system

Abstract

Provide a system. A means for providing a user interface for a subordinate to input case information; means for receiving input case information and extracting relevant concerns using a generative artificial intelligence model; a means for evaluating case information based on extracted concerns using a supervisor's individualized judgment model; a means for transmitting and displaying the evaluation results on the subordinate's user terminal; A means for subordinates to re-edit project information based on the evaluation results and upload it to the project management system; 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 modern companies, communication between superiors and subordinates, especially those in high positions, is extremely important, but due to the busy schedules of superiors, it is difficult to find time for consultations and meetings. It is also difficult to report and consult from each department in a timely and effective manner, which slows down the pace of business development. Furthermore, when there are multiple projects, there is a need to understand their progress and quickly resolve problems. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by providing a means for providing a user interface for subordinates to input project information and a means for receiving the input project information and extracting related concerns using a generative artificial intelligence model. Furthermore, it provides a means for evaluating the project information based on the extracted concerns using the supervisor's personalized judgment model. The evaluation results are sent to the subordinate's user device and displayed. This allows the subordinate to re-edit the project information based on the feedback and upload it to a project management system. Furthermore, the evaluation results and the re-edited project information are stored in a database to track project progress and, if necessary, notify the supervisor's digital twin. This enables rapid project progress and efficient problem resolution without the supervisor's intervention.

[0006] A "subordinate" is an employee within an organization who performs work under the instructions of a superior.

[0007] "Project Information" means data or documents containing detailed information related to a particular project or task.

[0008] "User interface" is a general term for the display screen and input means that allow a user to interact with a system.

[0009] A "generative artificial intelligence model" is an artificial intelligence model that has the ability to automatically generate text and information based on large datasets.

[0010] "Concerns" are elements that indicate potential problems or risks with the project.

[0011] A "boss's individualized judgment model" is an artificial intelligence model that has learned the decision-making process and values ​​of a specific boss.

[0012] The "evaluation results" are information that indicates the importance of concerns and the priority of countermeasures.

[0013] A "user terminal" is a device used by a user, such as a computer or smartphone.

[0014] "Feedback" is a general term for comments and suggestions for improvement provided based on evaluation results.

[0015] "Re-editing" is the process of revising and improving project information based on feedback.

[0016] A "project management system" is a software tool for managing multiple projects in an integrated manner and tracking and evaluating their progress.

[0017] A "database" is a system for systematically storing and managing information.

[0018] "Project progress" refers to indicators or conditions that indicate whether the project is progressing as planned.

[0019] A "digital twin" is a digital model that virtually recreates a real person or physical event. [Brief explanation of the drawings]

[0020] [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

[0021] 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.

[0022] First, the terms used in the following description will be explained.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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."

[0028] [First embodiment]

[0029] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0030] 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.

[0031] 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).

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0037] 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.

[0038] 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.

[0039] 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.

[0040] 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."

[0041] The present invention provides a system for improving the efficiency of communication between superiors and subordinates and for facilitating the processing of cases and decision-making within a company. This system is realized through the following components and processes.

[0042] First, a user interface is provided for subordinates to input project information. Through this interface, subordinates can input detailed information about projects and tasks using their own devices (e.g., computers or smartphones). This information is then sent to the server.

[0043] The server then analyzes the received project information. First, it uses a generative artificial intelligence model (e.g., ChatGPT (registered trademark)) to extract concerns about the project. These concerns are elements that indicate risks or potential problems in the project.

[0044] Based on the extracted concerns, the server calls up the supervisor's personalized decision-making model. This decision-making model learns the decision-making process and values ​​of the specific supervisor and evaluates the priority of the concerns. The evaluation results provide the importance of the concerns and the priority of countermeasures.

[0045] The evaluation results are sent to the subordinate's user device and displayed as feedback. This allows the subordinate to re-edit the project information based on the evaluation results. For example, if a risk of exceeding the budget is pointed out, the subordinate can add information to review the budget. The re-edited project information is then uploaded to the project management system.

[0046] Additionally, the server tracks project progress and notifies the supervisor's digital twin when necessary, allowing for quick action at points where supervisor intervention or confirmation is required.

[0047] As a concrete example, when a subordinate inputs a proposal for a new marketing campaign into the system, the proposal is sent to the server. The server uses a generative artificial intelligence model to extract concerns such as "risk of going over budget" and "unclear target market." These concerns are evaluated using the supervisor's judgment model, resulting in an evaluation result that prioritizes "risk of going over budget." Based on this result, the subordinate can reorganize the budget and clarify the target market. The re-edited proposal is then uploaded to the project management system.

[0048] This system allows the supervisor's digital twin to function effectively, allowing projects to progress quickly without the supervisor's direct intervention, resulting in more efficient communication within the company and improving the speed at which business is promoted.

[0049] The processing flow will be explained below.

[0050] Step 1:

[0051] The user inputs project information. Using the user interface of the device, the user inputs detailed project information, such as a proposal for a new marketing campaign. This input information is collected by the device as text data.

