations (e.g., sending emails or creating files) and recording the details of those operations, as well as collecting emotional data such as the user's facial expressions, voice, and text input.

[0254] Step 2: Sending data

[0255] The device sends the collected operation data and emotion data to the server. Data transmission is performed periodically and synchronized in real time, allowing the server to always have the latest operation data and emotion data.

[0256] Step 3: Receive and view feedback

[0257] The device receives feedback from the server, which is then displayed to the user in real time. For example, a notification pops up telling the user to specifically change the subject of the email. The device also displays advice based on the user's emotional state.

[0258] User behavior

[0259] Step 1: Getting the job done

[0260] Users can carry out their normal work. No special operations or settings are required, and they can use the system while performing their daily work.

[0261] Step 2: Review and respond to feedback

[0262] Users can check the feedback displayed on their device, revise their work methods as needed, and apply the advice to their next task. For example, if they are notified to "improve the headings in a document," they can apply that advice the next time they create a document. Furthermore, they can also refer to advice based on emotional data to manage stress and control their emotions.

[0263] For example, if a user is using a project management tool to manage tasks, the server collects and analyzes the task completion status and comments. It also collects emotional data as the user progresses through the task and incorporates this data into the analysis. This allows the server to provide detailed feedback on the user's project management capabilities. For example, the server may provide advice such as "improve task prioritization" or "take short breaks when under stress."

[0264] Example 2

[0265] 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."

[0266] Conventional business support systems only analyzed user operation data and were unable to provide advice or suggestions for improvement that took into account emotional data such as the user's emotional state and stress level. This limited the ability to improve users' work performance and made it difficult to optimize overall work efficiency and emotional management. Furthermore, they lacked the ability to manage and analyze operation data from multiple business tools in an integrated manner, making it difficult to obtain comprehensive feedback.

[0267] 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.

[0268] In this invention, the server includes means for monitoring a user's computer operations in real time and transmitting collected operation data and emotional data to the server, means for analyzing the operation data and emotional data in the server and evaluating the user's work performance and emotional state, means for generating specific feedback to the user based on the analysis results and notifying them in a timely manner, and means for linking different work tools and collecting data from each work tool to add to the analysis. This makes it possible to simultaneously improve a user's work performance and optimize their emotional management, and provide comprehensive feedback from various angles.

[0269] "User" means an individual who uses the System to perform business operations.

[0270] "Computer operation" refers to a series of operations or actions that a user performs on a computer.

[0271] "Real-time" refers to processing and communication occurring immediately, without delay.

[0272] "Operational Data" refers to specific data about operations performed by a user on a computer.

[0273] "Emotion data" refers to data that indicates the user's emotional state, and includes facial expression changes, voice, text input, and the like.

[0274] "Server" refers to a central processing unit for analyzing collected operation data and emotion data and providing feedback.

[0275] "Analysis" refers to the process of examining collected data in detail and deriving its meaning and value.

[0276] "Business performance" refers to the efficiency and effectiveness with which users carry out their work.

[0277] "Evaluation" refers to judging a user's work performance and emotional state based on analyzed data.

[0278] "Feedback" refers to advice and improvements provided to users based on the analysis results.

[0279] "Timely notification" means that feedback is communicated to the user at an appropriate time.

[0280] "Business tools" refers to various software used to carry out business, such as project management tools, messaging tools, and document creation tools.

[0281] "Integration" refers to sharing data between different business tools and making it mutually usable.

[0282] "Collecting data and adding it to the analysis" refers to obtaining operational data from each business tool and incorporating it into the analysis process.

[0283] This invention is an "AI personal mentor" system designed to improve the basic business skills of desk workers, and is equipped with the ability to recognize and analyze the user's emotions. The system monitors the user's computer operations in real time, collecting operational and emotional data and sending it to a server, which then analyzes the data and provides feedback. This feedback includes specific advice and suggestions for improvement to improve the user's work skills.

[0284] Hardware and software used

[0285] The system uses the following hardware and software:

[0286] User device (PC, laptop, etc.)

[0287] Server (with high performance processing capabilities)

[0288] Multimodal AI "Gemini"

[0289] Sentiment Analysis Engine

[0290] Project management tools, messaging tools, document creation tools, etc.

[0291] Data collection and transmission

[0292] The device collects various operations performed by the user (for example, sending and receiving emails, creating documents, and using project management tools) as well as emotion recognition data such as facial expressions, voice, and text input in real time. This data is then sent to a server at regular intervals. Specific technologies used for this include data streaming technology and real-time communication protocols.

[0293] Data analysis

[0294] The server uses the multimodal AI "Gemini" and an emotion analysis engine to analyze the collected operation data and emotional data. This analysis evaluates the user's work performance (e.g., email response speed, document structure, task prioritization) and emotional state (e.g., stress level and emotional changes). Combining the operation data and emotional data allows for more detailed feedback.

[0295] Feedback generation and notification

[0296] The server generates specific feedback for the user based on the analysis results. This feedback includes suggestions and advice for improving the user's work skills (e.g., "improve the structure of the document" or "change the subject line of the email to be more specific"). It also provides timely feedback based on the user's emotional data. For example, it recommends "taking a short break" when the user is under high stress.

[0297] Integration with business tools

[0298] The server works with project management tools, messaging tools, document creation tools, etc., collecting operational data from each tool and adding it to the analysis, making it possible to provide feedback that integrates data from all the business tools used by the user.

[0299] Specific examples

[0300] When a user manages a task using a project management tool, the server collects and analyzes the task completion status and comments. It also collects and considers the user's emotional data as they progress through the task, allowing it to provide detailed feedback on the user's project management ability.

[0301] Prompt Sentence Examples

[0302] "What techniques are effective for improving project prioritization?"

[0303] "What are the benefits of taking a short break when you're stressed?"

[0304] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0305] Step 1: Collect data

[0306] The device monitors the user's computer operations in real time, specifically detecting operations such as sending and receiving emails, creating documents, and using project management tools. It also simultaneously collects the user's emotional data, including facial expressions, voice, and text input.

[0307] Input: User's computer operation data and emotional data.

[0308] Output: Collected operation data and sentiment data.

[0309] Step 2: Sending data

[0310] The device transmits the collected operation data and emotion data to a server at regular intervals, using data streaming technology and real-time communication protocols.

[0311] Input: Collected operation data and emotion data.

[0312] Output: The data sent to the server.

[0313] Step 3: Temporarily save data

[0314] The server receives the operation data and emotion data sent from the terminal and temporarily stores them in storage.

[0315] Input: Submitted operation data and emotion data.

[0316] Output: Temporarily stored data.

[0317] Step 4: Analyze the data

[0318] The server uses the multimodal AI "Gemini" and an emotion analysis engine to analyze the stored operation data and emotion data, specifically assessing the user's work performance (e.g., email response speed, document structure, task prioritization) and emotional state (e.g., stress level and emotional changes).

[0319] Input: Temporarily stored operation data and emotion data.

[0320] Output: Analysis results (evaluation of work performance and emotional state).

[0321] Step 5: Generate feedback

[0322] The server generates specific feedback for the user based on the analysis results, including specific advice and suggestions for improvement (e.g., "Improve the structure of the document," "Modify the subject line of the email specifically") and emotion management advice (e.g., "Take a short break").

[0323] Input: Analysis results.

[0324] Output: Specific feedback generated.

[0325] Step 6: Notification of feedback

[0326] The server notifies the user of the generated feedback in a timely manner via pop-ups, emails, in-app notifications, etc.

[0327] Input: Generated feedback.

[0328] Output: Feedback sent to the user's device.

[0329] Step 7: Review and respond to feedback

[0330] Users can review the feedback displayed on their device and adjust their work methods as needed. For example, if they receive feedback to "improve task prioritization," they can apply that advice the next time they work. They can also use advice based on emotional data to manage stress and control their emotions.

[0331] Input: Feedback sent to the user's device.

[0332] Output: User-identified and applied work improvements and emotion management.

[0333] Through this process, users receive specific and useful feedback in real time, enabling them to improve their work efficiency and emotional management.

[0334] (Application example 2)

[0335] 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."

[0336] Previous business skill improvement systems only provided evaluations and feedback based on operational data, but were unable to provide feedback that took into account the user's emotional state. Furthermore, they lacked integration with other tools and were slow to provide real-time feedback. This limited the improvement of users' work skills, making it difficult to address declines in work efficiency, especially under high-stress conditions.

[0337] 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.

[0338] In this invention, the server includes means for monitoring user operations in real time and transmitting collected operation data and emotional data to the server, means for analyzing the operation data and emotional data in the server and evaluating the user's work performance and emotional state, means for generating and notifying the user of specific feedback based on the analysis results, means for linking with different tools and collecting and analyzing data from the tools, means for transmitting data collected by the terminal to the server and notifying the terminal of feedback generated on the server side, and means for monitoring the emotional data and work performance using a display device worn by the user and displaying feedback in real time. This makes it possible to provide detailed feedback that takes the user's emotional state into consideration, thereby improving work skills and emotion management ability while improving work efficiency.

[0339] "User" refers to a person who uses the system to perform work.

[0340] "Operation data" includes information about various operations performed by a user using an electronic device.

[0341] "Emotional data" refers to information that includes information about a user's emotional state, such as facial expressions, voice, text input, etc.

[0342] A "server" is a computer system that receives and analyzes data over a network.

[0343] "Analysis" refers to the process of identifying patterns or evaluations based on collected data.

[0344] "Feedback" refers to advice, improvements, and information generated based on the analysis results.

[0345] "Tools" refers to electronic software or devices used to perform a task.

[0346] "Notification" refers to the act of providing information to a user in real time.

[0347] "Terminal" refers to an electronic device operated by a user and used to collect data.

[0348] "Display device" refers to an electronic device for presenting information to the user's visual field.

[0349] "Monitoring" refers to the act of monitoring a user's actions and emotional state in real time.

[0350] "Real-time" refers to the timing of data collection, analysis, and feedback occurring almost immediately.

[0351] This invention uses a system that uses a display device worn by store staff to evaluate the user's emotional state and work performance in real time and provide appropriate feedback. Specific implementation methods of the system are described below.

[0352] 1. System Configuration

[0353] The server receives operation data and emotion data sent in real time from the user's device. Operation data includes information related to the user's email correspondence, document creation, and project management. Emotion data includes the user's facial expressions, voice, and text input. The server temporarily stores this data in storage.

[0354] The device is responsible for monitoring the user's computer operations in real time. It detects specific operations (e.g., sending emails or creating files) and records the details of those operations, while also collecting emotional data such as the user's facial expressions, voice, and text input. This data is then sent to a server at regular intervals.

[0355] 2. Data analysis and feedback

[0356] The server analyzes the received operation data and emotional data. The analysis uses the multimodal AI "Gemini" and an emotion engine. The operation data and emotional data are combined and analyzed to evaluate the user's work performance and emotional state. For example, evaluation items include email response speed, document structure, task prioritization, emotional changes, and stress levels. Based on the analysis results, the server generates specific improvements and advice to improve the user's work skills. The system is also configured to display appropriate feedback when the user is feeling stressed.

[0357] 3. Integration with business tools

[0358] The server can be linked to business tools such as project management tools, messaging tools, and document creation tools. This allows operation data to be collected from each tool and added to the analysis. For example, by obtaining task completion status and comment contents from a project management tool, detailed feedback on the user's project management ability can be provided.

[0359] 4. Data transmission and notification

[0360] The device sends the collected operation data and emotion data to the server. Data transmission is performed periodically and synchronized in real time. This allows the server to always have the latest operation data and emotion data. The server notifies the user's device of the generated feedback, and the received feedback is displayed to the user in real time. For example, a notification saying "Change the subject of the email specifically" is displayed as a pop-up. Advice based on the user's emotional state is also displayed in the same way.

[0361] 5. User Conduct

[0362] Users use the system while performing their daily work. No special operations or settings are required. They are encouraged to check the feedback they receive, revise their work methods as needed, and apply the advice to their next task. For example, if the feedback suggests "improving the document headings," they can apply that advice the next time they create a document. They can also use the system to manage stress and control their emotions by referring to advice based on emotional data.

[0363] Specific examples

[0364] Imagine a situation where a customer may be dissatisfied during a very busy time in a store. When a staff member becomes tired, the system suggests "taking a five-minute break," helping to improve the quality of customer service. Also, if a customer looks dissatisfied while a staff member is interacting with them, the system will display specific advice such as "offering additional support to the customer."

[0365] Prompt Sentence Examples

[0366] "If you recognize a situation where a customer is dissatisfied with the service, offer solutions. Also, if staff are tired, offer effective break times."

[0367] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0368] Step 1:

[0369] Collecting user interaction and sentiment data

[0370] The device monitors and collects in real time the user's computer operations and emotional data such as facial expressions, voice, and text input. For example, operation data includes email sending and receiving and document creation history, while emotional data includes facial expression data obtained through facial recognition. The input is the user's operations and emotional state, and the output is the collected operation data and emotional data.

[0371] Step 2:

[0372] Sending data

[0373] The collected operation data and emotion data are sent to the server at regular intervals. The device synchronizes the data with the server in real time, ensuring that the server always has the latest data. In this step, the input is the collected operation data and emotion data, and the output is the data sent to the server.

[0374] Step 3:

[0375] Data analysis

[0376] The server analyzes the received operation data and emotional data. The analysis uses a multimodal AI "AI model" and an emotion engine to evaluate business performance and emotional state. In the analysis process, operation data (e.g., email response speed and document structure) and emotional data (changes in facial expressions and stress levels) are evaluated. The input is the operation data and emotional data sent to the server, and the output is the analysis results.

[0377] Step 4:

[0378] Generate feedback

[0379] Based on the analysis results, the server generates specific improvements and advice for improving the user's work skills. It also generates appropriate feedback when the user feels stressed. The input is the analysis results, and the output is the feedback content. For example, advice such as "take a five-minute break" or "change the subject line of your email to be more specific" is included.

[0380] Step 5:

[0381] Data Notification

[0382] The server notifies the device of the generated feedback. The device displays the received feedback to the user in real time. In this step, the input is the feedback content from the server, and the output is the feedback displayed to the user. For example, the feedback is displayed as a pop-up on the device screen.

[0383] Step 6:

[0384] Integration with business tools

[0385] The server connects to business tools such as project management tools, messaging tools, and document creation tools. This allows it to collect operational data from each tool and add it to the analysis. In this step, the input is data from each business tool, and the output is the integrated data sent to the server for analysis.

[0386] Step 7:

[0387] User behavior

[0388] While carrying out their normal work, users can check the feedback displayed on their device and apply improvements and advice as needed. For example, they can refer to feedback such as "improve the document headings" or "suggest additional support to customers" and reflect these in their next work. The input is the feedback displayed on the device, and the output is the user's work improvement actions.

[0389] 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.

[0390] 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 (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.

[0391] 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.

[0392] [Second embodiment]

[0393] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0394] 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.

[0395] 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).

[0396] 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.

[0397] 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.

[0398] 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).

[0399] 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.

[0400] 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.

[0401] 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.

[0402] 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.

[0403] 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.

[0404] 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."

[0405] This invention is an "AI personal mentor" system designed to improve the basic business skills of desk workers. The system monitors the user's computer operations in real time, collects operational data, and sends it to a server that analyzes the data and provides feedback. This feedback includes specific advice and suggestions for improvement to improve the user's work skills.

[0406] Server-side processing

[0407] The server receives data on user operations. The user's computer operations are collected in real time and the necessary data is sent to the server. The server then analyzes the received data. The analysis uses the artificial intelligence model "Gemini" to evaluate the user's work performance. This evaluation includes, for example, email sending and receiving speed, document creation accuracy, and project management ability.

[0408] The server then generates specific feedback based on the analysis results. This feedback includes suggestions and advice for improvement, helping users improve their work skills. For example, specific advice such as "make email subject lines more specific" may be included. The server also integrates with various work tools, collecting data from project management tools, messaging tools, document creation tools, and more, and adding this data to the analysis to provide a comprehensive skill assessment.

[0409] Terminal side processing

[0410] The user's device monitors computer operations in real time. The operation data generated by this monitoring is sent to a server at regular intervals. When the device detects a specific operation (such as sending an email or creating a document), it records that information. The collected data is sent to the server and used for analysis.