[0052] Step 2:

[0053] The terminal sends the case information entered by the user to the server. When the user presses the "Send" button, the case information is sent to the server via the Internet or a network.

[0054] Step 3:

[0055] The server receives the case information. The server stores the received case information in storage. The stored information includes the case content and user identification information.

[0056] Step 4:

[0057] The server calls a generative AI model and analyzes the received project information. The generative AI model (e.g., ChatGPT) is used to extract project-related concerns (e.g., "risk of exceeding budget" or "unclear target market").

[0058] Step 5:

[0059] The server calls up the supervisor's personalized decision-making model and evaluates the extracted concerns. The decision-making model, which has learned the supervisor's decision-making process and values, evaluates the priority of the concerns (for example, the risk of exceeding the budget is the top priority).

[0060] Step 6:

[0061] The server sends the evaluation results to the subordinate's terminal, where they are displayed as feedback.

[0062] Step 7:

[0063] The device displays the received feedback, allowing users to see the priority and rating of concerns on the device screen.

[0064] Step 8:

[0065] Users can re-edit the project information based on the feedback. For example, if they are told that there is a high risk of going over budget, they can review the budget plan and add more details.

[0066] Step 9:

[0067] The terminal uploads the re-edited project information to the project management system, and the re-edited new project information is saved in the project management system.

[0068] Step 10:

[0069] The server tracks the progress of the project and notifies the supervisor's digital twin as needed, so that specific progress and issues are shared with the supervisor's digital twin in real time, allowing necessary action to be taken promptly.

[0070] This process allows the boss's digital twin to function effectively, enabling subordinates to move projects forward quickly and efficiently.

[0071] Example 1

[0072] 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."

[0073] In modern companies, efficient communication and decision-making between superiors and subordinates is extremely important. Especially when large-scale projects or multiple projects are underway simultaneously, it can be difficult for superiors to accurately grasp the details of all projects and make decisions quickly. Furthermore, delays in reporting and feedback from subordinates to superiors can sometimes delay the progress of a project. There is a need for a system that can eliminate these inefficient communications and support rapid and accurate decision-making.

[0074] 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.

[0075] In this invention, the server includes means for providing a user interface for a subordinate to input project information, means for receiving the input project information and extracting related concerns using a generative artificial intelligence model, means for evaluating the project information based on the extracted concerns using the supervisor's personalized judgment model, means for sending and displaying the evaluation results to the subordinate's user terminal, means for the subordinate to re-edit the project information based on the evaluation results and upload it to a project management system, and means for tracking project progress and notifying the supervisor's digital twin as necessary. This enables rapid reception and analysis of project information from subordinates, efficient feedback to the supervisor, real-time tracking of project progress, and timely notification to the supervisor's digital twin.

[0076] "Project information" refers to detailed information about a project or task, specifically including the project name, summary, goals, deadline, budget, etc.

[0077] A "user interface" is a means by which a user directly interacts with a computer system, and primarily includes graphical user interfaces and web interfaces.

[0078] A "server" refers to a computer system that provides data or services in response to requests from clients.

[0079] A "generative artificial intelligence model" refers to an AI model that has the ability to generate new information or patterns from given data; an example is a natural language generation model.

[0080] "Concerns" are elements that indicate risks or potential problems with the project.

[0081] An "individualized boss judgment model" refers to an AI model that learns the decision-making process and values ​​of a specific boss and makes evaluations based on that.

[0082] "Evaluation results" refer to the analysis results and recommendations obtained using generative artificial intelligence models and judgment models.

[0083] A "user terminal" is a device that is directly operated by a user, and examples include computers and smartphones.

[0084] "Project management system" refers to a software tool used to manage the planning, execution, monitoring, and closure of projects.

[0085] "Project progress" refers to information showing the progress of a project and its current achievement status.

[0086] "Digital twin" refers to a digital representation of a physical entity, and in this context refers to an AI agent that mimics the decisions and actions of its superior.

[0087] This invention is a system for improving communication between superiors and subordinates and for accelerating case processing and decision-making within a company. This system is realized using subordinate terminals, a server, and a generative artificial intelligence model.

[0088] First, the subordinate uses his / her own device (e.g., a computer or smartphone) to enter project information. The user interface for this input is provided through a dedicated application or web browser. The subordinate enters details such as the project name, summary, goals, deadline, and budget into the form and clicks the "Submit" button, which sends the information to the server.

[0089] The server stores the received case information in a database and uses a generative artificial intelligence model (e.g., ChatGPT) to extract concerns related to the case. Concern extraction involves inputting the case information into the ChatGPT API and obtaining the extracted results in text format. For example, the prompt could be, "Please extract potential risks and issues from the given marketing campaign proposal."

[0090] Next, the server applies the supervisor's personalized judgment model (e.g., TENSORFLOW (registered trademark) model) to the extracted concerns and evaluates their importance. Here, the server loads the judgment model and inputs the concern data to obtain the evaluation results. For example, a request such as "Please evaluate the importance of this risk using your supervisor's judgment model" is used as a prompt sentence.

[0091] The evaluation results are sent from the server to the subordinate's device and displayed on the user interface. The subordinate then re-edits the project information based on the feedback and re-uploads it to the project management system. For example, if a risk of going over budget is pointed out, the subordinate will review the budget and enter additional information.

[0092] The progress of the project is continuously tracked by the server, which periodically retrieves and updates status information using the project management system's APIs. If necessary, the server also sends notifications to the supervisor's digital twin. If a notification is deemed necessary, the notification is sent via email or a chat tool (e.g., Slack).

[0093] Through this process, the system can quickly advance projects without direct intervention from superiors, improving communication efficiency within the company and speeding up decision-making.

[0094] As a concrete example, when a subordinate enters a proposal for a new marketing campaign into the system, the proposal is sent to the server. The server uses a generative artificial intelligence model to extract concerns such as "risk of going over budget" and "unclear target market." These concerns are evaluated using the supervisor's judgment model, resulting in an evaluation result that prioritizes "risk of going over budget." Based on this result, the subordinate reorganizes the budget and clarifies the target market. This revised proposal is uploaded to a project management system, and the server tracks progress and sends notifications to the supervisor's digital twin as needed. As a result, communication within the company becomes more efficient and business progress is accelerated.

[0095] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0096] Step 1:

[0097] The subordinate, who is the user, enters project information using a user interface on his or her own device. This user interface is provided through a dedicated application or a web browser. For example, the subordinate enters details such as the project name, summary, goals, deadline, and budget into a form and clicks the "Submit" button.

[0098] Input: Project information (project name, summary, goals, deadline, budget, etc.)

[0099] Output: Data sent from the terminal to the server (JSON format job information)

[0100] Step 2:

[0101] The terminal sends the input job information to the server in real time using an HTTP request.

[0102] Input: Data sent from the device (JSON format job information)

[0103] Output: Data received by the server (JSON format job information)

[0104] Step 3:

[0105] The server stores the received job information in a database and starts the analysis process. The server inserts the job information into the database and then adds it to a queue for the analysis process.

[0106] Input: Data received from the server (JSON format job information)

[0107] Output: Case information saved in the database

[0108] Step 4:

[0109] The server uses a generative artificial intelligence model (e.g., ChatGPT) to extract concerns from the case information. To extract concerns, the server passes the case information as input to the ChatGPT API and obtains the extracted results in text format.

[0110] Input: Case information stored in the database

[0111] Output: Extracted concerns (text format)

[0112] Step 5:

[0113] The server applies the supervisor's personalized judgment model (e.g., TensorFlow model) to the extracted concerns and evaluates their importance. The server loads the judgment model and inputs the concern data to obtain the evaluation results.

[0114] Input: Extracted concerns (text format)

[0115] Output: Evaluation results (importance of concerns and priority of countermeasures, in text format)

[0116] Step 6:

[0117] The server sends the evaluation results to the subordinate's device and displays them as feedback.The server sends the evaluation results in JSON format to the device, and the device displays the results in the user interface.

[0118] Input: Evaluation results (importance of concerns and priority of countermeasures, in text format)

[0119] Output: Evaluation results displayed on the user's device (JSON format)

[0120] Step 7:

[0121] The subordinate, who is the user, re-edits the project information based on the feedback and re-uploads it to the project management system. The subordinate enters the re-edited information and clicks the "Submit" button again to upload it to the project management system.

[0122] Input: Re-edited project information (JSON format)

[0123] Output: Project information stored in the project management system

[0124] Step 8:

[0125] The server continuously tracks the progress of the project through the project management system, periodically calling the project management system's APIs to retrieve and update status information.

[0126] Input: Project management system status information

[0127] Output: Progress data stored on the server

[0128] Step 9:

[0129] If the server determines that a notification should be sent to the supervisor's digital twin based on the project's progress, it will send the notification via email or chat tool.

[0130] Input: Progress data stored on the server

[0131] Output: Notification sent to the manager's digital twin (via email or chat tool)

[0132] The above processing steps enable quick receipt and analysis of project information from subordinates, efficient feedback to superiors, real-time tracking of project progress, and timely notification to the superior's digital twin.

[0133] (Application example 1)

[0134] 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."

[0135] Conventional systems for handling and deciding on projects within companies are prone to lack of communication between subordinates and superiors and delays in the transmission of information, making it difficult to speed up decision-making and efficiently handle projects. Similar communication problems also arise in the field of content planning and distribution between planning teams and management teams. As a result, content production and distribution are frequently delayed. It is necessary to solve these issues and achieve more efficient communication and faster decision-making within companies and in the content field.

[0136] 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.

[0137] In this invention, the server includes means for providing a user interface for subordinates and content planning teams to input project information and content planning information, means for receiving the input project information and content planning information and extracting related concerns using a generative artificial intelligence model, means for evaluating the project information and content planning information based on the extracted concerns using an individualized judgment model for superiors and management, means for transmitting and displaying the evaluation results to user terminals of the subordinates and content planning teams, and means for the subordinates and content planning teams to re-edit the project information and content planning information based on the evaluation results and upload it to a project management system and a content distribution system. This enables more efficient communication and faster decision-making within a company and in the content field.