[0411] The device also displays real-time feedback from the server, which is displayed to the user as a pop-up message or notification, allowing them to take immediate action. For example, if a user is composing an email, a pop-up message will immediately tell them to be more specific in the subject line.

[0412] User operations

[0413] Users can benefit from the system by simply performing their daily tasks as usual. No special operations or settings are required. When a user logs in, the system automatically collects operational data and sends it to the server. The user receives feedback from the server, checks it, and reflects it in their next task. For example, if the feedback advises them to "improve the structure of the document," they will keep that advice in mind the next time they create a document.

[0414] For example, if a user manages tasks using a project management tool, the server collects and analyzes the task completion status and comments, allowing it to provide detailed feedback on the user's project management capabilities. For example, it may provide advice such as "improve task prioritization."

[0415] In this way, the system of the present invention can continuously improve the user's basic business skills by monitoring and analyzing the user's business data in real time and providing feedback.

[0416] The processing flow will be explained below.

[0417] Program processing flow (detailed explanation for each step)

[0418] Server-side processing

[0419] Step 1: Collect data

[0420] The server receives operational data sent in real time from the user's device. The user's operations (e.g., sending and receiving emails, creating documents, and operations in project management tools) are recorded and sent to the server. The server stores this data in temporary storage.

[0421] Step 2: Analyze the data

[0422] The server analyzes the received operation data. This is where the multimodal AI "Gemini" comes into play. The AI ​​analyzes the content of each operation and evaluates business performance. For example, evaluation criteria include email response speed, document structure, and task prioritization.

[0423] Step 3: Generate feedback

[0424] The server generates feedback based on the analysis results, including suggestions for improvement and specific advice for the user, such as "You should be more specific in your email subject lines."

[0425] Step 4: Integration with business tools

[0426] The server connects to business tools such as project management tools, messaging tools, and document creation tools. This allows it to collect operational data from each tool and add it to the analysis. For example, it obtains task completion status and comment contents from the project management tool.

[0427] Step 5: Feedback notification

[0428] The server sends the generated feedback to the user's device via pop-up, email, in-app notification, etc. The user can check the feedback in real time and apply it to their work.

[0429] Terminal side processing

[0430] Step 1: Monitoring user actions

[0431] The device monitors the user's computer operations in real time, detects specific operations (e.g., sending emails, creating files), records the operations, and sends the data to the server at regular intervals.

[0432] Step 2: Sending data

[0433] The terminal sends the collected operation data to the server. The data is sent periodically and synchronized in real time, allowing the server to always have the latest operation data.

[0434] Step 3: Receive and view feedback

[0435] The device receives feedback from the server, which is then displayed to the user in real time, such as a pop-up notification saying, "Change the subject of the email to be more specific."

[0436] User behavior

[0437] Step 1: Getting the job done

[0438] Users can carry out their normal work. No special operations or settings are required, and they can use the system while performing their daily work.

[0439] Step 2: Review and respond to feedback

[0440] The user checks the feedback displayed on the device, and if necessary, reviews their work methods and applies the advice to their next work. For example, if the feedback suggests "improving the document headings," the user can apply that advice the next time they create a document.

[0441] In this way, the system of the present invention can monitor and analyze users' business data in real time and provide specific feedback, thereby improving users' basic business skills.

[0442] Example 1

[0443] 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."

[0444] Conventional work support systems for desk workers often do not fully utilize user operation data and are unable to provide real-time feedback. As a result, there are issues with users' work skill improvement being delayed and work efficiency not improving. In addition, they lack the functionality to integrate and analyze data from multiple business applications, making comprehensive skill evaluation difficult.

[0445] 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.

[0446] In this invention, the server includes means for monitoring a user's computer operations in real time and transmitting the collected operation data to an information processing device, means for analyzing the operation data in the information processing device and evaluating the user's work performance, and means for generating and notifying specific feedback to the user based on the analysis results. This makes it possible to provide immediate feedback using the user's operation data. In addition, by integrating data from multiple business applications and analyzing it using an artificial intelligence model, it becomes possible to provide comprehensive skill evaluations and specific improvements and advice.

[0447] "User" refers to an individual or organization that uses the System to carry out business.

[0448] "Computer operation" refers to a series of operations or actions that a user performs on a computer.

[0449] "Real-time" refers to near-simultaneous processing and feedback, with minimal delay.

[0450] "Operational data" refers to information about operations performed by a user on a computer, including, for example, data on sending and receiving emails, document creation, and project management.

[0451] "Information processing device" refers to a server or computer system for analyzing collected data and providing feedback to the user.

[0452] "Analysis" refers to the process of evaluating collected operational data to determine user performance.

[0453] "Feedback" refers to specific advice and improvements provided to users based on the analysis results.

[0454] "Business Applications" refers to software or tools that users use to perform their jobs, such as project management tools or messaging tools.

[0455] "Bundling" refers to aggregating multiple pieces of operation data over a fixed period of time and putting them together in a format that can be transmitted.

[0456] "Database" refers to an information management system for storing received operational data.

[0457] "Artificial Intelligence Model" refers to a machine learning or natural language processing algorithm used to analyze operational data and evaluate business performance.

[0458] "Areas for improvement" refers to specific areas that users should pay attention to in order to improve their work performance.

[0459] "Notification" refers to messages sent to users to convey analysis results and feedback.

[0460] This invention is a "work performance support system" for improving users' work skills. This system monitors users' computer operations in real time, transmits the collected operation data to a server, and provides specific feedback to users based on the analysis results.

[0461] Server-side processing

[0462] The server receives and stores operation data sent by the user. Specifically, the server, which functions as an information processing device, stores the operation data in a database. Next, this data is analyzed using an artificial intelligence model (for example, a machine learning algorithm or a natural language processing algorithm). The AI ​​model used is a generative AI model such as "Gemini." This analysis quantifies and evaluates the user's work performance. The server then generates specific feedback based on the analysis results and sends it to the user's device. This feedback includes advice and areas for improvement to improve work skills.

[0463] Terminal side processing

[0464] The user's terminal is installed with software that monitors computer operations in real time. This software detects user operation events (e.g., sending and receiving emails, document creation, project management, etc.) and records the operation data. The recorded data is bundled at regular intervals and sent to the server via an encrypted communication channel. In addition, the terminal receives feedback from the server in real time and displays it to the user as a pop-up message or notification. For example, while the user is typing the subject of an email, the feedback "Please make the subject of the email more specific" is displayed.

[0465] User operations

[0466] Users can simply carry out their work as usual without any special operations or settings. When they log in to the system, operation data is automatically collected and sent to the server. Users can check the feedback sent from the server and reflect it in their next work. For example, when creating a document, they can receive feedback such as "improve the document structure."

[0467] Specific examples

[0468] As a concrete example, consider the case where a user manages tasks using a project management tool. The server collects and analyzes the task completion status and comments, and provides detailed feedback on the user's project management skills. For example, advice such as "improve task prioritization" may be presented as feedback. In this way, by monitoring and analyzing the user's business data in real time and providing immediate feedback, the user's basic business skills are continuously improved.

[0469] Prompt Sentence Examples

[0470] "Please display the following feedback appropriately: The user is creating a document. The rating is 'The document is difficult to understand.'"

[0471] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0472] Step 1:

[0473] Start monitoring user computer operations

[0474] The user's terminal runs software for monitoring computer operations. This software detects and records the user's operation events (e.g., sending emails, creating documents, etc.) in real time. The input is the user's operation event, and the output is the recorded operation data. For example, when a user creates a new email, the subject, body, and recipient information of that email are recorded.

[0475] Step 2:

[0476] Bundling and sending operational data

[0477] The device bundles the operation data collected at regular intervals and aggregates it into a format that can be sent. This data is sent to the server via an encrypted communication channel. The input is the collected operation data, and the output is the bundled data. Specifically, all operations performed by the user over the course of one hour are compiled into a data set.

[0478] Step 3:

[0479] Receiving and storing data

[0480] The server receives bundled data sent from the terminal. The received data is stored in a database and later used for analysis. The input is the bundled data sent from the terminal, and the output is the data stored in the database. Specifically, the server stores the email transmission data it receives in a specified database.

[0481] Step 4:

[0482] Start of analysis using "generative AI models"

[0483] The server inputs the operation data stored in the database into the "generative AI model" and begins analysis. This model quantifies and evaluates the user's work performance. The input is the operation data in the database, and the output is the analysis results. Specifically, the generative AI model outputs evaluation values ​​for email sending speed and document accuracy.

[0484] Step 5:

[0485] Generate feedback based on analysis results

[0486] The server generates specific feedback based on the analysis results of the generative AI model. This feedback includes specific advice and areas for improvement to improve work skills. The input is the analysis results, and the output is the generated feedback. Specifically, this includes feedback such as "Make the subject line of the email more specific."

[0487] Step 6:

[0488] Sending feedback to users

[0489] The server sends the generated feedback to the user's device. The sent feedback is encrypted and notified to the user. The input is the generated feedback, and the output is the feedback sent to the user's device. As a specific operation, while the user is composing an email, a notification pops up urging the user to "make the subject more specific."

[0490] Step 7:

[0491] Reviewing user feedback and incorporating it into work

[0492] The user checks the feedback displayed on the device and reflects it in their next work. The input is the feedback displayed on the device, and the output is improved work performance. Specifically, the next time the user composes an email, they will be more specific in the subject line.

[0493] (Application example 1)

[0494] 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."

[0495] Improving the operational efficiency and work performance of robots in factories is a critical issue for workers. Conventional systems have struggled to monitor robot operation data in real time, analyze that data, and provide specific feedback. This has made it difficult for factory operators and engineers to continuously improve their robot operation skills and prevent errors before they occur. Therefore, there is a need for a system that can collect and analyze robot operation data in real time and provide specific feedback.

[0496] 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.

[0497] In this invention, the server includes: a means for monitoring user operations in real time and transmitting collected operation data to the server; a means for analyzing the operation data and evaluating the user's performance in the server; a means for generating and notifying specific feedback to the user based on the analysis results; a means for linking with different tools and collecting and analyzing data from the tools; a means for collecting robot operation data and periodically transmitting it to the server; and a means for displaying feedback from the server in real time on a terminal. This allows operators to receive specific improvements and advice on their robot operation in real time. This is expected to improve the work performance of factory operators and engineers and enable efficient robot operation.

[0498] "User" means a person or entity that utilizes the system to provide operational data and receive feedback therefrom.

[0499] "Operation data" is information about a series of actions performed by a robot or tool and the results of those actions.

[0500] A "server" is a central processing unit for receiving operational data, performing analysis, and generating feedback.

[0501] "Performance" refers to the efficiency and accuracy of a task performed by a user or robot.

[0502] "Real time" refers to a time frame in which events are processed as they occur.

[0503] "Tools" refers to the software and hardware used by users and robots to perform operations.

[0504] "Feedback" refers to specific advice and improvements generated based on the analysis results.

[0505] "Notifications" are a means of communicating feedback and other information to users.

[0506] The term "robot" refers to an automated mechanical device operated by an operator in a factory or the like.

[0507] "Data collection" is the process of collecting data about the robot and user operations.

[0508] "Analysis" is the process of evaluating collected data and measuring performance.

[0509] "Evaluation" is the act of quantitatively judging the performance of a user or robot based on the analysis results.

[0510] "Device" means the electronic device (e.g., tablet, smart glasses) used by the user to receive feedback.

[0511] This invention is a system designed to improve the operational efficiency and business performance of factory robots. This system includes a series of processes that monitors robot operation data in real time, analyzes the data, and generates feedback. An embodiment of this system will be described in detail below.

[0512] Hardware and Software Configuration

[0513] Server-side configuration

[0514] The server consists of the following hardware and software:

[0515] Hardware: High-performance central processing units (e.g., AWS EC2 instances, GCP Compute Engine)

[0516] Software: Artificial intelligence model for data analysis (generative AI model) (e.g., implemented in TensorFlow or PyTorch), analysis script (e.g., Python), database (e.g., PostgreSQL)

[0517] The server receives and analyzes the collected operation data. The analysis uses data such as the robot's movement data, error occurrence status, and task completion time. This data is processed by a "generative AI model" to evaluate the robot's operation performance. Appropriate feedback is generated from the analysis results and notified to the user's device.

[0518] Terminal configuration

[0519] A user's device consists of the following hardware and software:

[0520] Hardware: Tablets, smart glasses, or other electronic devices

[0521] Software: Application for feedback notification (e.g. iOS / Android application)

[0522] The user's device receives feedback from the server in real time and displays it to the user as a pop-up message or notification, allowing the user to quickly check and respond to the feedback.

[0523] Process example

[0524] For example, consider a situation where a robot is assembling parts in a factory. In this case, the following process is executed:

[0525] 1. Data collection: Sensors and cameras installed on the robot collect operation data and error occurrence status in real time and periodically send it to a server.

[0526] 2. Data analysis: The received data is analyzed using a "generative AI model" to evaluate the robot's operational performance.

[0527] 3. Feedback generation: Based on the analysis results, specific feedback is generated to the user (e.g., "The installation of part A is unstable. Please adjust the motor torque setting").

[0528] 4. Notification: The feedback is notified to the user's device and displayed as a pop-up message.

[0529] Prompt Sentence Examples

[0530] Below are some examples of prompts used in this system:

[0531] Data collected: Robot operation data, error occurrence status, operation time

[0532] AI analysis result: "Part A installation operation is unstable"

[0533] Feedback generation: "Advise to adjust motor torque settings"

[0534] In this way, it becomes possible to provide appropriate feedback to users based on the operation data of factory robots, enabling efficient operation.

[0535] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0536] Step 1:

[0537] The terminal collects operational data (e.g., movement data, error occurrence status, task completion time) through sensors and cameras installed on the robot. This data is measured in real time.

[0538] Input: Robot operation data, error occurrence status, task completion time

[0539] Data processing: Collecting raw data from sensors and cameras

[0540] Output: Structured operation data

[0541] Step 2:

[0542] The terminal transmits the collected operation data to the server at regular intervals (e.g., every minute).

[0543] Input: Collected operational data

[0544] Data processing: Convert to JSON format etc. and send over the network

[0545] Output: Operation data received on the server side

[0546] Step 3:

[0547] The server then inputs the received operational data into a "generative AI model" for analysis, where the data is pre-processed and converted into a format suitable for the model.

[0548] Input: Operation data sent to the server

[0549] Data calculations: performance analysis using generative AI models (e.g., operation accuracy, error rate)

[0550] Output: Analysis results

[0551] Step 4:

[0552] The server generates specific feedback based on the analysis results from the generative AI model, such as advice on areas where errors frequently occur during robot operation and areas for improvement.

[0553] Input: Analysis results

[0554] Data arithmetic: Advice generation based on feedback generation algorithms

[0555] Output: Specific feedback (e.g., advice and improvements)

[0556] Step 5:

[0557] The server sends the generated feedback to the user's device in real time, either through pop-up messages or real-time notifications.

[0558] Input: Specific feedback

[0559] Data processing: generating notification formats (e.g., popup messages)

[0560] Output: Feedback displayed on the terminal

[0561] Step 6:

[0562] The device visualizes the received feedback to the user, allowing them to see improvements to their operation in real time.

[0563] Input: Feedback from the server

[0564] Data processing: Display feedback on the screen

[0565] Output: A feedback message for the user to see.

[0566] Step 7:

[0567] Users can adjust the robot's operation based on feedback received through the device, allowing them to refine their operation in real time.

[0568] Input: Feedback message

[0569] Data processing: Adjusting the robot's operation method

[0570] Output: Improved operation results

[0571] 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.

[0572] This invention is an "AI personal mentor" system designed to improve the basic business skills of desk workers, and is equipped with the ability to recognize and analyze the user's emotions. The system monitors the user's computer operations in real time, collecting operational and emotional data and sending it to a server, which then analyzes the data and provides feedback. This feedback includes specific advice and suggestions for improvement to improve the user's work skills.

[0573] Server-side processing

[0574] Data collection

[0575] The server receives operation data and emotion data sent in real time from the user's device. The user's operation details (e.g., sending and receiving emails, creating documents, operating project management tools) and emotion recognition data (e.g., facial expressions, voice, text input, etc.) are recorded and sent to the server. This data is temporarily stored in storage.

[0576] Data analysis

[0577] The server analyzes the received operation data and emotional data. This is where the multimodal AI "Gemini" and emotion engine come into play. The operation data and emotional data are combined and analyzed to evaluate the user's work performance and emotional state. For example, evaluation criteria include email response speed, document structure, task prioritization, emotional changes, and stress levels.