[0138] "User interface" is a general term for the display devices and operating means that allow a user to input information and interact with a system.

[0139] "Project information" is a term that refers to detailed data and information about a project or business.

[0140] A "generative artificial intelligence model" is a machine learning algorithm that uses natural language processing to extract meaning and patterns from input data and generate responses.

[0141] "Concerns" are elements that could pose risks or problems in a project or content plan.

[0142] A "decision model" is an algorithm that has learned the decision-making process and values ​​of a specific manager or management.

[0143] "Evaluation results" refer to the results of the assessment of the importance of concerns and the priority of countermeasures using a judgment model.

[0144] "Terminal" means a device for accessing and operating the system through a user interface.

[0145] "Feedback" refers to information returned to the user, such as evaluation results or other responses from the system.

[0146] A "project management system" is software for planning, executing, and managing the progress of projects.

[0147] "Content planning information" refers to detailed plans and data regarding content production and distribution.

[0148] A "content distribution system" is a system for distributing digital content and providing it to viewers.

[0149] A "digital twin" is a virtual model of a physical object that is reflected in the digital world in real time.

[0150] The present invention relates to a system for improving communication efficiency and speeding up decision-making in the fields of corporate and content management. This system is realized through the following components and processes.

[0151] First, a user interface is provided for subordinates and content planning teams to input project information and content planning information. Through this interface, users can input information using their own devices (e.g., computers or smartphones). This information is then sent to the server.

[0152] The server then analyzes the received project information and content planning information. Specifically, it uses a generative artificial intelligence model (e.g., ChatGPT) to extract concerns about the project or content planning. The extracted concerns are elements that indicate project risks or potential problems.

[0153] Based on the extracted concerns, the server calls up individualized decision-making models for supervisors and management. These decision-making models learn the decision-making processes and values ​​of specific supervisors and management, and evaluate the priority of the concerns. The evaluation results provide the importance of the concerns and the priority of countermeasures.

[0154] The evaluation results are sent to the user devices of subordinates and the content planning team and displayed as feedback. This allows users to re-edit project information and content planning information based on the evaluation results. For example, if a "risk of going over budget" is pointed out, information can be added to review the budget. The re-edited information is uploaded to the project management system and content distribution system.

[0155] Additionally, the server tracks project progress and content delivery progress, notifying the digital twins of supervisors and management when necessary, allowing for swift action at points where supervisor or management intervention or review is required.

[0156] For example, when a subordinate enters a proposal for a new marketing campaign, the proposal is sent to a server. The server uses a generative artificial intelligence model to extract concerns such as "risk of going over budget" and "unclear target market." These concerns are evaluated using the supervisor's judgment model, resulting in an evaluation result that prioritizes, for example, "risk of going over budget." Based on this evaluation result, the subordinate can reorganize the budget and clarify the target market. The re-edited proposal is then uploaded to a project management system.

[0157] An example of a prompt might be, "Please extract the issues and concerns from the following content planning information: advertising budget is 500,000 yen, the main target is young people in their late 20s, and the results analysis needs to be completed by the end of March."

[0158] This system will improve communication efficiency within companies and in the content field, enabling faster and more optimal decision-making.

[0159] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0160] Step 1:

[0161] A user uses a terminal to input project information or content plan information. The input information is sent to the server through a user interface. The input data here is the details of a specific project or content plan.

[0162] Step 2:

[0163] The server analyzes the received case information or content planning information. It uses a generative AI model (e.g., ChatGPT) to extract relevant concerns from the input data. It uses prompts to identify concerns and list them. Data processing based on the input data involves analyzing the text and reading the concerns using natural language processing. The output is a list of concerns.

[0164] Step 3:

[0165] The server calls the supervisor's and management's individualized decision-making model based on the extracted concerns. The decision-making model is an algorithm that evaluates the importance and priority of the extracted concerns. The input data is a list of concerns, and the output is the evaluation results of the concerns. This evaluation reflects the decision-making process and values ​​of the specific supervisor or management.

[0166] Step 4:

[0167] The server sends the evaluation results to the user. The evaluation results are displayed as feedback on the user's device. The user re-edits the project information and content plan information based on the evaluation results. The input data is the evaluation results, and the output is the revised project information and content plan information.

[0168] Step 5:

[0169] The user sends a command to upload the modified project information or content planning information to the project management system or content distribution system. The server then uploads the modified information to the corresponding system. The input data is the modified information, and the output data is the new information stored in the project management system or content distribution system.

[0170] Step 6:

[0171] The server tracks the progress of the project and the progress of the content delivery. When necessary, it sends notifications to the digital twins of superiors and management. These notifications include important progress and issues in the project and content delivery. The input data is progress information, and the output is notification messages to the digital twins.

[0172] By following the above processing steps, the system can improve the efficiency of communication and control within a company and in the content field, and support quick and appropriate decision-making.

[0173] 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.

[0174] This invention relates to a system for streamlining communication between superiors and subordinates and for accelerating internal company case processing and decision-making. This system incorporates an emotion engine that recognizes the user's emotions, providing optimal feedback with even greater accuracy.