[0578] Generate feedback

[0579] The server generates feedback based on the analysis results. This feedback includes suggestions for improvement and specific advice to help users improve their work skills. It also includes a function to provide timely feedback based on emotional data. For example, if a user is feeling stressed, the server generates advice such as "take a break to relax."

[0580] Integration with business tools

[0581] The server connects to business tools such as project management tools, messaging tools, and document creation tools. This allows it to collect operational data from each tool and add it to the analysis. For example, it obtains task completion status and comment contents from the project management tool.

[0582] Feedback Notification

[0583] The server sends the generated feedback to the user's device via pop-up, email, in-app notification, etc. The user can check the feedback in real time and apply it to their work.

[0584] Terminal side processing

[0585] User Action Monitoring

[0586] The device monitors the user's computer operations in real time. It detects specific operations (e.g., sending emails or creating files) and records the details of those operations. At the same time, the device also collects emotional data such as the user's facial expressions, voice, and text input. This data is sent to a server at regular intervals.

[0587] Sending data

[0588] The device sends the collected operation data and emotion data to the server. Data transmission is performed periodically and synchronized in real time, allowing the server to always have the latest operation data and emotion data.

[0589] Receiving and viewing feedback

[0590] The device receives feedback from the server, which is then displayed to the user in real time. For example, a notification pops up telling the user to specifically change the subject of the email. The device also displays advice based on the user's emotional state.

[0591] User behavior

[0592] Business execution

[0593] Users can carry out their normal work. No special operations or settings are required, and they can use the system while performing their daily work.

[0594] Review and respond to feedback

[0595] The user checks the feedback displayed on the device, and if necessary, reviews their work methods and applies the advice to their next task. For example, if the feedback advises them to "improve the document headings," they can apply that advice the next time they create a document. Furthermore, by referring to advice based on emotional data, they can manage stress and control their emotions.

[0596] For example, if a user is using a project management tool to manage tasks, the server collects and analyzes the task completion status and comments. It also collects emotional data as the user progresses through the task and incorporates this data into the analysis. This allows the server to provide detailed feedback on the user's project management capabilities. For example, the server may provide advice such as "improve task prioritization" or "take short breaks when under stress."

[0597] In this way, the system of the present invention monitors and analyzes the user's business data and emotional data in real time and provides comprehensive feedback, thereby enabling the user to continuously improve their basic business skills and emotional management skills.

[0598] The processing flow will be explained below.

[0599] Server-side processing

[0600] Step 1: Collect data

[0601] The server receives operational data and emotional data sent from the user's device in real time. Specifically, the server records the user's operations (e.g., sending and receiving emails, creating documents, and using project management tools) and emotional recognition data (e.g., facial expressions, voice, text input, etc.) and sends them to the server.

[0602] Step 2: Save your data

[0603] The server stores the received operation data and emotion data in temporary storage, which is used in subsequent analysis steps.

[0604] Step 3: Analyze the data

[0605] The server analyzes the operation data and emotional data stored in the storage. This analysis is performed using the multimodal AI "Gemini" and an emotion engine. This evaluates the user's work performance and emotional state. For example, evaluation items include email response speed, document creation accuracy, task prioritization, and the user's stress level.

[0606] Step 4: Generate feedback

[0607] The server generates feedback based on the analysis results. This feedback includes specific advice and areas for improvement to help users improve their work skills. For example, advice such as "You should be more specific in the subject line of your emails" is generated. Feedback is also provided based on emotional data, and if a user is feeling stressed, advice such as "Take a break to relax" is provided.

[0608] Step 5: Integration with business tools

[0609] The server connects to business tools such as project management tools, messaging tools, and document creation tools. This allows it to collect operational data from each tool and add it to the analysis. For example, it obtains task completion status and comment contents from the project management tool.

[0610] Step 6: Notification of feedback

[0611] The server sends the generated feedback to the user's device via pop-up, email, in-app notification, etc. The user can check the feedback in real time and apply it to their work.

[0612] Terminal side processing

[0613] Step 1: Monitoring user actions

[0614] The device monitors the user's computer operations in real time, detecting specific operations (e.g., sending emails or creating files) and recording the details of those operations, as well as collecting emotional data such as the user's facial expressions, voice, and text input.

[0615] Step 2: Sending data

[0616] The device sends the collected operation data and emotion data to the server. Data transmission is performed periodically and synchronized in real time, allowing the server to always have the latest operation data and emotion data.

[0617] Step 3: Receive and view feedback

[0618] The device receives feedback from the server, which is then displayed to the user in real time. For example, a notification pops up telling the user to specifically change the subject of the email. The device also displays advice based on the user's emotional state.

[0619] User behavior

[0620] Step 1: Getting the job done

[0621] Users can carry out their normal work. No special operations or settings are required, and they can use the system while performing their daily work.

[0622] Step 2: Review and respond to feedback

[0623] Users can check the feedback displayed on their device, revise their work methods as needed, and apply the advice to their next task. For example, if they are notified to "improve the headings in a document," they can apply that advice the next time they create a document. Furthermore, they can also refer to advice based on emotional data to manage stress and control their emotions.

[0624] For example, if a user is using a project management tool to manage tasks, the server collects and analyzes the task completion status and comments. It also collects emotional data as the user progresses through the task and incorporates this data into the analysis. This allows the server to provide detailed feedback on the user's project management capabilities. For example, the server may provide advice such as "improve task prioritization" or "take short breaks when under stress."

[0625] Example 2

[0626] 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."

[0627] Conventional business support systems only analyzed user operation data and were unable to provide advice or suggestions for improvement that took into account emotional data such as the user's emotional state and stress level. This limited the ability to improve users' work performance and made it difficult to optimize overall work efficiency and emotional management. Furthermore, they lacked the ability to manage and analyze operation data from multiple business tools in an integrated manner, making it difficult to obtain comprehensive feedback.

[0628] 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.

[0629] In this invention, the server includes means for monitoring a user's computer operations in real time and transmitting collected operation data and emotional data to the server, means for analyzing the operation data and emotional data in the server and evaluating the user's work performance and emotional state, means for generating specific feedback to the user based on the analysis results and notifying them in a timely manner, and means for linking different work tools and collecting data from each work tool to add to the analysis. This makes it possible to simultaneously improve a user's work performance and optimize their emotional management, and provide comprehensive feedback from various angles.

[0630] "User" means an individual who uses the System to perform business operations.

[0631] "Computer operation" refers to a series of operations or actions that a user performs on a computer.

[0632] "Real-time" refers to processing and communication occurring immediately, without delay.

[0633] "Operational Data" refers to specific data about operations performed by a user on a computer.

[0634] "Emotion data" refers to data that indicates the user's emotional state, and includes facial expression changes, voice, text input, and the like.

[0635] "Server" refers to a central processing unit for analyzing collected operation data and emotion data and providing feedback.

[0636] "Analysis" refers to the process of examining collected data in detail and deriving its meaning and value.

[0637] "Business performance" refers to the efficiency and effectiveness with which users carry out their work.

[0638] "Evaluation" refers to judging a user's work performance and emotional state based on analyzed data.

[0639] "Feedback" refers to advice and improvements provided to users based on the analysis results.

[0640] "Timely notification" means that feedback is communicated to the user at an appropriate time.

[0641] "Business tools" refers to various software used to carry out business, such as project management tools, messaging tools, and document creation tools.

[0642] "Integration" refers to sharing data between different business tools and making it mutually usable.

[0643] "Collecting data and adding it to the analysis" refers to obtaining operational data from each business tool and incorporating it into the analysis process.

[0644] This invention is an "AI personal mentor" system designed to improve the basic business skills of desk workers, and is equipped with the ability to recognize and analyze the user's emotions. The system monitors the user's computer operations in real time, collecting operational and emotional data and sending it to a server, which then analyzes the data and provides feedback. This feedback includes specific advice and suggestions for improvement to improve the user's work skills.

[0645] Hardware and software used

[0646] The system uses the following hardware and software:

[0647] User device (PC, laptop, etc.)

[0648] Server (with high performance processing capabilities)

[0649] Multimodal AI "Gemini"

[0650] Sentiment Analysis Engine

[0651] Project management tools, messaging tools, document creation tools, etc.

[0652] Data collection and transmission

[0653] The device collects various operations performed by the user (for example, sending and receiving emails, creating documents, and using project management tools) as well as emotion recognition data such as facial expressions, voice, and text input in real time. This data is then sent to a server at regular intervals. Specific technologies used for this include data streaming technology and real-time communication protocols.

[0654] Data analysis

[0655] The server uses the multimodal AI "Gemini" and an emotion analysis engine to analyze the collected operation data and emotional data. This analysis evaluates the user's work performance (e.g., email response speed, document structure, task prioritization) and emotional state (e.g., stress level and emotional changes). Combining the operation data and emotional data allows for more detailed feedback.

[0656] Feedback generation and notification

[0657] The server generates specific feedback for the user based on the analysis results. This feedback includes suggestions and advice for improving the user's work skills (e.g., "improve the structure of the document" or "change the subject line of the email to be more specific"). It also provides timely feedback based on the user's emotional data. For example, it recommends "taking a short break" when the user is under high stress.

[0658] Integration with business tools

[0659] The server works with project management tools, messaging tools, document creation tools, etc., collecting operational data from each tool and adding it to the analysis, making it possible to provide feedback that integrates data from all the business tools used by the user.

[0660] Specific examples

[0661] When a user manages a task using a project management tool, the server collects and analyzes the task completion status and comments. It also collects and considers the user's emotional data as they progress through the task, allowing it to provide detailed feedback on the user's project management ability.

[0662] Prompt Sentence Examples

[0663] "What techniques are effective for improving project prioritization?"

[0664] "What are the benefits of taking a short break when you're stressed?"

[0665] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0666] Step 1: Collect data

[0667] The device monitors the user's computer operations in real time, specifically detecting operations such as sending and receiving emails, creating documents, and using project management tools. It also simultaneously collects the user's emotional data, including facial expressions, voice, and text input.

[0668] Input: User's computer operation data and emotional data.

[0669] Output: Collected operation data and sentiment data.

[0670] Step 2: Sending data

[0671] The device transmits the collected operation data and emotion data to a server at regular intervals, using data streaming technology and real-time communication protocols.

[0672] Input: Collected operation data and emotion data.

[0673] Output: The data sent to the server.

[0674] Step 3: Temporarily save data

[0675] The server receives the operation data and emotion data sent from the terminal and temporarily stores them in storage.

[0676] Input: Submitted operation data and emotion data.

[0677] Output: Temporarily stored data.

[0678] Step 4: Analyze the data

[0679] The server uses the multimodal AI "Gemini" and an emotion analysis engine to analyze the stored operation data and emotion data, specifically assessing the user's work performance (e.g., email response speed, document structure, task prioritization) and emotional state (e.g., stress level and emotional changes).

[0680] Input: Temporarily stored operation data and emotion data.

[0681] Output: Analysis results (evaluation of work performance and emotional state).

[0682] Step 5: Generate feedback

[0683] The server generates specific feedback for the user based on the analysis results, including specific advice and suggestions for improvement (e.g., "Improve the structure of the document," "Modify the subject line of the email specifically") and emotion management advice (e.g., "Take a short break").

[0684] Input: Analysis results.

[0685] Output: Specific feedback generated.

[0686] Step 6: Notification of feedback

[0687] The server notifies the user of the generated feedback in a timely manner via pop-ups, emails, in-app notifications, etc.

[0688] Input: Generated feedback.

[0689] Output: Feedback sent to the user's device.

[0690] Step 7: Review and respond to feedback

[0691] Users can review the feedback displayed on their device and adjust their work methods as needed. For example, if they receive feedback to "improve task prioritization," they can apply that advice the next time they work. They can also use advice based on emotional data to manage stress and control their emotions.

[0692] Input: Feedback sent to the user's device.

[0693] Output: User-identified and applied work improvements and emotion management.

[0694] Through this process, users receive specific and useful feedback in real time, enabling them to improve their work efficiency and emotional management.

[0695] (Application example 2)

[0696] 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."

[0697] Previous business skill improvement systems only provided evaluations and feedback based on operational data, but were unable to provide feedback that took into account the user's emotional state. Furthermore, they lacked integration with other tools and were slow to provide real-time feedback. This limited the improvement of users' work skills, making it difficult to address declines in work efficiency, especially under high-stress conditions.

[0698] 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.

[0699] In this invention, the server includes means for monitoring user operations in real time and transmitting collected operation data and emotional data to the server, means for analyzing the operation data and emotional data in the server and evaluating the user's work performance and emotional state, means for generating and notifying the user of specific feedback based on the analysis results, means for linking with different tools and collecting and analyzing data from the tools, means for transmitting data collected by the terminal to the server and notifying the terminal of feedback generated on the server side, and means for monitoring the emotional data and work performance using a display device worn by the user and displaying feedback in real time. This makes it possible to provide detailed feedback that takes the user's emotional state into consideration, thereby improving work skills and emotion management ability while improving work efficiency.

[0700] "User" refers to a person who uses the system to perform work.

[0701] "Operation data" includes information about various operations performed by a user using an electronic device.

[0702] "Emotional data" refers to information that includes information about a user's emotional state, such as facial expressions, voice, text input, etc.

[0703] A "server" is a computer system that receives and analyzes data over a network.

[0704] "Analysis" refers to the process of identifying patterns or evaluations based on collected data.

[0705] "Feedback" refers to advice, improvements, and information generated based on the analysis results.

[0706] "Tools" refers to electronic software or devices used to perform a task.

[0707] "Notification" refers to the act of providing information to a user in real time.

[0708] "Terminal" refers to an electronic device operated by a user and used to collect data.

[0709] "Display device" refers to an electronic device for presenting information to the user's visual field.

[0710] "Monitoring" refers to the act of monitoring a user's actions and emotional state in real time.

[0711] "Real-time" refers to the timing of data collection, analysis, and feedback occurring almost immediately.

[0712] This invention uses a system that uses a display device worn by store staff to evaluate the user's emotional state and work performance in real time and provide appropriate feedback. Specific implementation methods of the system are described below.

[0713] 1. System Configuration

[0714] The server receives operation data and emotion data sent in real time from the user's device. Operation data includes information related to the user's email correspondence, document creation, and project management. Emotion data includes the user's facial expressions, voice, and text input. The server temporarily stores this data in storage.

[0715] The device is responsible for monitoring the user's computer operations in real time. It detects specific operations (e.g., sending emails or creating files) and records the details of those operations, while also collecting emotional data such as the user's facial expressions, voice, and text input. This data is then sent to a server at regular intervals.

[0716] 2. Data analysis and feedback

[0717] The server analyzes the received operation data and emotional data. The analysis uses the multimodal AI "Gemini" and an emotion engine. The operation data and emotional data are combined and analyzed to evaluate the user's work performance and emotional state. For example, evaluation items include email response speed, document structure, task prioritization, emotional changes, and stress levels. Based on the analysis results, the server generates specific improvements and advice to improve the user's work skills. The system is also configured to display appropriate feedback when the user is feeling stressed.

[0718] 3. Integration with business tools

[0719] The server can be linked to business tools such as project management tools, messaging tools, and document creation tools. This allows operation data to be collected from each tool and added to the analysis. For example, by obtaining task completion status and comment contents from a project management tool, detailed feedback on the user's project management ability can be provided.

[0720] 4. Data transmission and notification

[0721] The device sends the collected operation data and emotion data to the server. Data transmission is performed periodically and synchronized in real time. This allows the server to always have the latest operation data and emotion data. The server notifies the user's device of the generated feedback, and the received feedback is displayed to the user in real time. For example, a notification saying "Change the subject of the email specifically" is displayed as a pop-up. Advice based on the user's emotional state is also displayed in the same way.

[0722] 5. User Conduct

[0723] Users use the system while performing their daily work. No special operations or settings are required. They are encouraged to check the feedback they receive, revise their work methods as needed, and apply the advice to their next task. For example, if the feedback suggests "improving the document headings," they can apply that advice the next time they create a document. They can also use the system to manage stress and control their emotions by referring to advice based on emotional data.

[0724] Specific examples

[0725] Imagine a situation where a customer may be dissatisfied during a very busy time in a store. When a staff member becomes tired, the system suggests "taking a five-minute break," helping to improve the quality of customer service. Also, if a customer looks dissatisfied while a staff member is interacting with them, the system will display specific advice such as "offering additional support to the customer."

[0726] Prompt Sentence Examples

[0727] "If you recognize a situation where a customer is dissatisfied with the service, offer solutions. Also, if staff are tired, offer effective break times."

[0728] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0729] Step 1:

[0730] Collecting user interaction and sentiment data

[0731] The device monitors and collects in real time the user's computer operations and emotional data such as facial expressions, voice, and text input. For example, operation data includes email sending and receiving and document creation history, while emotional data includes facial expression data obtained through facial recognition. The input is the user's operations and emotional state, and the output is the collected operation data and emotional data.

[0732] Step 2:

[0733] Sending data

[0734] The collected operation data and emotion data are sent to the server at regular intervals. The device synchronizes the data with the server in real time, ensuring that the server always has the latest data. In this step, the input is the collected operation data and emotion data, and the output is the data sent to the server.

[0735] Step 3:

[0736] Data analysis

[0737] The server analyzes the received operation data and emotional data. The analysis uses a multimodal AI "AI model" and an emotion engine to evaluate business performance and emotional state. In the analysis process, operation data (e.g., email response speed and document structure) and emotional data (changes in facial expressions and stress levels) are evaluated. The input is the operation data and emotional data sent to the server, and the output is the analysis results.

[0738] Step 4:

[0739] Generate feedback

[0740] Based on the analysis results, the server generates specific improvements and advice for improving the user's work skills. It also generates appropriate feedback when the user feels stressed. The input is the analysis results, and the output is the feedback content. For example, advice such as "take a five-minute break" or "change the subject line of your email to be more specific" is included.

[0741] Step 5:

[0742] Data Notification

[0743] The server notifies the device of the generated feedback. The device displays the received feedback to the user in real time. In this step, the input is the feedback content from the server, and the output is the feedback displayed to the user. For example, the feedback is displayed as a pop-up on the device screen.

[0744] Step 6:

[0745] Integration with business tools

[0746] The server connects to business tools such as project management tools, messaging tools, and document creation tools. This allows it to collect operational data from each tool and add it to the analysis. In this step, the input is data from each business tool, and the output is the integrated data sent to the server for analysis.

[0747] Step 7:

[0748] User behavior

[0749] While carrying out their normal work, users can check the feedback displayed on their device and apply improvements and advice as needed. For example, they can refer to feedback such as "improve the document headings" or "suggest additional support to customers" and reflect these in their next work. The input is the feedback displayed on the device, and the output is the user's work improvement actions.

[0750] 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.

[0751] 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.

[0752] 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.

[0753] [Third embodiment]

[0754] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0755] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0756] 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).

[0757] 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.

[0758] 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.

[0759] 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).

[0760] 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.

[0761] 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.

[0762] 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.

[0763] 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.

[0764] 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.

[0765] 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."

[0766] This invention is an "AI personal mentor" system designed to improve the basic business skills of desk workers. The system monitors the user's computer operations in real time, collects operational data, and sends it to a server that analyzes the data and provides feedback. This feedback includes specific advice and suggestions for improvement to improve the user's work skills.

[0767] Server-side processing

[0768] The server receives data on user operations. The user's computer operations are collected in real time and the necessary data is sent to the server. The server then analyzes the received data. The analysis uses the artificial intelligence model "Gemini" to evaluate the user's work performance. This evaluation includes, for example, email sending and receiving speed, document creation accuracy, and project management ability.

[0769] The server then generates specific feedback based on the analysis results. This feedback includes suggestions and advice for improvement, helping users improve their work skills. For example, specific advice such as "make email subject lines more specific" may be included. The server also integrates with various work tools, collecting data from project management tools, messaging tools, document creation tools, and more, and adding this data to the analysis to provide a comprehensive skill assessment.

[0770] Terminal side processing

[0771] The user's device monitors computer operations in real time. The operation data generated by this monitoring is sent to a server at regular intervals. When the device detects a specific operation (such as sending an email or creating a document), it records that information. The collected data is sent to the server and used for analysis.

[0772] The device also displays real-time feedback from the server, which is displayed to the user as a pop-up message or notification, allowing them to take immediate action. For example, if a user is composing an email, a pop-up message will immediately tell them to be more specific in the subject line.

[0773] User operations

[0774] Users can benefit from the system by simply performing their daily tasks as usual. No special operations or settings are required. When a user logs in, the system automatically collects operational data and sends it to the server. The user receives feedback from the server, checks it, and reflects it in their next task. For example, if the feedback advises them to "improve the structure of the document," they will keep that advice in mind the next time they create a document.

[0775] For example, if a user manages tasks using a project management tool, the server collects and analyzes the task completion status and comments, allowing it to provide detailed feedback on the user's project management capabilities. For example, it may provide advice such as "improve task prioritization."

[0776] In this way, the system of the present invention can continuously improve the user's basic business skills by monitoring and analyzing the user's business data in real time and providing feedback.

[0777] The processing flow will be explained below.

[0778] Program processing flow (detailed explanation for each step)

[0779] Server-side processing

[0780] Step 1: Collect data

[0781] The server receives operational data sent in real time from the user's device. The user's operations (e.g., sending and receiving emails, creating documents, and operations in project management tools) are recorded and sent to the server. The server stores this data in temporary storage.

[0782] Step 2: Analyze the data

[0783] The server analyzes the received operation data. This is where the multimodal AI "Gemini" comes into play. The AI ​​analyzes the content of each operation and evaluates business performance. For example, evaluation criteria include email response speed, document structure, and task prioritization.

[0784] Step 3: Generate feedback

[0785] The server generates feedback based on the analysis results, including suggestions for improvement and specific advice for the user, such as "You should be more specific in your email subject lines."

[0786] Step 4: Integration with business tools

[0787] The server connects to business tools such as project management tools, messaging tools, and document creation tools. This allows it to collect operational data from each tool and add it to the analysis. For example, it obtains task completion status and comment contents from the project management tool.

[0788] Step 5: Feedback notification

[0789] The server sends the generated feedback to the user's device via pop-up, email, in-app notification, etc. The user can check the feedback in real time and apply it to their work.

[0790] Terminal side processing

[0791] Step 1: Monitoring user actions

[0792] The device monitors the user's computer operations in real time, detects specific operations (e.g., sending emails, creating files), records the operations, and sends the data to the server at regular intervals.

[0793] Step 2: Sending data

[0794] The terminal sends the collected operation data to the server. The data is sent periodically and synchronized in real time, allowing the server to always have the latest operation data.

[0795] Step 3: Receive and view feedback

[0796] The device receives feedback from the server, which is then displayed to the user in real time, such as a pop-up notification saying, "Change the subject of the email to be more specific."

[0797] User behavior

[0798] Step 1: Getting the job done

[0799] Users can carry out their normal work. No special operations or settings are required, and they can use the system while performing their daily work.

[0800] Step 2: Review and respond to feedback

[0801] The user checks the feedback displayed on the device, and if necessary, reviews their work methods and applies the advice to their next work. For example, if the feedback suggests "improving the document headings," the user can apply that advice the next time they create a document.

[0802] In this way, the system of the present invention can monitor and analyze users' business data in real time and provide specific feedback, thereby improving users' basic business skills.

[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] Conventional work support systems for desk workers often do not fully utilize user operation data and are unable to provide real-time feedback. As a result, there are issues with users' work skill improvement being delayed and work efficiency not improving. In addition, they lack the functionality to integrate and analyze data from multiple business applications, making comprehensive skill evaluation difficult.

[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 monitoring a user's computer operations in real time and transmitting the collected operation data to an information processing device, means for analyzing the operation data in the information processing device and evaluating the user's work performance, and means for generating and notifying specific feedback to the user based on the analysis results. This makes it possible to provide immediate feedback using the user's operation data. In addition, by integrating data from multiple business applications and analyzing it using an artificial intelligence model, it becomes possible to provide comprehensive skill evaluations and specific improvements and advice.

[0808] "User" refers to an individual or organization that uses the System to carry out business.

[0809] "Computer operation" refers to a series of operations or actions that a user performs on a computer.

[0810] "Real-time" refers to near-simultaneous processing and feedback, with minimal delay.

[0811] "Operational data" refers to information about operations performed by a user on a computer, including, for example, data on sending and receiving emails, document creation, and project management.

[0812] "Information processing device" refers to a server or computer system for analyzing collected data and providing feedback to the user.

[0813] "Analysis" refers to the process of evaluating collected operational data to determine user performance.

[0814] "Feedback" refers to specific advice and improvements provided to users based on the analysis results.

[0815] "Business Applications" refers to software or tools that users use to perform their jobs, such as project management tools or messaging tools.

[0816] "Bundling" refers to aggregating multiple pieces of operation data over a fixed period of time and putting them together in a format that can be transmitted.

[0817] "Database" refers to an information management system for storing received operational data.

[0818] "Artificial Intelligence Model" refers to a machine learning or natural language processing algorithm used to analyze operational data and evaluate business performance.

[0819] "Areas for improvement" refers to specific areas that users should pay attention to in order to improve their work performance.

[0820] "Notification" refers to messages sent to users to convey analysis results and feedback.

[0821] This invention is a "work performance support system" for improving users' work skills. This system monitors users' computer operations in real time, transmits the collected operation data to a server, and provides specific feedback to users based on the analysis results.

[0822] Server-side processing

[0823] The server receives and stores operation data sent by the user. Specifically, the server, which functions as an information processing device, stores the operation data in a database. Next, this data is analyzed using an artificial intelligence model (for example, a machine learning algorithm or a natural language processing algorithm). The AI ​​model used is a generative AI model such as "Gemini." This analysis quantifies and evaluates the user's work performance. The server then generates specific feedback based on the analysis results and sends it to the user's device. This feedback includes advice and areas for improvement to improve work skills.

[0824] Terminal side processing

[0825] The user's terminal is installed with software that monitors computer operations in real time. This software detects user operation events (e.g., sending and receiving emails, document creation, project management, etc.) and records the operation data. The recorded data is bundled at regular intervals and sent to the server via an encrypted communication channel. In addition, the terminal receives feedback from the server in real time and displays it to the user as a pop-up message or notification. For example, while the user is typing the subject of an email, the feedback "Please make the subject of the email more specific" is displayed.

[0826] User operations

[0827] Users can simply carry out their work as usual without any special operations or settings. When they log in to the system, operation data is automatically collected and sent to the server. Users can check the feedback sent from the server and reflect it in their next work. For example, when creating a document, they can receive feedback such as "improve the document structure."

[0828] Specific examples

[0829] As a concrete example, consider the case where a user manages tasks using a project management tool. The server collects and analyzes the task completion status and comments, and provides detailed feedback on the user's project management skills. For example, advice such as "improve task prioritization" may be presented as feedback. In this way, by monitoring and analyzing the user's business data in real time and providing immediate feedback, the user's basic business skills are continuously improved.

[0830] Prompt Sentence Examples

[0831] "Please display the following feedback appropriately: The user is creating a document. The rating is 'The document is difficult to understand.'"

[0832] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0833] Step 1:

[0834] Start monitoring user computer operations

[0835] The user's terminal runs software for monitoring computer operations. This software detects and records the user's operation events (e.g., sending emails, creating documents, etc.) in real time. The input is the user's operation event, and the output is the recorded operation data. For example, when a user creates a new email, the subject, body, and recipient information of that email are recorded.

[0836] Step 2:

[0837] Bundling and sending operational data

[0838] The device bundles the operation data collected at regular intervals and aggregates it into a format that can be sent. This data is sent to the server via an encrypted communication channel. The input is the collected operation data, and the output is the bundled data. Specifically, all operations performed by the user over the course of one hour are compiled into a data set.

[0839] Step 3:

[0840] Receiving and storing data

[0841] The server receives bundled data sent from the terminal. The received data is stored in a database and later used for analysis. The input is the bundled data sent from the terminal, and the output is the data stored in the database. Specifically, the server stores the email transmission data it receives in a specified database.

[0842] Step 4:

[0843] Start of analysis using "generative AI models"

[0844] The server inputs the operation data stored in the database into the "generative AI model" and begins analysis. This model quantifies and evaluates the user's work performance. The input is the operation data in the database, and the output is the analysis results. Specifically, the generative AI model outputs evaluation values ​​for email sending speed and document accuracy.

[0845] Step 5:

[0846] Generate feedback based on analysis results

[0847] The server generates specific feedback based on the analysis results of the generative AI model. This feedback includes specific advice and areas for improvement to improve work skills. The input is the analysis results, and the output is the generated feedback. Specifically, this includes feedback such as "Make the subject line of the email more specific."

[0848] Step 6:

[0849] Sending feedback to users

[0850] The server sends the generated feedback to the user's device. The sent feedback is encrypted and notified to the user. The input is the generated feedback, and the output is the feedback sent to the user's device. As a specific operation, while the user is composing an email, a notification pops up urging the user to "make the subject more specific."

[0851] Step 7:

[0852] Reviewing user feedback and incorporating it into work

[0853] The user checks the feedback displayed on the device and reflects it in their next work. The input is the feedback displayed on the device, and the output is improved work performance. Specifically, the next time the user composes an email, they will be more specific in the subject line.

[0854] (Application example 1)

[0855] 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."

[0856] Improving the operational efficiency and work performance of robots in factories is a critical issue for workers. Conventional systems have struggled to monitor robot operation data in real time, analyze that data, and provide specific feedback. This has made it difficult for factory operators and engineers to continuously improve their robot operation skills and prevent errors before they occur. Therefore, there is a need for a system that can collect and analyze robot operation data in real time and provide specific feedback.

[0857] 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.

[0858] In this invention, the server includes: a means for monitoring user operations in real time and transmitting collected operation data to the server; a means for analyzing the operation data and evaluating the user's performance in the server; a means for generating and notifying specific feedback to the user based on the analysis results; a means for linking with different tools and collecting and analyzing data from the tools; a means for collecting robot operation data and periodically transmitting it to the server; and a means for displaying feedback from the server in real time on a terminal. This allows operators to receive specific improvements and advice on their robot operation in real time. This is expected to improve the work performance of factory operators and engineers and enable efficient robot operation.

[0859] "User" means a person or entity that utilizes the system to provide operational data and receive feedback therefrom.

[0860] "Operation data" is information about a series of actions performed by a robot or tool and the results of those actions.

[0861] A "server" is a central processing unit for receiving operational data, performing analysis, and generating feedback.

[0862] "Performance" refers to the efficiency and accuracy of a task performed by a user or robot.

[0863] "Real time" refers to a time frame in which events are processed as they occur.

[0864] "Tools" refers to the software and hardware used by users and robots to perform operations.

[0865] "Feedback" refers to specific advice and improvements generated based on the analysis results.

[0866] "Notifications" are a means of communicating feedback and other information to users.

[0867] The term "robot" refers to an automated mechanical device operated by an operator in a factory or the like.

[0868] "Data collection" is the process of collecting data about the robot and user operations.

[0869] "Analysis" is the process of evaluating collected data and measuring performance.

[0870] "Evaluation" is the act of quantitatively judging the performance of a user or robot based on the analysis results.

[0871] "Device" means the electronic device (e.g., tablet, smart glasses) used by the user to receive feedback.

[0872] This invention is a system designed to improve the operational efficiency and business performance of factory robots. This system includes a series of processes that monitors robot operation data in real time, analyzes the data, and generates feedback. An embodiment of this system will be described in detail below.

[0873] Hardware and Software Configuration

[0874] Server-side configuration

[0875] The server consists of the following hardware and software:

[0876] Hardware: High-performance central processing units (e.g., AWS EC2 instances, GCP Compute Engine)

[0877] Software: Artificial intelligence model for data analysis (generative AI model) (e.g., implemented in TensorFlow or PyTorch), analysis script (e.g., Python), database (e.g., PostgreSQL)

[0878] The server receives and analyzes the collected operation data. The analysis uses data such as the robot's movement data, error occurrence status, and task completion time. This data is processed by a "generative AI model" to evaluate the robot's operation performance. Appropriate feedback is generated from the analysis results and notified to the user's device.

[0879] Terminal configuration

[0880] A user's device consists of the following hardware and software:

[0881] Hardware: Tablets, smart glasses, or other electronic devices

[0882] Software: Application for feedback notification (e.g. iOS / Android application)

[0883] The user's device receives feedback from the server in real time and displays it to the user as a pop-up message or notification, allowing the user to quickly check and respond to the feedback.