[0175] First, it provides a user interface for subordinates to input project information. Through this interface, subordinates can use their own devices to input detailed information about projects and tasks, including specific plans and issues. The input information is then sent from the device to the server.

[0176] The server then analyzes the received project information and uses a generative artificial intelligence model (e.g., ChatGPT) to extract project-related concerns, such as project risks and potential issues.

[0177] Furthermore, the server recognizes the user's emotions using an emotion engine, which analyzes the user's input, voice and facial expressions to identify the user's emotional state (e.g., stress, anger, satisfaction).

[0178] Based on the extracted concerns and the user's emotional information, the server invokes the supervisor's personalized judgment model. This judgment model learns the specific supervisor's decision-making process and values ​​and evaluates the case information based on the concerns. The evaluation results are adjusted according to the user's emotional state, allowing for more appropriate and personalized feedback.

[0179] The evaluation results are then sent to the subordinate's user device and displayed. This allows the subordinate to check the evaluation results and identify areas for improvement and risks. For example, if a risk of budget overrun is pointed out and the user is feeling stressed, the system will provide feedback including specific measures to mitigate the risk and the provision of support resources.

[0180] The subordinates re-edit the project information based on this feedback. The re-edited project information is sent back to the server from the terminal and uploaded to the project management system. The re-edited information and evaluation results are saved in the database.

[0181] Finally, the server tracks project progress and notifies the supervisor's digital twin when necessary, allowing the supervisor to intervene quickly. This process allows subordinates to work efficiently and receive appropriate support from their supervisor's virtual twin.

[0182] The processing flow will be explained below.

[0183] Step 1:

[0184] The user enters project information. The user uses the device's user interface to enter information about a new project. This input process includes project details, objectives, budget, etc. For example, a user might enter "Proposal for a new marketing campaign."

[0185] Step 2:

[0186] The terminal sends the case information entered by the user to the server. When the user presses the "Send" button, the case information is sent from the user terminal to the server via the network.

[0187] Step 3:

[0188] The server receives the case information. The server receives the entered information and stores it in an internal database. This information is used for subsequent processing.

[0189] Step 4:

[0190] The server uses a generative AI model to analyze project information and extract concerns. Project information is input into the generative AI model (e.g., ChatGPT), which extracts risks and issues related to the project. For example, "risk of exceeding budget" and "uncertain target market" are extracted.

[0191] Step 5:

[0192] The server recognizes the user's emotions using an emotion engine, which analyzes the facial expressions and voice data the user shows when inputting to identify the user's emotional state (e.g., stress, excitement, satisfaction).

[0193] Step 6:

[0194] The server calls the supervisor's personalized judgment model. The supervisor's judgment model evaluates the concerns using the extracted concerns and the recognized user's emotional information as input. For example, it may evaluate that the "risk of exceeding the budget" is high and therefore should be addressed as a priority.

[0195] Step 7:

[0196] The server sends the evaluation results to the subordinate's user device and displays them. The evaluation results are adjusted according to the user's emotional state and sent to the subordinate's device as optimal feedback. The device displays this and provides specific suggestions for improvement and risk countermeasures.

[0197] Step 8:

[0198] The device displays the received feedback, and the user can check the feedback on the device screen and understand any issues or suggestions for improvement.

[0199] Step 9:

[0200] Users can then re-edit the project information based on the feedback. For example, if they are told there is a high risk of going over budget, they can re-edit the budget plan and add information to clarify the target market details.

[0201] Step 10:

[0202] The terminal uploads the re-edited project information to the project management system, where the new re-edited project information is saved and available for other related departments to refer to.

[0203] Step 11:

[0204] The server tracks the progress of the project and notifies the supervisor's digital twin as needed. If there are any outstanding issues or progress on the project, the supervisor's digital twin is notified in real time, allowing appropriate action to be taken promptly.

[0205] This process allows subordinates to work on projects quickly and efficiently and receive appropriate support through their superiors' digital twins, improving communication and decision-making within the company and speeding up business progress.

[0206] Example 2

[0207] 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."

[0208] Traditional corporate communication systems make it difficult for superiors and subordinates to work together efficiently, often resulting in delays in project processing and decision-making. Another issue is that they are unable to properly grasp the emotional state of subordinates, making it difficult to provide appropriate feedback or support. This leads to inadequate understanding of project risk management and progress, resulting in reduced work efficiency.

[0209] 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.

[0210] In this invention, the server includes means for providing a user interface for a subordinate to input project information, means for receiving the input project information and extracting related concerns using a generative artificial intelligence model, means for analyzing the user's input content, voice data, or video and driving an emotion engine that identifies the user's emotional state, means for evaluating the project information based on the extracted concerns using the supervisor's personalized judgment model, means for transmitting and displaying the evaluation results to the subordinate's user terminal, and means for the subordinate to re-edit the project information based on the evaluation results and upload it to the project management system. This enables the provision of optimal feedback that takes the user's emotional state into consideration and the efficient management of project progress.

[0211] A "user interface" is a means of providing an operation screen or input form for subordinates to input case information.

[0212] "Project information" is data that includes detailed information about projects and tasks, specific plans, pending issues, and the like.

[0213] A "generative artificial intelligence model" refers to an artificial intelligence algorithm or system that analyzes input information and extracts relevant concerns.

[0214] "Concerns" refer to factors that require attention, such as project risks or potential problems.

[0215] An "emotion engine" refers to a system or algorithm that analyzes user input, audio data, or video to identify a user's emotional state.

[0216] "Emotional state" refers to the user's psychological state, such as stress, anger, or satisfaction.

[0217] A "decision model" refers to an individualized algorithm or system that learns a superior's decision-making process and values ​​and then evaluates case information.

[0218] "Evaluation results" refers to feedback generated based on the extracted concerns and the user's emotional state.

[0219] "User terminal" refers to devices such as computers and smartphones used by subordinates.

[0220] A "project management system" refers to a platform or software for managing project progress, tasks, and resources.

[0221] A "digital twin" is a system that acts as a virtual avatar of a manager, receiving real-time project progress and important notifications.

[0222] This invention relates to a system for streamlining communication between superiors and subordinates and for accelerating internal company case processing and decision-making. This system incorporates an emotion engine that recognizes the user's emotions, providing optimal feedback with even greater accuracy.

[0223] First, a user interface is provided for subordinates to input project information. Subordinates use their own devices to input detailed information about projects and tasks. Using this interface, they can easily input specific plans and issues. The input information is sent from the device to the server. The user interface is implemented as a web application that runs on a general web browser.

[0224] The server then analyzes the received project information and uses a generative artificial intelligence model (e.g., a GPT model) to extract project-related concerns. These concerns represent project risks and potential issues, such as the risk of budget overruns or schedule delays.

[0225] Additionally, the server recognizes the user's emotions using an emotion engine that analyzes user input, voice data, or camera footage to identify the user's emotional state (e.g., stress, anger, satisfaction). Specific software used for this emotion analysis includes Azure® Cognitive Services' facial recognition and voice emotion analysis APIs.

[0226] Based on the extracted concerns and the user's emotional information, the server invokes the supervisor's personalized judgment model. This judgment model learns the specific supervisor's decision-making process and values ​​and evaluates the case information based on the concerns. The evaluation results are adjusted according to the user's emotional state, providing more appropriate and personalized feedback.

[0227] The evaluation results are then sent to the subordinate's user device and displayed. The subordinate can review the evaluation results and identify areas for improvement and risks. For example, the system may point out the risk of exceeding the budget and suggest specific measures to mitigate the risk. As a concrete example, feedback is generated using prompt sentences such as the following:

[0228] Example prompt sentence:

[0229] "Project A's budget is 10% over schedule. I'd like some suggestions for improvement that take into account the current progress and a breakdown of how the budget is being used. I'm also feeling quite stressed about this situation."

[0230] The subordinates then re-edit the project information based on this feedback. The re-edited information is then sent back to the server from the terminal and uploaded to the project management system (e.g., Jira or Asana). This re-edited information and the evaluation results are then stored in the server's database.

[0231] Finally, the server tracks project progress and notifies the supervisor's digital twin of any significant changes or risks, allowing the supervisor to intervene quickly and provide the necessary support. This process ensures that subordinates can efficiently progress through projects and receive appropriate support from their supervisors.

[0232] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0233] Step 1:

[0234] A user uses their own device to input detailed project and work information into the user interface. This input includes specific plans and concerns. For example, they might input information such as, "Project A's budget is 5 million yen, and it is currently 80% complete. One concern is that it may exceed the budget." This information is sent from the device to the server (input: project information entered into the subordinate's device, output: project information sent to the server).

[0235] Step 2:

[0236] The server receives project information sent from the terminal. To analyze the received information, the server uses a generative artificial intelligence model (e.g., GPT model). This model extracts concerns related to the project. For example, concerns such as "possibility of budget overruns" and "progress delays" are automatically extracted from the input information (Input: Project information sent from the terminal, Output: Extracted concerns).

[0237] Step 3:

[0238] The server uses an emotion engine to recognize the user's emotions. The emotion engine analyzes the user's input (text data), voice data, or camera footage to identify the user's emotional state (e.g., stress, anger, satisfaction). For example, it detects that the user is "feeling stressed" from the input text or voice tone (input: user's text data, voice data, video; output: identified emotional state).

[0239] Step 4:

[0240] The server calls the manager's personalized decision-making model based on the extracted concerns and the user's recognized emotional state. This model learns the decision-making process and values ​​of the specific manager and makes an evaluation based on the extracted concerns. For example, it may make an evaluation such as, "There is a high risk of exceeding the budget, but the user is feeling stressed, so we will propose specific countermeasures" (Input: extracted concerns, identified emotional state, Output: evaluation result).