[0884] Process example

[0885] For example, consider a situation where a robot is assembling parts in a factory. In this case, the following process is executed:

[0886] 1. Data collection: Sensors and cameras installed on the robot collect operation data and error occurrence status in real time and periodically send it to a server.

[0887] 2. Data analysis: The received data is analyzed using a "generative AI model" to evaluate the robot's operational performance.

[0888] 3. Feedback generation: Based on the analysis results, specific feedback is generated to the user (e.g., "The installation of part A is unstable. Please adjust the motor torque setting").

[0889] 4. Notification: The feedback is notified to the user's device and displayed as a pop-up message.

[0890] Prompt Sentence Examples

[0891] Below are some examples of prompts used in this system:

[0892] Data collected: Robot operation data, error occurrence status, operation time

[0893] AI analysis result: "Part A installation operation is unstable"

[0894] Feedback generation: "Advise to adjust motor torque settings"

[0895] In this way, it becomes possible to provide appropriate feedback to users based on the operation data of factory robots, enabling efficient operation.

[0896] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0897] Step 1:

[0898] The terminal collects operational data (e.g., movement data, error occurrence status, task completion time) through sensors and cameras installed on the robot. This data is measured in real time.

[0899] Input: Robot operation data, error occurrence status, task completion time

[0900] Data processing: Collecting raw data from sensors and cameras

[0901] Output: Structured operation data

[0902] Step 2:

[0903] The terminal transmits the collected operation data to the server at regular intervals (e.g., every minute).

[0904] Input: Collected operational data

[0905] Data processing: Convert to JSON format etc. and send over the network

[0906] Output: Operation data received on the server side

[0907] Step 3:

[0908] The server then inputs the received operational data into a "generative AI model" for analysis, where the data is pre-processed and converted into a format suitable for the model.

[0909] Input: Operation data sent to the server

[0910] Data calculations: performance analysis using generative AI models (e.g., operation accuracy, error rate)

[0911] Output: Analysis results

[0912] Step 4:

[0913] The server generates specific feedback based on the analysis results from the generative AI model, such as advice on areas where errors frequently occur during robot operation and areas for improvement.

[0914] Input: Analysis results

[0915] Data arithmetic: Advice generation based on feedback generation algorithms

[0916] Output: Specific feedback (e.g., advice and improvements)

[0917] Step 5:

[0918] The server sends the generated feedback to the user's device in real time, either through pop-up messages or real-time notifications.

[0919] Input: Specific feedback

[0920] Data processing: generating notification formats (e.g., popup messages)

[0921] Output: Feedback displayed on the terminal

[0922] Step 6:

[0923] The device visualizes the received feedback to the user, allowing them to see improvements to their operation in real time.

[0924] Input: Feedback from the server

[0925] Data processing: Display feedback on the screen

[0926] Output: A feedback message for the user to see.

[0927] Step 7:

[0928] Users can adjust the robot's operation based on feedback received through the device, allowing them to refine their operation in real time.

[0929] Input: Feedback message

[0930] Data processing: Adjusting the robot's operation method

[0931] Output: Improved operation results

[0932] 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.

[0933] This invention is an "AI personal mentor" system designed to improve the basic business skills of desk workers, and is equipped with the ability to recognize and analyze the user's emotions. The system monitors the user's computer operations in real time, collecting operational and emotional data and sending it to a server, which then analyzes the data and provides feedback. This feedback includes specific advice and suggestions for improvement to improve the user's work skills.

[0934] Server-side processing

[0935] Data collection

[0936] The server receives operation data and emotion data sent in real time from the user's device. The user's operation details (e.g., sending and receiving emails, creating documents, operating project management tools) and emotion recognition data (e.g., facial expressions, voice, text input, etc.) are recorded and sent to the server. This data is temporarily stored in storage.

[0937] Data analysis

[0938] The server analyzes the received operation data and emotional data. This is where the multimodal AI "Gemini" and emotion engine come into play. The operation data and emotional data are combined and analyzed to evaluate the user's work performance and emotional state. For example, evaluation criteria include email response speed, document structure, task prioritization, emotional changes, and stress levels.

[0939] Generate feedback

[0940] The server generates feedback based on the analysis results. This feedback includes suggestions for improvement and specific advice to help users improve their work skills. It also includes a function to provide timely feedback based on emotional data. For example, if a user is feeling stressed, the server generates advice such as "take a break to relax."

[0941] Integration with business tools

[0942] The server connects to business tools such as project management tools, messaging tools, and document creation tools. This allows it to collect operational data from each tool and add it to the analysis. For example, it obtains task completion status and comment contents from the project management tool.

[0943] Feedback Notification

[0944] The server sends the generated feedback to the user's device via pop-up, email, in-app notification, etc. The user can check the feedback in real time and apply it to their work.

[0945] Terminal side processing

[0946] User Action Monitoring

[0947] The device monitors the user's computer operations in real time. It detects specific operations (e.g., sending emails or creating files) and records the details of those operations. At the same time, the device also collects emotional data such as the user's facial expressions, voice, and text input. This data is sent to a server at regular intervals.

[0948] Sending data

[0949] The device sends the collected operation data and emotion data to the server. Data transmission is performed periodically and synchronized in real time, allowing the server to always have the latest operation data and emotion data.

[0950] Receiving and viewing feedback

[0951] The device receives feedback from the server, which is then displayed to the user in real time. For example, a notification pops up telling the user to specifically change the subject of the email. The device also displays advice based on the user's emotional state.

[0952] User behavior

[0953] Business execution

[0954] Users can carry out their normal work. No special operations or settings are required, and they can use the system while performing their daily work.

[0955] Review and respond to feedback

[0956] The user checks the feedback displayed on the device, and if necessary, reviews their work methods and applies the advice to their next task. For example, if the feedback advises them to "improve the document headings," they can apply that advice the next time they create a document. Furthermore, by referring to advice based on emotional data, they can manage stress and control their emotions.

[0957] For example, if a user is using a project management tool to manage tasks, the server collects and analyzes the task completion status and comments. It also collects emotional data as the user progresses through the task and incorporates this data into the analysis. This allows the server to provide detailed feedback on the user's project management capabilities. For example, the server may provide advice such as "improve task prioritization" or "take short breaks when under stress."

[0958] In this way, the system of the present invention monitors and analyzes the user's business data and emotional data in real time and provides comprehensive feedback, thereby enabling the user to continuously improve their basic business skills and emotional management skills.

[0959] The processing flow will be explained below.

[0960] Server-side processing

[0961] Step 1: Collect data

[0962] The server receives operational data and emotional data sent from the user's device in real time. Specifically, the server records the user's operations (e.g., sending and receiving emails, creating documents, and using project management tools) and emotional recognition data (e.g., facial expressions, voice, text input, etc.) and sends them to the server.

[0963] Step 2: Save your data

[0964] The server stores the received operation data and emotion data in temporary storage, which is used in subsequent analysis steps.

[0965] Step 3: Analyze the data

[0966] The server analyzes the operation data and emotional data stored in the storage. This analysis is performed using the multimodal AI "Gemini" and an emotion engine. This evaluates the user's work performance and emotional state. For example, evaluation items include email response speed, document creation accuracy, task prioritization, and the user's stress level.

[0967] Step 4: Generate feedback

[0968] The server generates feedback based on the analysis results. This feedback includes specific advice and areas for improvement to help users improve their work skills. For example, advice such as "You should be more specific in the subject line of your emails" is generated. Feedback is also provided based on emotional data, and if a user is feeling stressed, advice such as "Take a break to relax" is provided.

[0969] Step 5: Integration with business tools

[0970] The server connects to business tools such as project management tools, messaging tools, and document creation tools. This allows it to collect operational data from each tool and add it to the analysis. For example, it obtains task completion status and comment contents from the project management tool.

[0971] Step 6: Notification of feedback

[0972] The server sends the generated feedback to the user's device via pop-up, email, in-app notification, etc. The user can check the feedback in real time and apply it to their work.

[0973] Terminal side processing

[0974] Step 1: Monitoring user actions

[0975] The device monitors the user's computer operations in real time, detecting specific operations (e.g., sending emails or creating files) and recording the details of those operations, as well as collecting emotional data such as the user's facial expressions, voice, and text input.

[0976] Step 2: Sending data

[0977] The device sends the collected operation data and emotion data to the server. Data transmission is performed periodically and synchronized in real time, allowing the server to always have the latest operation data and emotion data.

[0978] Step 3: Receive and view feedback

[0979] The device receives feedback from the server, which is then displayed to the user in real time. For example, a notification pops up telling the user to specifically change the subject of the email. The device also displays advice based on the user's emotional state.

[0980] User behavior

[0981] Step 1: Getting the job done

[0982] Users can carry out their normal work. No special operations or settings are required, and they can use the system while performing their daily work.

[0983] Step 2: Review and respond to feedback

[0984] Users can check the feedback displayed on their device, revise their work methods as needed, and apply the advice to their next task. For example, if they are notified to "improve the headings in a document," they can apply that advice the next time they create a document. Furthermore, they can also refer to advice based on emotional data to manage stress and control their emotions.

[0985] For example, if a user is using a project management tool to manage tasks, the server collects and analyzes the task completion status and comments. It also collects emotional data as the user progresses through the task and incorporates this data into the analysis. This allows the server to provide detailed feedback on the user's project management capabilities. For example, the server may provide advice such as "improve task prioritization" or "take short breaks when under stress."

[0986] Example 2

[0987] 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."

[0988] Conventional business support systems only analyzed user operation data and were unable to provide advice or suggestions for improvement that took into account emotional data such as the user's emotional state and stress level. This limited the ability to improve users' work performance and made it difficult to optimize overall work efficiency and emotional management. Furthermore, they lacked the ability to manage and analyze operation data from multiple business tools in an integrated manner, making it difficult to obtain comprehensive feedback.

[0989] 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.

[0990] In this invention, the server includes means for monitoring a user's computer operations in real time and transmitting collected operation data and emotional data to the server, means for analyzing the operation data and emotional data in the server and evaluating the user's work performance and emotional state, means for generating specific feedback to the user based on the analysis results and notifying them in a timely manner, and means for linking different work tools and collecting data from each work tool to add to the analysis. This makes it possible to simultaneously improve a user's work performance and optimize their emotional management, and provide comprehensive feedback from various angles.

[0991] "User" means an individual who uses the System to perform business operations.

[0992] "Computer operation" refers to a series of operations or actions that a user performs on a computer.

[0993] "Real-time" refers to processing and communication occurring immediately, without delay.

[0994] "Operational Data" refers to specific data about operations performed by a user on a computer.

[0995] "Emotion data" refers to data that indicates the user's emotional state, and includes facial expression changes, voice, text input, and the like.

[0996] "Server" refers to a central processing unit for analyzing collected operation data and emotion data and providing feedback.

[0997] "Analysis" refers to the process of examining collected data in detail and deriving its meaning and value.

[0998] "Business performance" refers to the efficiency and effectiveness with which users carry out their work.

[0999] "Evaluation" refers to judging a user's work performance and emotional state based on analyzed data.

[1000] "Feedback" refers to advice and improvements provided to users based on the analysis results.

[1001] "Timely notification" means that feedback is communicated to the user at an appropriate time.

[1002] "Business tools" refers to various software used to carry out business, such as project management tools, messaging tools, and document creation tools.

[1003] "Integration" refers to sharing data between different business tools and making it mutually usable.

[1004] "Collecting data and adding it to the analysis" refers to obtaining operational data from each business tool and incorporating it into the analysis process.

[1005] This invention is an "AI personal mentor" system designed to improve the basic business skills of desk workers, and is equipped with the ability to recognize and analyze the user's emotions. The system monitors the user's computer operations in real time, collecting operational and emotional data and sending it to a server, which then analyzes the data and provides feedback. This feedback includes specific advice and suggestions for improvement to improve the user's work skills.

[1006] Hardware and software used

[1007] The system uses the following hardware and software:

[1008] User device (PC, laptop, etc.)

[1009] Server (with high performance processing capabilities)

[1010] Multimodal AI "Gemini"

[1011] Sentiment Analysis Engine

[1012] Project management tools, messaging tools, document creation tools, etc.

[1013] Data collection and transmission

[1014] The device collects various operations performed by the user (for example, sending and receiving emails, creating documents, and using project management tools) as well as emotion recognition data such as facial expressions, voice, and text input in real time. This data is then sent to a server at regular intervals. Specific technologies used for this include data streaming technology and real-time communication protocols.

[1015] Data analysis

[1016] The server uses the multimodal AI "Gemini" and an emotion analysis engine to analyze the collected operation data and emotional data. This analysis evaluates the user's work performance (e.g., email response speed, document structure, task prioritization) and emotional state (e.g., stress level and emotional changes). Combining the operation data and emotional data allows for more detailed feedback.

[1017] Feedback generation and notification

[1018] The server generates specific feedback for the user based on the analysis results. This feedback includes suggestions and advice for improving the user's work skills (e.g., "improve the structure of the document" or "change the subject line of the email to be more specific"). It also provides timely feedback based on the user's emotional data. For example, it recommends "taking a short break" when the user is under high stress.

[1019] Integration with business tools

[1020] The server works with project management tools, messaging tools, document creation tools, etc., collecting operational data from each tool and adding it to the analysis, making it possible to provide feedback that integrates data from all the business tools used by the user.

[1021] Specific examples

[1022] When a user manages a task using a project management tool, the server collects and analyzes the task completion status and comments. It also collects and considers the user's emotional data as they progress through the task, allowing it to provide detailed feedback on the user's project management ability.

[1023] Prompt Sentence Examples

[1024] "What techniques are effective for improving project prioritization?"

[1025] "What are the benefits of taking a short break when you're stressed?"

[1026] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1027] Step 1: Collect data

[1028] The device monitors the user's computer operations in real time, specifically detecting operations such as sending and receiving emails, creating documents, and using project management tools. It also simultaneously collects the user's emotional data, including facial expressions, voice, and text input.

[1029] Input: User's computer operation data and emotional data.

[1030] Output: Collected operation data and sentiment data.

[1031] Step 2: Sending data

[1032] The device transmits the collected operation data and emotion data to a server at regular intervals, using data streaming technology and real-time communication protocols.

[1033] Input: Collected operation data and emotion data.

[1034] Output: The data sent to the server.

[1035] Step 3: Temporarily save data

[1036] The server receives the operation data and emotion data sent from the terminal and temporarily stores them in storage.

[1037] Input: Submitted operation data and emotion data.

[1038] Output: Temporarily stored data.

[1039] Step 4: Analyze the data

[1040] The server uses the multimodal AI "Gemini" and an emotion analysis engine to analyze the stored operation data and emotion data, specifically assessing the user's work performance (e.g., email response speed, document structure, task prioritization) and emotional state (e.g., stress level and emotional changes).

[1041] Input: Temporarily stored operation data and emotion data.

[1042] Output: Analysis results (evaluation of work performance and emotional state).

[1043] Step 5: Generate feedback

[1044] The server generates specific feedback for the user based on the analysis results, including specific advice and suggestions for improvement (e.g., "Improve the structure of the document," "Modify the subject line of the email specifically") and emotion management advice (e.g., "Take a short break").

[1045] Input: Analysis results.

[1046] Output: Specific feedback generated.

[1047] Step 6: Notification of feedback

[1048] The server notifies the user of the generated feedback in a timely manner via pop-ups, emails, in-app notifications, etc.

[1049] Input: Generated feedback.

[1050] Output: Feedback sent to the user's device.

[1051] Step 7: Review and respond to feedback

[1052] Users can review the feedback displayed on their device and adjust their work methods as needed. For example, if they receive feedback to "improve task prioritization," they can apply that advice the next time they work. They can also use advice based on emotional data to manage stress and control their emotions.

[1053] Input: Feedback sent to the user's device.

[1054] Output: User-identified and applied work improvements and emotion management.

[1055] Through this process, users receive specific and useful feedback in real time, enabling them to improve their work efficiency and emotional management.

[1056] (Application example 2)

[1057] 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."

[1058] Previous business skill improvement systems only provided evaluations and feedback based on operational data, but were unable to provide feedback that took into account the user's emotional state. Furthermore, they lacked integration with other tools and were slow to provide real-time feedback. This limited the improvement of users' work skills, making it difficult to address declines in work efficiency, especially under high-stress conditions.