[0241] Step 5:

[0242] The server sends the generated evaluation results to the subordinate's user device. The device displays the evaluation results, which the subordinate can then review. Based on the evaluation results, the subordinate can identify areas for improvement and risks. For example, a specific suggestion such as "There is a high risk of exceeding the budget, so urgent cost-cutting measures are required" may be displayed (Input: Evaluation results, Output: Evaluation results displayed on the subordinate's device).

[0243] Step 6:

[0244] The subordinate re-edits the project information based on the feedback. The re-edited information is sent back to the server from the subordinate's device. The re-edited project information is uploaded to a project management system (e.g., Jira or Asana) and saved in a database. For example, information such as "A new budget adjustment plan has been created and the progress schedule has been revised" is re-edited (Input: Re-edited project information by subordinate, Output: Re-edited project information sent to the server).

[0245] Step 7:

[0246] The server tracks the progress of the project. If any important changes or risks occur, it notifies the supervisor's digital twin. This allows the supervisor to intervene quickly and provide the necessary support. For example, a notification may be sent to the supervisor's digital twin saying, "The budget adjustments were successful, but a new risk has emerged that requires action." (Input: Project progress information; Output: Notification sent to the supervisor's digital twin).

[0247] (Application example 2)

[0248] 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."

[0249] In modern factories, improving production line efficiency and responding quickly to maintenance work are extremely important, but insufficient sharing of relevant information and delayed decision-making are problems. Furthermore, insufficient communication between workers and managers can prevent appropriate support and feedback, leading to reduced productivity. Given this background, there is a need for a system that can improve the work environment and production efficiency by recognizing emotional states and providing personalized feedback.

[0250] 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.

[0251] In this invention, the server includes means for providing a user interface for a subordinate to input project information, means for receiving the input project information and extracting related concerns using a generative artificial intelligence model, means for using an emotion engine to recognize the worker's emotional state, means for evaluating the project information based on the extracted concerns and emotional information using a supervisor's personalized judgment model, means for sending and displaying the evaluation results to the subordinate's user terminal, and means for the subordinate to re-edit the project information based on the evaluation results and upload it to the project management system. This enables efficient communication between workers and managers, enables rapid problem detection and resolution, and improves production efficiency.

[0252] "Subordinates" refers to employees who work in the factory.

[0253] "Project information" refers to information including specific plans and pending issues related to production lines and maintenance operations.

[0254] "User interface" refers to the screens and operating means provided as a means for subordinates to input project information.

[0255] "Generative artificial intelligence model" refers to a system that uses a generative AI model to extract relevant concerns from input information.

[0256] "Related concerns" refer to the risks and potential problems of the project.

[0257] The "emotion engine" refers to a system that recognizes the emotional state of workers by analyzing voice, facial expressions, input, etc.

[0258] An "individualized judgment model" refers to a system that learns the decision-making process and values ​​of a specific supervisor and evaluates case information based on extracted concerns and emotional information.

[0259] "Evaluation results" refers to feedback and improvement suggestions generated as a result of evaluating case information based on concerns and emotional information.

[0260] "User terminal" refers to the computers and mobile devices used by subordinates.

[0261] A "project management system" refers to a system for managing project information and evaluation results.

[0262] "Database" refers to an electronic data storage system for storing evaluation results and project information.

[0263] A "digital twin" is a system that acts as a virtual avatar of a manager, tracking project progress and intervening as needed.

[0264] A system for implementing the present invention incorporates a series of means for improving work efficiency, and the system uses a user interface, a generative artificial intelligence model, an emotion engine, an individualized decision model, a database, a project management system, and a digital twin.

[0265] First, the server provides a user interface for subordinates to input project information. This interface allows workers to enter detailed information about the status of the production line and maintenance work. For example, a worker might enter, "The production line is running slowly and is likely to exceed its budget."

[0266] The entered project information is then sent from the user's device to a server. The server uses a generative AI model to extract relevant concerns from the project information. This generative AI model uses OpenAI (registered trademark) APIs and ChatGPT. During this process, the server identifies project risks and potential issues from the submitted text.

[0267] Furthermore, the server uses an emotion engine to recognize the worker's emotional state. The emotion engine analyzes the worker's input, voice, facial expressions, etc. to identify emotional states such as stress or satisfaction. For example, the server may recognize the worker's input as indicating stress.

[0268] The server then invokes the supervisor's personalized decision-making model based on the extracted concerns and emotional information. This decision-making model, which has learned the specific supervisor's decision-making process and values, evaluates the case information. The evaluation results are sent to the user's device and displayed to the worker. Specific feedback is provided, such as, "Consider how to reallocate your team and resources to optimize the speed of the production line. We also recommend that you consider new tools for budget management."

[0269] Based on this feedback, the worker re-edits the project information and sends it back to the server from the user's device. This re-edited information is then uploaded to the project management system.

[0270] The evaluation results and re-edited case information are stored in a database, and the server tracks the project's progress. When necessary, the server notifies the supervisor's digital twin, which acts as the supervisor's virtual avatar and allows for quick intervention in the project.

[0271] The following sentences are used as examples of prompt sentences:

[0272] Text format:

[0273] "Please extract the concerns or problems from the following text: Our production line is slow and we are on the verge of going over budget."