[1059] 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.

[1060] In this invention, the server includes means for monitoring user operations in real time and transmitting collected operation data and emotional data to the server, means for analyzing the operation data and emotional data in the server and evaluating the user's work performance and emotional state, means for generating and notifying the user of specific feedback based on the analysis results, means for linking with different tools and collecting and analyzing data from the tools, means for transmitting data collected by the terminal to the server and notifying the terminal of feedback generated on the server side, and means for monitoring the emotional data and work performance using a display device worn by the user and displaying feedback in real time. This makes it possible to provide detailed feedback that takes the user's emotional state into consideration, thereby improving work skills and emotion management ability while improving work efficiency.

[1061] "User" refers to a person who uses the system to perform work.

[1062] "Operation data" includes information about various operations performed by a user using an electronic device.

[1063] "Emotional data" refers to information that includes information about a user's emotional state, such as facial expressions, voice, text input, etc.

[1064] A "server" is a computer system that receives and analyzes data over a network.

[1065] "Analysis" refers to the process of identifying patterns or evaluations based on collected data.

[1066] "Feedback" refers to advice, improvements, and information generated based on the analysis results.

[1067] "Tools" refers to electronic software or devices used to perform a task.

[1068] "Notification" refers to the act of providing information to a user in real time.

[1069] "Terminal" refers to an electronic device operated by a user and used to collect data.

[1070] "Display device" refers to an electronic device for presenting information to the user's visual field.

[1071] "Monitoring" refers to the act of monitoring a user's actions and emotional state in real time.

[1072] "Real-time" refers to the timing of data collection, analysis, and feedback occurring almost immediately.

[1073] This invention uses a system that uses a display device worn by store staff to evaluate the user's emotional state and work performance in real time and provide appropriate feedback. Specific implementation methods of the system are described below.

[1074] 1. System Configuration

[1075] The server receives operation data and emotion data sent in real time from the user's device. Operation data includes information related to the user's email correspondence, document creation, and project management. Emotion data includes the user's facial expressions, voice, and text input. The server temporarily stores this data in storage.

[1076] The device is responsible for monitoring the user's computer operations in real time. It detects specific operations (e.g., sending emails or creating files) and records the details of those operations, while also collecting emotional data such as the user's facial expressions, voice, and text input. This data is then sent to a server at regular intervals.

[1077] 2. Data analysis and feedback

[1078] The server analyzes the received operation data and emotional data. The analysis uses the multimodal AI "Gemini" and an emotion engine. The operation data and emotional data are combined and analyzed to evaluate the user's work performance and emotional state. For example, evaluation items include email response speed, document structure, task prioritization, emotional changes, and stress levels. Based on the analysis results, the server generates specific improvements and advice to improve the user's work skills. The system is also configured to display appropriate feedback when the user is feeling stressed.

[1079] 3. Integration with business tools

[1080] The server can be linked to business tools such as project management tools, messaging tools, and document creation tools. This allows operation data to be collected from each tool and added to the analysis. For example, by obtaining task completion status and comment contents from a project management tool, detailed feedback on the user's project management ability can be provided.

[1081] 4. Data transmission and notification

[1082] The device sends the collected operation data and emotion data to the server. Data transmission is performed periodically and synchronized in real time. This allows the server to always have the latest operation data and emotion data. The server notifies the user's device of the generated feedback, and the received feedback is displayed to the user in real time. For example, a notification saying "Change the subject of the email specifically" is displayed as a pop-up. Advice based on the user's emotional state is also displayed in the same way.

[1083] 5. User Conduct

[1084] Users use the system while performing their daily work. No special operations or settings are required. They are encouraged to check the feedback they receive, revise their work methods as needed, and apply the advice to their next task. For example, if the feedback suggests "improving the document headings," they can apply that advice the next time they create a document. They can also use the system to manage stress and control their emotions by referring to advice based on emotional data.

[1085] Specific examples

[1086] Imagine a situation where a customer may be dissatisfied during a very busy time in a store. When a staff member becomes tired, the system suggests "taking a five-minute break," helping to improve the quality of customer service. Also, if a customer looks dissatisfied while a staff member is interacting with them, the system will display specific advice such as "offering additional support to the customer."

[1087] Prompt Sentence Examples

[1088] "If you recognize a situation where a customer is dissatisfied with the service, offer solutions. Also, if staff are tired, offer effective break times."

[1089] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1090] Step 1:

[1091] Collecting user interaction and sentiment data

[1092] The device monitors and collects in real time the user's computer operations and emotional data such as facial expressions, voice, and text input. For example, operation data includes email sending and receiving and document creation history, while emotional data includes facial expression data obtained through facial recognition. The input is the user's operations and emotional state, and the output is the collected operation data and emotional data.

[1093] Step 2:

[1094] Sending data

[1095] The collected operation data and emotion data are sent to the server at regular intervals. The device synchronizes the data with the server in real time, ensuring that the server always has the latest data. In this step, the input is the collected operation data and emotion data, and the output is the data sent to the server.

[1096] Step 3:

[1097] Data analysis

[1098] The server analyzes the received operation data and emotional data. The analysis uses a multimodal AI "AI model" and an emotion engine to evaluate business performance and emotional state. In the analysis process, operation data (e.g., email response speed and document structure) and emotional data (changes in facial expressions and stress levels) are evaluated. The input is the operation data and emotional data sent to the server, and the output is the analysis results.

[1099] Step 4:

[1100] Generate feedback

[1101] Based on the analysis results, the server generates specific improvements and advice for improving the user's work skills. It also generates appropriate feedback when the user feels stressed. The input is the analysis results, and the output is the feedback content. For example, advice such as "take a five-minute break" or "change the subject line of your email to be more specific" is included.

[1102] Step 5:

[1103] Data Notification

[1104] The server notifies the device of the generated feedback. The device displays the received feedback to the user in real time. In this step, the input is the feedback content from the server, and the output is the feedback displayed to the user. For example, the feedback is displayed as a pop-up on the device screen.

[1105] Step 6:

[1106] Integration with business tools

[1107] The server connects to business tools such as project management tools, messaging tools, and document creation tools. This allows it to collect operational data from each tool and add it to the analysis. In this step, the input is data from each business tool, and the output is the integrated data sent to the server for analysis.

[1108] Step 7:

[1109] User behavior

[1110] While carrying out their normal work, users can check the feedback displayed on their device and apply improvements and advice as needed. For example, they can refer to feedback such as "improve the document headings" or "suggest additional support to customers" and reflect these in their next work. The input is the feedback displayed on the device, and the output is the user's work improvement actions.

[1111] 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.

[1112] 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.

[1113] 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.

[1114] [Fourth embodiment]

[1115] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1116] 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.

[1117] 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).

[1118] 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.

[1119] 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.

[1120] 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).

[1121] 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.

[1122] 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.

[1123] 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.

[1124] 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.

[1125] 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.

[1126] 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.

[1127] 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."

[1128] This invention is an "AI personal mentor" system designed to improve the basic business skills of desk workers. The system monitors the user's computer operations in real time, collects operational data, and sends it to a server that analyzes the data and provides feedback. This feedback includes specific advice and suggestions for improvement to improve the user's work skills.

[1129] Server-side processing

[1130] The server receives data on user operations. The user's computer operations are collected in real time and the necessary data is sent to the server. The server then analyzes the received data. The analysis uses the artificial intelligence model "Gemini" to evaluate the user's work performance. This evaluation includes, for example, email sending and receiving speed, document creation accuracy, and project management ability.

[1131] The server then generates specific feedback based on the analysis results. This feedback includes suggestions and advice for improvement, helping users improve their work skills. For example, specific advice such as "make email subject lines more specific" may be included. The server also integrates with various work tools, collecting data from project management tools, messaging tools, document creation tools, and more, and adding this data to the analysis to provide a comprehensive skill assessment.

[1132] Terminal side processing

[1133] The user's device monitors computer operations in real time. The operation data generated by this monitoring is sent to a server at regular intervals. When the device detects a specific operation (such as sending an email or creating a document), it records that information. The collected data is sent to the server and used for analysis.

[1134] The device also displays real-time feedback from the server, which is displayed to the user as a pop-up message or notification, allowing them to take immediate action. For example, if a user is composing an email, a pop-up message will immediately tell them to be more specific in the subject line.

[1135] User operations

[1136] Users can benefit from the system by simply performing their daily tasks as usual. No special operations or settings are required. When a user logs in, the system automatically collects operational data and sends it to the server. The user receives feedback from the server, checks it, and reflects it in their next task. For example, if the feedback advises them to "improve the structure of the document," they will keep that advice in mind the next time they create a document.

[1137] For example, if a user manages tasks using a project management tool, the server collects and analyzes the task completion status and comments, allowing it to provide detailed feedback on the user's project management capabilities. For example, it may provide advice such as "improve task prioritization."

[1138] In this way, the system of the present invention can continuously improve the user's basic business skills by monitoring and analyzing the user's business data in real time and providing feedback.

[1139] The processing flow will be explained below.

[1140] Program processing flow (detailed explanation for each step)

[1141] Server-side processing

[1142] Step 1: Collect data

[1143] The server receives operational data sent in real time from the user's device. The user's operations (e.g., sending and receiving emails, creating documents, and operations in project management tools) are recorded and sent to the server. The server stores this data in temporary storage.

[1144] Step 2: Analyze the data

[1145] The server analyzes the received operation data. This is where the multimodal AI "Gemini" comes into play. The AI ​​analyzes the content of each operation and evaluates business performance. For example, evaluation criteria include email response speed, document structure, and task prioritization.

[1146] Step 3: Generate feedback

[1147] The server generates feedback based on the analysis results, including suggestions for improvement and specific advice for the user, such as "You should be more specific in your email subject lines."

[1148] Step 4: Integration with business tools

[1149] The server connects to business tools such as project management tools, messaging tools, and document creation tools. This allows it to collect operational data from each tool and add it to the analysis. For example, it obtains task completion status and comment contents from the project management tool.

[1150] Step 5: Feedback notification

[1151] The server sends the generated feedback to the user's device via pop-up, email, in-app notification, etc. The user can check the feedback in real time and apply it to their work.

[1152] Terminal side processing

[1153] Step 1: Monitoring user actions

[1154] The device monitors the user's computer operations in real time, detects specific operations (e.g., sending emails, creating files), records the operations, and sends the data to the server at regular intervals.

[1155] Step 2: Sending data

[1156] The terminal sends the collected operation data to the server. The data is sent periodically and synchronized in real time, allowing the server to always have the latest operation data.

[1157] Step 3: Receive and view feedback

[1158] The device receives feedback from the server, which is then displayed to the user in real time, such as a pop-up notification saying, "Change the subject of the email to be more specific."

[1159] User behavior

[1160] Step 1: Getting the job done

[1161] Users can carry out their normal work. No special operations or settings are required, and they can use the system while performing their daily work.

[1162] Step 2: Review and respond to feedback

[1163] The user checks the feedback displayed on the device, and if necessary, reviews their work methods and applies the advice to their next work. For example, if the feedback suggests "improving the document headings," the user can apply that advice the next time they create a document.

[1164] In this way, the system of the present invention can monitor and analyze users' business data in real time and provide specific feedback, thereby improving users' basic business skills.

[1165] Example 1

[1166] 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."

[1167] Conventional work support systems for desk workers often do not fully utilize user operation data and are unable to provide real-time feedback. As a result, there are issues with users' work skill improvement being delayed and work efficiency not improving. In addition, they lack the functionality to integrate and analyze data from multiple business applications, making comprehensive skill evaluation difficult.

[1168] 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.

[1169] In this invention, the server includes means for monitoring a user's computer operations in real time and transmitting the collected operation data to an information processing device, means for analyzing the operation data in the information processing device and evaluating the user's work performance, and means for generating and notifying specific feedback to the user based on the analysis results. This makes it possible to provide immediate feedback using the user's operation data. In addition, by integrating data from multiple business applications and analyzing it using an artificial intelligence model, it becomes possible to provide comprehensive skill evaluations and specific improvements and advice.

[1170] "User" refers to an individual or organization that uses the System to carry out business.

[1171] "Computer operation" refers to a series of operations or actions that a user performs on a computer.

[1172] "Real-time" refers to near-simultaneous processing and feedback, with minimal delay.

[1173] "Operational data" refers to information about operations performed by a user on a computer, including, for example, data on sending and receiving emails, document creation, and project management.

[1174] "Information processing device" refers to a server or computer system for analyzing collected data and providing feedback to the user.

[1175] "Analysis" refers to the process of evaluating collected operational data to determine user performance.

[1176] "Feedback" refers to specific advice and improvements provided to users based on the analysis results.

[1177] "Business Applications" refers to software or tools that users use to perform their jobs, such as project management tools or messaging tools.

[1178] "Bundling" refers to aggregating multiple pieces of operation data over a fixed period of time and putting them together in a format that can be transmitted.

[1179] "Database" refers to an information management system for storing received operational data.

[1180] "Artificial Intelligence Model" refers to a machine learning or natural language processing algorithm used to analyze operational data and evaluate business performance.

[1181] "Areas for improvement" refers to specific areas that users should pay attention to in order to improve their work performance.

[1182] "Notification" refers to messages sent to users to convey analysis results and feedback.

[1183] This invention is a "work performance support system" for improving users' work skills. This system monitors users' computer operations in real time, transmits the collected operation data to a server, and provides specific feedback to users based on the analysis results.

[1184] Server-side processing

[1185] The server receives and stores operation data sent by the user. Specifically, the server, which functions as an information processing device, stores the operation data in a database. Next, this data is analyzed using an artificial intelligence model (for example, a machine learning algorithm or a natural language processing algorithm). The AI ​​model used is a generative AI model such as "Gemini." This analysis quantifies and evaluates the user's work performance. The server then generates specific feedback based on the analysis results and sends it to the user's device. This feedback includes advice and areas for improvement to improve work skills.

[1186] Terminal side processing

[1187] The user's terminal is installed with software that monitors computer operations in real time. This software detects user operation events (e.g., sending and receiving emails, document creation, project management, etc.) and records the operation data. The recorded data is bundled at regular intervals and sent to the server via an encrypted communication channel. In addition, the terminal receives feedback from the server in real time and displays it to the user as a pop-up message or notification. For example, while the user is typing the subject of an email, the feedback "Please make the subject of the email more specific" is displayed.

[1188] User operations

[1189] Users can simply carry out their work as usual without any special operations or settings. When they log in to the system, operation data is automatically collected and sent to the server. Users can check the feedback sent from the server and reflect it in their next work. For example, when creating a document, they can receive feedback such as "improve the document structure."

[1190] Specific examples

[1191] As a concrete example, consider the case where a user manages tasks using a project management tool. The server collects and analyzes the task completion status and comments, and provides detailed feedback on the user's project management skills. For example, advice such as "improve task prioritization" may be presented as feedback. In this way, by monitoring and analyzing the user's business data in real time and providing immediate feedback, the user's basic business skills are continuously improved.

[1192] Prompt Sentence Examples

[1193] "Please display the following feedback appropriately: The user is creating a document. The rating is 'The document is difficult to understand.'"

[1194] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1195] Step 1:

[1196] Start monitoring user computer operations

[1197] The user's terminal runs software for monitoring computer operations. This software detects and records the user's operation events (e.g., sending emails, creating documents, etc.) in real time. The input is the user's operation event, and the output is the recorded operation data. For example, when a user creates a new email, the subject, body, and recipient information of that email are recorded.

[1198] Step 2:

[1199] Bundling and sending operational data

[1200] The device bundles the operation data collected at regular intervals and aggregates it into a format that can be sent. This data is sent to the server via an encrypted communication channel. The input is the collected operation data, and the output is the bundled data. Specifically, all operations performed by the user over the course of one hour are compiled into a data set.

[1201] Step 3:

[1202] Receiving and storing data

[1203] The server receives bundled data sent from the terminal. The received data is stored in a database and later used for analysis. The input is the bundled data sent from the terminal, and the output is the data stored in the database. Specifically, the server stores the email transmission data it receives in a specified database.

[1204] Step 4:

[1205] Start of analysis using "generative AI models"

[1206] The server inputs the operation data stored in the database into the "generative AI model" and begins analysis. This model quantifies and evaluates the user's work performance. The input is the operation data in the database, and the output is the analysis results. Specifically, the generative AI model outputs evaluation values ​​for email sending speed and document accuracy.

[1207] Step 5:

[1208] Generate feedback based on analysis results

[1209] The server generates specific feedback based on the analysis results of the generative AI model. This feedback includes specific advice and areas for improvement to improve work skills. The input is the analysis results, and the output is the generated feedback. Specifically, this includes feedback such as "Make the subject line of the email more specific."

[1210] Step 6:

[1211] Sending feedback to users

[1212] The server sends the generated feedback to the user's device. The sent feedback is encrypted and notified to the user. The input is the generated feedback, and the output is the feedback sent to the user's device. As a specific operation, while the user is composing an email, a notification pops up urging the user to "make the subject more specific."

[1213] Step 7:

[1214] Reviewing user feedback and incorporating it into work

[1215] The user checks the feedback displayed on the device and reflects it in their next work. The input is the feedback displayed on the device, and the output is improved work performance. Specifically, the next time the user composes an email, they will be more specific in the subject line.

[1216] (Application example 1)

[1217] 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."

[1218] Improving the operational efficiency and work performance of robots in factories is a critical issue for workers. Conventional systems have struggled to monitor robot operation data in real time, analyze that data, and provide specific feedback. This has made it difficult for factory operators and engineers to continuously improve their robot operation skills and prevent errors before they occur. Therefore, there is a need for a system that can collect and analyze robot operation data in real time and provide specific feedback.

[1219] 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.

[1220] In this invention, the server includes: a means for monitoring user operations in real time and transmitting collected operation data to the server; a means for analyzing the operation data and evaluating the user's performance in the server; a means for generating and notifying specific feedback to the user based on the analysis results; a means for linking with different tools and collecting and analyzing data from the tools; a means for collecting robot operation data and periodically transmitting it to the server; and a means for displaying feedback from the server in real time on a terminal. This allows operators to receive specific improvements and advice on their robot operation in real time. This is expected to improve the work performance of factory operators and engineers and enable efficient robot operation.

[1221] "User" means a person or entity that utilizes the system to provide operational data and receive feedback therefrom.

[1222] "Operation data" is information about a series of actions performed by a robot or tool and the results of those actions.

[1223] A "server" is a central processing unit for receiving operational data, performing analysis, and generating feedback.

[1224] "Performance" refers to the efficiency and accuracy of a task performed by a user or robot.

[1225] "Real time" refers to a time frame in which events are processed as they occur.

[1226] "Tools" refers to the software and hardware used by users and robots to perform operations.

[1227] "Feedback" refers to specific advice and improvements generated based on the analysis results.

[1228] "Notifications" are a means of communicating feedback and other information to users.

[1229] The term "robot" refers to an automated mechanical device operated by an operator in a factory or the like.

[1230] "Data collection" is the process of collecting data about the robot and user operations.

[1231] "Analysis" is the process of evaluating collected data and measuring performance.

[1232] "Evaluation" is the act of quantitatively judging the performance of a user or robot based on the analysis results.

[1233] "Device" means the electronic device (e.g., tablet, smart glasses) used by the user to receive feedback.

[1234] This invention is a system designed to improve the operational efficiency and business performance of factory robots. This system includes a series of processes that monitors robot operation data in real time, analyzes the data, and generates feedback. An embodiment of this system will be described in detail below.

[1235] Hardware and Software Configuration

[1236] Server-side configuration

[1237] The server consists of the following hardware and software:

[1238] Hardware: High-performance central processing units (e.g., AWS EC2 instances, GCP Compute Engine)

[1239] Software: Artificial intelligence model for data analysis (generative AI model) (e.g., implemented in TensorFlow or PyTorch), analysis script (e.g., Python), database (e.g., PostgreSQL)

[1240] The server receives and analyzes the collected operation data. The analysis uses data such as the robot's movement data, error occurrence status, and task completion time. This data is processed by a "generative AI model" to evaluate the robot's operation performance. Appropriate feedback is generated from the analysis results and notified to the user's device.

[1241] Terminal configuration

[1242] A user's device consists of the following hardware and software:

[1243] Hardware: Tablets, smart glasses, or other electronic devices

[1244] Software: Application for feedback notification (e.g. iOS / Android application)

[1245] The user's device receives feedback from the server in real time and displays it to the user as a pop-up message or notification, allowing the user to quickly check and respond to the feedback.

[1246] Process example

[1247] For example, consider a situation where a robot is assembling parts in a factory. In this case, the following process is executed:

[1248] 1. Data collection: Sensors and cameras installed on the robot collect operation data and error occurrence status in real time and periodically send it to a server.

[1249] 2. Data analysis: The received data is analyzed using a "generative AI model" to evaluate the robot's operational performance.

[1250] 3. Feedback generation: Based on the analysis results, specific feedback is generated to the user (e.g., "The installation of part A is unstable. Please adjust the motor torque setting").

[1251] 4. Notification: The feedback is notified to the user's device and displayed as a pop-up message.

[1252] Prompt Sentence Examples

[1253] Below are some examples of prompts used in this system:

[1254] Data collected: Robot operation data, error occurrence status, operation time

[1255] AI analysis result: "Part A installation operation is unstable"

[1256] Feedback generation: "Advise to adjust motor torque settings"

[1257] In this way, it becomes possible to provide appropriate feedback to users based on the operation data of factory robots, enabling efficient operation.

[1258] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1259] Step 1:

[1260] The terminal collects operational data (e.g., movement data, error occurrence status, task completion time) through sensors and cameras installed on the robot. This data is measured in real time.

[1261] Input: Robot operation data, error occurrence status, task completion time

[1262] Data processing: Collecting raw data from sensors and cameras

[1263] Output: Structured operation data

[1264] Step 2:

[1265] The terminal transmits the collected operation data to the server at regular intervals (e.g., every minute).

[1266] Input: Collected operational data

[1267] Data processing: Convert to JSON format etc. and send over the network

[1268] Output: Operation data received on the server side

[1269] Step 3:

[1270] The server then inputs the received operational data into a "generative AI model" for analysis, where the data is pre-processed and converted into a format suitable for the model.

[1271] Input: Operation data sent to the server

[1272] Data calculations: performance analysis using generative AI models (e.g., operation accuracy, error rate)

[1273] Output: Analysis results

[1274] Step 4:

[1275] The server generates specific feedback based on the analysis results from the generative AI model, such as advice on areas where errors frequently occur during robot operation and areas for improvement.

[1276] Input: Analysis results

[1277] Data arithmetic: Advice generation based on feedback generation algorithms

[1278] Output: Specific feedback (e.g., advice and improvements)

[1279] Step 5:

[1280] The server sends the generated feedback to the user's device in real time, either through pop-up messages or real-time notifications.

[1281] Input: Specific feedback

[1282] Data processing: generating notification formats (e.g., popup messages)

[1283] Output: Feedback displayed on the terminal

[1284] Step 6:

[1285] The device visualizes the received feedback to the user, allowing them to see improvements to their operation in real time.

[1286] Input: Feedback from the server

[1287] Data processing: Display feedback on the screen

[1288] Output: A feedback message for the user to see.

[1289] Step 7:

[1290] Users can adjust the robot's operation based on feedback received through the device, allowing them to refine their operation in real time.

[1291] Input: Feedback message

[1292] Data processing: Adjusting the robot's operation method

[1293] Output: Improved operation results

[1294] 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.

[1295] This invention is an "AI personal mentor" system designed to improve the basic business skills of desk workers, and is equipped with the ability to recognize and analyze the user's emotions. The system monitors the user's computer operations in real time, collecting operational and emotional data and sending it to a server, which then analyzes the data and provides feedback. This feedback includes specific advice and suggestions for improvement to improve the user's work skills.

[1296] Server-side processing

[1297] Data collection

[1298] The server receives operation data and emotion data sent in real time from the user's device. The user's operation details (e.g., sending and receiving emails, creating documents, operating project management tools) and emotion recognition data (e.g., facial expressions, voice, text input, etc.) are recorded and sent to the server. This data is temporarily stored in storage.

[1299] Data analysis

[1300] The server analyzes the received operation data and emotional data. This is where the multimodal AI "Gemini" and emotion engine come into play. The operation data and emotional data are combined and analyzed to evaluate the user's work performance and emotional state. For example, evaluation criteria include email response speed, document structure, task prioritization, emotional changes, and stress levels.

[1301] Generate feedback

[1302] The server generates feedback based on the analysis results. This feedback includes suggestions for improvement and specific advice to help users improve their work skills. It also includes a function to provide timely feedback based on emotional data. For example, if a user is feeling stressed, the server generates advice such as "take a break to relax."

[1303] Integration with business tools

[1304] The server connects to business tools such as project management tools, messaging tools, and document creation tools. This allows it to collect operational data from each tool and add it to the analysis. For example, it obtains task completion status and comment contents from the project management tool.

[1305] Feedback Notification

[1306] The server sends the generated feedback to the user's device via pop-up, email, in-app notification, etc. The user can check the feedback in real time and apply it to their work.

[1307] Terminal side processing

[1308] User Action Monitoring

[1309] The device monitors the user's computer operations in real time. It detects specific operations (e.g., sending emails or creating files) and records the details of those operations. At the same time, the device also collects emotional data such as the user's facial expressions, voice, and text input. This data is sent to a server at regular intervals.

[1310] Sending data

[1311] The device sends the collected operation data and emotion data to the server. Data transmission is performed periodically and synchronized in real time, allowing the server to always have the latest operation data and emotion data.

[1312] Receiving and viewing feedback

[1313] The device receives feedback from the server, which is then displayed to the user in real time. For example, a notification pops up telling the user to specifically change the subject of the email. The device also displays advice based on the user's emotional state.

[1314] User behavior

[1315] Business execution

[1316] Users can carry out their normal work. No special operations or settings are required, and they can use the system while performing their daily work.

[1317] Review and respond to feedback

[1318] The user checks the feedback displayed on the device, and if necessary, reviews their work methods and applies the advice to their next task. For example, if the feedback advises them to "improve the document headings," they can apply that advice the next time they create a document. Furthermore, by referring to advice based on emotional data, they can manage stress and control their emotions.

[1319] For example, if a user is using a project management tool to manage tasks, the server collects and analyzes the task completion status and comments. It also collects emotional data as the user progresses through the task and incorporates this data into the analysis. This allows the server to provide detailed feedback on the user's project management capabilities. For example, the server may provide advice such as "improve task prioritization" or "take short breaks when under stress."

[1320] In this way, the system of the present invention monitors and analyzes the user's business data and emotional data in real time and provides comprehensive feedback, thereby enabling the user to continuously improve their basic business skills and emotional management skills.

[1321] The processing flow will be explained below.

[1322] Server-side processing

[1323] Step 1: Collect data

[1324] The server receives operational data and emotional data sent from the user's device in real time. Specifically, the server records the user's operations (e.g., sending and receiving emails, creating documents, and using project management tools) and emotional recognition data (e.g., facial expressions, voice, text input, etc.) and sends them to the server.

[1325] Step 2: Save your data

[1326] The server stores the received operation data and emotion data in temporary storage, which is used in subsequent analysis steps.

[1327] Step 3: Analyze the data

[1328] The server analyzes the operation data and emotional data stored in the storage. This analysis is performed using the multimodal AI "Gemini" and an emotion engine. This evaluates the user's work performance and emotional state. For example, evaluation items include email response speed, document creation accuracy, task prioritization, and the user's stress level.

[1329] Step 4: Generate feedback

[1330] The server generates feedback based on the analysis results. This feedback includes specific advice and areas for improvement to help users improve their work skills. For example, advice such as "You should be more specific in the subject line of your emails" is generated. Feedback is also provided based on emotional data, and if a user is feeling stressed, advice such as "Take a break to relax" is provided.

[1331] Step 5: Integration with business tools

[1332] The server connects to business tools such as project management tools, messaging tools, and document creation tools. This allows it to collect operational data from each tool and add it to the analysis. For example, it obtains task completion status and comment contents from the project management tool.

[1333] Step 6: Notification of feedback

[1334] The server sends the generated feedback to the user's device via pop-up, email, in-app notification, etc. The user can check the feedback in real time and apply it to their work.

[1335] Terminal side processing

[1336] Step 1: Monitoring user actions

[1337] The device monitors the user's computer operations in real time, detecting specific operations (e.g., sending emails or creating files) and recording the details of those operations, as well as collecting emotional data such as the user's facial expressions, voice, and text input.

[1338] Step 2: Sending data

[1339] The device sends the collected operation data and emotion data to the server. Data transmission is performed periodically and synchronized in real time, allowing the server to always have the latest operation data and emotion data.

[1340] Step 3: Receive and view feedback

[1341] The device receives feedback from the server, which is then displayed to the user in real time. For example, a notification pops up telling the user to specifically change the subject of the email. The device also displays advice based on the user's emotional state.

[1342] User behavior

[1343] Step 1: Getting the job done

[1344] Users can carry out their normal work. No special operations or settings are required, and they can use the system while performing their daily work.

[1345] Step 2: Review and respond to feedback

[1346] Users can check the feedback displayed on their device, revise their work methods as needed, and apply the advice to their next task. For example, if they are notified to "improve the headings in a document," they can apply that advice the next time they create a document. Furthermore, they can also refer to advice based on emotional data to manage stress and control their emotions.

[1347] For example, if a user is using a project management tool to manage tasks, the server collects and analyzes the task completion status and comments. It also collects emotional data as the user progresses through the task and incorporates this data into the analysis. This allows the server to provide detailed feedback on the user's project management capabilities. For example, the server may provide advice such as "improve task prioritization" or "take short breaks when under stress."

[1348] Example 2

[1349] 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."

[1350] Conventional business support systems only analyzed user operation data and were unable to provide advice or suggestions for improvement that took into account emotional data such as the user's emotional state and stress level. This limited the ability to improve users' work performance and made it difficult to optimize overall work efficiency and emotional management. Furthermore, they lacked the ability to manage and analyze operation data from multiple business tools in an integrated manner, making it difficult to obtain comprehensive feedback.

[1351] 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.

[1352] In this invention, the server includes means for monitoring a user's computer operations in real time and transmitting collected operation data and emotional data to the server, means for analyzing the operation data and emotional data in the server and evaluating the user's work performance and emotional state, means for generating specific feedback to the user based on the analysis results and notifying them in a timely manner, and means for linking different work tools and collecting data from each work tool to add to the analysis. This makes it possible to simultaneously improve a user's work performance and optimize their emotional management, and provide comprehensive feedback from various angles.

[1353] "User" means an individual who uses the System to perform business operations.

[1354] "Computer operation" refers to a series of operations or actions that a user performs on a computer.

[1355] "Real-time" refers to processing and communication occurring immediately, without delay.

[1356] "Operational Data" refers to specific data about operations performed by a user on a computer.

[1357] "Emotion data" refers to data that indicates the user's emotional state, and includes facial expression changes, voice, text input, and the like.

[1358] "Server" refers to a central processing unit for analyzing collected operation data and emotion data and providing feedback.

[1359] "Analysis" refers to the process of examining collected data in detail and deriving its meaning and value.

[1360] "Business performance" refers to the efficiency and effectiveness with which users carry out their work.

[1361] "Evaluation" refers to judging a user's work performance and emotional state based on analyzed data.

[1362] "Feedback" refers to advice and improvements provided to users based on the analysis results.

[1363] "Timely notification" means that feedback is communicated to the user at an appropriate time.

[1364] "Business tools" refers to various software used to carry out business, such as project management tools, messaging tools, and document creation tools.

[1365] "Integration" refers to sharing data between different business tools and making it mutually usable.

[1366] "Collecting data and adding it to the analysis" refers to obtaining operational data from each business tool and incorporating it into the analysis process.

[1367] This invention is an "AI personal mentor" system designed to improve the basic business skills of desk workers, and is equipped with the ability to recognize and analyze the user's emotions. The system monitors the user's computer operations in real time, collecting operational and emotional data and sending it to a server, which then analyzes the data and provides feedback. This feedback includes specific advice and suggestions for improvement to improve the user's work skills.

[1368] Hardware and software used

[1369] The system uses the following hardware and software:

[1370] User device (PC, laptop, etc.)

[1371] Server (with high performance processing capabilities)

[1372] Multimodal AI "Gemini"

[1373] Sentiment Analysis Engine

[1374] Project management tools, messaging tools, document creation tools, etc.

[1375] Data collection and transmission

[1376] The device collects various operations performed by the user (for example, sending and receiving emails, creating documents, and using project management tools) as well as emotion recognition data such as facial expressions, voice, and text input in real time. This data is then sent to a server at regular intervals. Specific technologies used for this include data streaming technology and real-time communication protocols.

[1377] Data analysis

[1378] The server uses the multimodal AI "Gemini" and an emotion analysis engine to analyze the collected operation data and emotional data. This analysis evaluates the user's work performance (e.g., email response speed, document structure, task prioritization) and emotional state (e.g., stress level and emotional changes). Combining the operation data and emotional data allows for more detailed feedback.

[1379] Feedback generation and notification

[1380] The server generates specific feedback for the user based on the analysis results. This feedback includes suggestions and advice for improving the user's work skills (e.g., "improve the structure of the document" or "change the subject line of the email to be more specific"). It also provides timely feedback based on the user's emotional data. For example, it recommends "taking a short break" when the user is under high stress.

[1381] Integration with business tools

[1382] The server works with project management tools, messaging tools, document creation tools, etc., collecting operational data from each tool and adding it to the analysis, making it possible to provide feedback that integrates data from all the business tools used by the user.

[1383] Specific examples

[1384] When a user manages a task using a project management tool, the server collects and analyzes the task completion status and comments. It also collects and considers the user's emotional data as they progress through the task, allowing it to provide detailed feedback on the user's project management ability.

[1385] Prompt Sentence Examples

[1386] "What techniques are effective for improving project prioritization?"

[1387] "What are the benefits of taking a short break when you're stressed?"

[1388] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1389] Step 1: Collect data

[1390] The device monitors the user's computer operations in real time, specifically detecting operations such as sending and receiving emails, creating documents, and using project management tools. It also simultaneously collects the user's emotional data, including facial expressions, voice, and text input.

[1391] Input: User's computer operation data and emotional data.

[1392] Output: Collected operation data and sentiment data.

[1393] Step 2: Sending data

[1394] The device transmits the collected operation data and emotion data to a server at regular intervals, using data streaming technology and real-time communication protocols.

[1395] Input: Collected operation data and emotion data.

[1396] Output: The data sent to the server.

[1397] Step 3: Temporarily save data

[1398] The server receives the operation data and emotion data sent from the terminal and temporarily stores them in storage.

[1399] Input: Submitted operation data and emotion data.

[1400] Output: Temporarily stored data.

[1401] Step 4: Analyze the data

[1402] The server uses the multimodal AI "Gemini" and an emotion analysis engine to analyze the stored operation data and emotion data, specifically assessing the user's work performance (e.g., email response speed, document structure, task prioritization) and emotional state (e.g., stress level and emotional changes).

[1403] Input: Temporarily stored operation data and emotion data.

[1404] Output: Analysis results (evaluation of work performance and emotional state).

[1405] Step 5: Generate feedback

[1406] The server generates specific feedback for the user based on the analysis results, including specific advice and suggestions for improvement (e.g., "Improve the structure of the document," "Modify the subject line of the email specifically") and emotion management advice (e.g., "Take a short break").

[1407] Input: Analysis results.

[1408] Output: Specific feedback generated.

[1409] Step 6: Notification of feedback

[1410] The server notifies the user of the generated feedback in a timely manner via pop-ups, emails, in-app notifications, etc.

[1411] Input: Generated feedback.

[1412] Output: Feedback sent to the user's device.

[1413] Step 7: Review and respond to feedback

[1414] Users can review the feedback displayed on their device and adjust their work methods as needed. For example, if they receive feedback to "improve task prioritization," they can apply that advice the next time they work. They can also use advice based on emotional data to manage stress and control their emotions.

[1415] Input: Feedback sent to the user's device.

[1416] Output: User-identified and applied work improvements and emotion management.

[1417] Through this process, users receive specific and useful feedback in real time, enabling them to improve their work efficiency and emotional management.

[1418] (Application example 2)

[1419] 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."

[1420] Previous business skill improvement systems only provided evaluations and feedback based on operational data, but were unable to provide feedback that took into account the user's emotional state. Furthermore, they lacked integration with other tools and were slow to provide real-time feedback. This limited the improvement of users' work skills, making it difficult to address declines in work efficiency, especially under high-stress conditions.

[1421] 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.

[1422] In this invention, the server includes means for monitoring user operations in real time and transmitting collected operation data and emotional data to the server, means for analyzing the operation data and emotional data in the server and evaluating the user's work performance and emotional state, means for generating and notifying the user of specific feedback based on the analysis results, means for linking with different tools and collecting and analyzing data from the tools, means for transmitting data collected by the terminal to the server and notifying the terminal of feedback generated on the server side, and means for monitoring the emotional data and work performance using a display device worn by the user and displaying feedback in real time. This makes it possible to provide detailed feedback that takes the user's emotional state into consideration, thereby improving work skills and emotion management ability while improving work efficiency.

[1423] "User" refers to a person who uses the system to perform work.

[1424] "Operation data" includes information about various operations performed by a user using an electronic device.

[1425] "Emotional data" refers to information that includes information about a user's emotional state, such as facial expressions, voice, text input, etc.

[1426] A "server" is a computer system that receives and analyzes data over a network.

[1427] "Analysis" refers to the process of identifying patterns or evaluations based on collected data.

[1428] "Feedback" refers to advice, improvements, and information generated based on the analysis results.

[1429] "Tools" refers to electronic software or devices used to perform a task.

[1430] "Notification" refers to the act of providing information to a user in real time.

[1431] "Terminal" refers to an electronic device operated by a user and used to collect data.

[1432] "Display device" refers to an electronic device for presenting information to the user's visual field.

[1433] "Monitoring" refers to the act of monitoring a user's actions and emotional state in real time.

[1434] "Real-time" refers to the timing of data collection, analysis, and feedback occurring almost immediately.

[1435] This invention uses a system that uses a display device worn by store staff to evaluate the user's emotional state and work performance in real time and provide appropriate feedback. Specific implementation methods of the system are described below.

[1436] 1. System Configuration

[1437] The server receives operation data and emotion data sent in real time from the user's device. Operation data includes information related to the user's email correspondence, document creation, and project management. Emotion data includes the user's facial expressions, voice, and text input. The server temporarily stores this data in storage.

[1438] The device is responsible for monitoring the user's computer operations in real time. It detects specific operations (e.g., sending emails or creating files) and records the details of those operations, while also collecting emotional data such as the user's facial expressions, voice, and text input. This data is then sent to a server at regular intervals.

[1439] 2. Data analysis and feedback

[1440] The server analyzes the received operation data and emotional data. The analysis uses the multimodal AI "Gemini" and an emotion engine. The operation data and emotional data are combined and analyzed to evaluate the user's work performance and emotional state. For example, evaluation items include email response speed, document structure, task prioritization, emotional changes, and stress levels. Based on the analysis results, the server generates specific improvements and advice to improve the user's work skills. The system is also configured to display appropriate feedback when the user is feeling stressed.

[1441] 3. Integration with business tools

[1442] The server can be linked to business tools such as project management tools, messaging tools, and document creation tools. This allows operation data to be collected from each tool and added to the analysis. For example, by obtaining task completion status and comment contents from a project management tool, detailed feedback on the user's project management ability can be provided.

[1443] 4. Data transmission and notification

[1444] The device sends the collected operation data and emotion data to the server. Data transmission is performed periodically and synchronized in real time. This allows the server to always have the latest operation data and emotion data. The server notifies the user's device of the generated feedback, and the received feedback is displayed to the user in real time. For example, a notification saying "Change the subject of the email specifically" is displayed as a pop-up. Advice based on the user's emotional state is also displayed in the same way.

[1445] 5. User Conduct

[1446] Users use the system while performing their daily work. No special operations or settings are required. They are encouraged to check the feedback they receive, revise their work methods as needed, and apply the advice to their next task. For example, if the feedback suggests "improving the document headings," they can apply that advice the next time they create a document. They can also use the system to manage stress and control their emotions by referring to advice based on emotional data.

[1447] Specific examples

[1448] Imagine a situation where a customer may be dissatisfied during a very busy time in a store. When a staff member becomes tired, the system suggests "taking a five-minute break," helping to improve the quality of customer service. Also, if a customer looks dissatisfied while a staff member is interacting with them, the system will display specific advice such as "offering additional support to the customer."

[1449] Prompt Sentence Examples

[1450] "If you recognize a situation where a customer is dissatisfied with the service, offer solutions. Also, if staff are tired, offer effective break times."

[1451] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1452] Step 1:

[1453] Collecting user interaction and sentiment data

[1454] The device monitors and collects in real time the user's computer operations and emotional data such as facial expressions, voice, and text input. For example, operation data includes email sending and receiving and document creation history, while emotional data includes facial expression data obtained through facial recognition. The input is the user's operations and emotional state, and the output is the collected operation data and emotional data.

[1455] Step 2:

[1456] Sending data

[1457] The collected operation data and emotion data are sent to the server at regular intervals. The device synchronizes the data with the server in real time, ensuring that the server always has the latest data. In this step, the input is the collected operation data and emotion data, and the output is the data sent to the server.

[1458] Step 3:

[1459] Data analysis

[1460] The server analyzes the received operation data and emotional data. The analysis uses a multimodal AI "AI model" and an emotion engine to evaluate business performance and emotional state. In the analysis process, operation data (e.g., email response speed and document structure) and emotional data (changes in facial expressions and stress levels) are evaluated. The input is the operation data and emotional data sent to the server, and the output is the analysis results.

[1461] Step 4:

[1462] Generate feedback

[1463] Based on the analysis results, the server generates specific improvements and advice for improving the user's work skills. It also generates appropriate feedback when the user feels stressed. The input is the analysis results, and the output is the feedback content. For example, advice such as "take a five-minute break" or "change the subject line of your email to be more specific" is included.

[1464] Step 5:

[1465] Data Notification

[1466] The server notifies the device of the generated feedback. The device displays the received feedback to the user in real time. In this step, the input is the feedback content from the server, and the output is the feedback displayed to the user. For example, the feedback is displayed as a pop-up on the device screen.

[1467] Step 6:

[1468] Integration with business tools

[1469] The server connects to business tools such as project management tools, messaging tools, and document creation tools. This allows it to collect operational data from each tool and add it to the analysis. In this step, the input is data from each business tool, and the output is the integrated data sent to the server for analysis.

[1470] Step 7:

[1471] User behavior

[1472] While carrying out their normal work, users can check the feedback displayed on their device and apply improvements and advice as needed. For example, they can refer to feedback such as "improve the document headings" or "suggest additional support to customers" and reflect these in their next work. The input is the feedback displayed on the device, and the output is the user's work improvement actions.

[1473] 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.

[1474] 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.

[1475] 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.

[1476] 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.

[1477] 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.

[1478] 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.

[1479] 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).

[1480] 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.

[1481] 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."

[1482] 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.

[1483] 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).

[1484] 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.

[1485] 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.

[1486] 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.

[1487] 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.

[1488] 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.

[1489] 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.

[1490] 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.

[1491] 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.

[1492] 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.

[1493] 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.

[1494] The following is further disclosed regarding the above embodiment.

[1495] (Claim 1)

[1496] a means for monitoring the user's computer operations in real time and transmitting the collected operation data to a server;

[1497] a means for analyzing the operation data in the server and evaluating the user's business performance;

[1498] A means for generating and notifying specific feedback to users based on the analysis results;

[1499] A means of linking with different business tools and collecting and analyzing data from business tools,

[1500] A system including:

[1501] (Claim 2)

[1502] 10. The system of claim 1, wherein the collected operational data includes data related to a user's email sending and receiving, document creation, and project management.

[1503] (Claim 3)

[1504] 2. The system of claim 1, wherein the specific feedback includes advice and suggestions for improvement to improve the user's work skills.

[1505] (Claim 4)

[1506] 10. The system of claim 1, wherein the different business tools include a project management tool, a messaging tool, and a document creation tool.

[1507] (Claim 5)

[1508] 10. The system of claim 1, wherein the server uses an artificial intelligence model to analyze operational data and generate feedback.

[1509] "Example 1"

[1510] (Claim 1)

[1511] means for monitoring a user's computer operation in real time and transmitting collected operation data to an information processing device;

[1512] means for analyzing the operation data in the information processing device and evaluating the user's business performance;

[1513] A means for generating and notifying specific feedback to users based on the analysis results;

[1514] A means of linking with different business applications and collecting and analyzing data from business applications,

[1515] a means for periodically bundling the collected operation data and transmitting the bundled data to a server;

[1516] means for storing the received operational data in a database;

[1517] A means of analyzing operational data using an artificial intelligence model and quantifying and evaluating business performance;

[1518] A means for generating feedback based on the analyzed performance evaluation, providing specific improvements and advice to the user; and

[1519] a means for transmitting the generated feedback to a user's device and displaying it in real time;

[1520] A system including:

[1521] (Claim 2)

[1522] 10. The system of claim 1, wherein the collected operational data includes data related to a user's email sending and receiving, document creation, and project management.

[1523] (Claim 3)

[1524] 2. The system of claim 1, wherein the specific feedback includes advice and suggestions for improvement to improve the user's work skills.

[1525] "Application Example 1"

[1526] (Claim 1)

[1527] A means for monitoring user operations in real time and transmitting collected operation data to a server;

[1528] means for analyzing the operation data in the server and evaluating the user's performance;

[1529] A means for generating and notifying specific feedback to users based on the analysis results;

[1530] A means of linking with different tools and collecting and analyzing data from the tools,

[1531] A means for collecting robot operation data and transmitting the data to a server at regular intervals;

[1532] A means for displaying feedback from the server on the device in real time;

[1533] A system including:

[1534] (Claim 2)

[1535] 10. The system of claim 1, wherein the collected operational data includes data related to robot behavior, error occurrences, and task completion times.

[1536] (Claim 3)

[1537] 2. The system of claim 1, wherein the specific feedback includes advice and improvements for improving robot operation skills.

[1538] "Example 2: Combining Emotion Engines"

[1539] (Claim 1)

[1540] a means for monitoring the user's computer operations in real time and transmitting the collected operation data and emotion data to a server;

[1541] a means for analyzing the operation data and emotion data in the server and evaluating the user's work performance and emotion state;

[1542] A means for generating specific feedback to users based on the analysis results and notifying them in a timely manner;

[1543] A means of linking with different business tools, collecting data from each business tool, and adding it to the analysis.

[1544] A system including:

[1545] (Claim 2)

[1546] 10. The system of claim 1, wherein the collected operational data includes data related to a user's email sending and receiving, document creation, and project management.

[1547] (Claim 3)

[1548] 10. The system of claim 1, wherein the specific feedback includes advice and improvements for improving the user's work skills and advice on emotion management.

[1549] "Application example 2 when combining emotion engines"

[1550] (Claim 1)

[1551] A means for monitoring user operations in real time and transmitting collected operation data and emotion data to a server;

[1552] a means for analyzing the operation data and emotion data in the server and evaluating the user's work performance and emotion state;

[1553] A means for generating and notifying specific feedback to users based on the analysis results;

[1554] A means of linking with different tools and collecting and analyzing data from the tools,

[1555] means for transmitting the collected data to a server by the terminal and notifying the terminal of feedback generated by the server;

[1556] a means for monitoring emotional data and work performance and displaying real-time feedback using a display device worn by the user;

[1557] A system including:

[1558] (Claim 2)

[1559] 10. The system of claim 1, wherein the collected operational data includes data related to a user's email correspondence, document creation, and project management.

[1560] (Claim 3)

[1561] 10. The system of claim 1, wherein the specific feedback includes advice and improvements for improving the user's work skills, as well as stress management advice based on the user's emotional state. [Explanation of symbols]

[1562] 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 monitoring the user's computer operations in real time and transmitting the collected operation data to a server; a means for analyzing the operation data in the server and evaluating the user's business performance; A means for generating and notifying specific feedback to users based on the analysis results; A means of linking with different business tools and collecting and analyzing data from business tools, A system including:

2. 10. The system of claim 1, wherein the collected operational data includes data related to a user's email sending and receiving, document creation, and project management.

3. The system according to claim 1 , wherein the specific feedback includes advice and points for improvement for improving the user's work skills.

4. The system of claim 1 , wherein the different business tools include a project management tool, a messaging tool, and a document creation tool.

5. The system of claim 1 , wherein the server uses an artificial intelligence model to analyze operational data and generate feedback.

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