[0274] "Please provide feedback based on your feelings of stress based on the following concerns: The production line is slow and we're on the verge of going over budget."

[0275] This allows for efficient communication between workers and managers, enabling problems to be detected and resolved quickly, improving production efficiency.

[0276] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0277] Step 1:

[0278] A user interface is used by subordinates to input job information. Workers enter detailed information about the production line and maintenance tasks through user terminals. This input text is saved for later processing. Inputs include specific job information such as, "The production line is running slowly and is on the verge of exceeding budget."

[0279] Step 2:

[0280] The input project information is sent from the user's device to a server. The server temporarily stores the received information and uses a generative artificial intelligence model to extract relevant concerns from the project information. Specifically, it uses OpenAI's API and ChatGPT to analyze the input text and identify potential problems and risks. The output generates concerns such as "slowdown in production line speed" and "risk of budget overruns."

[0281] Step 3:

[0282] After extracting concerns, the server uses an emotion engine to recognize the worker's emotional state. It analyzes the user's input, voice, facial expressions, etc. to identify the emotional state. For example, it identifies emotions such as "stress" or "anxiety" from the input text. The input is the text sent in the previous step, and emotional information is generated as the output.

[0283] Step 4:

[0284] The server calls up the supervisor's personalized judgment model based on the extracted concerns and emotional information. This judgment model has learned the decision-making process and values ​​of the specific supervisor and evaluates the case information. The generative AI model is then used again for evaluation. For example, the supervisor's judgment model is used to generate "specific proposals for optimizing the speed of the production line." The input is the concerns and emotional information, and the output is the evaluation result.

[0285] Step 5:

[0286] The evaluation results are sent to the user's terminal. Specific feedback displayed on the subordinate's terminal may include, "Consider how to reallocate resources to optimize the speed of the production line." This allows the worker to identify areas for improvement or risks. The input is the evaluation results, and the output is feedback.

[0287] Step 6:

[0288] The subordinate re-edits the project information based on the feedback they receive. This re-edited information is then sent back to the server from the user's device. The server then uploads the re-edited information to the project management system and stores it in the database. The input is the re-edited project information, and the output is the updated project information stored in the database.

[0289] Step 7:

[0290] The server tracks the progress of the project. Based on the information stored in the project management system, it monitors progress in real time and sends notifications to the manager's digital twin as needed. This digital twin acts as the manager's virtual avatar, enabling quick intervention in the project. The input is project progress data, and the output is notifications.

[0291] 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.

[0292] 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 (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0293] 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.

[0294] [Second embodiment]

[0295] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0296] 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.

[0297] 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).

[0298] 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.

[0299] 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.

[0300] 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).

[0301] 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.

[0302] 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.

[0303] 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.

[0304] 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.

[0305] 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.

[0306] 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."

[0307] The present invention provides a system for improving the efficiency of communication between superiors and subordinates and for facilitating the processing of cases and decision-making within a company. This system is realized through the following components and processes.

[0308] First, a user interface is provided for subordinates to input project information. Through this interface, subordinates can input detailed information about projects and tasks using their own devices (e.g., computers or smartphones). This information is then sent to the server.

[0309] The server then analyzes the received project information. First, it uses a generative AI model (e.g., ChatGPT) to extract concerns about the project. These concerns indicate risks and potential problems for the project.

[0310] Based on the extracted concerns, the server calls up the supervisor's personalized decision-making model. This decision-making model learns the decision-making process and values ​​of the specific supervisor and evaluates the priority of the concerns. The evaluation results provide the importance of the concerns and the priority of countermeasures.

[0311] The evaluation results are sent to the subordinate's user device and displayed as feedback. This allows the subordinate to re-edit the project information based on the evaluation results. For example, if a risk of exceeding the budget is pointed out, the subordinate can add information to review the budget. The re-edited project information is then uploaded to the project management system.

[0312] Additionally, the server tracks project progress and notifies the supervisor's digital twin when necessary, allowing for quick action at points where supervisor intervention or confirmation is required.

[0313] As a concrete example, when a subordinate inputs a proposal for a new marketing campaign into the system, the proposal is sent to the server. The server uses a generative artificial intelligence model to extract concerns such as "risk of going over budget" and "unclear target market." These concerns are evaluated using the supervisor's judgment model, resulting in an evaluation result that prioritizes "risk of going over budget." Based on this result, the subordinate can reorganize the budget and clarify the target market. The re-edited proposal is then uploaded to the project management system.

[0314] This system allows the supervisor's digital twin to function effectively, allowing projects to progress quickly without the supervisor's direct intervention, resulting in more efficient communication within the company and improving the speed at which business is promoted.

[0315] The processing flow will be explained below.

[0316] Step 1:

[0317] The user inputs project information. Using the user interface of the device, the user inputs detailed project information, such as a proposal for a new marketing campaign. This input information is collected by the device as text data.

[0318] Step 2:

[0319] The terminal sends the case information entered by the user to the server. When the user presses the "Send" button, the case information is sent to the server via the Internet or a network.

[0320] Step 3:

[0321] The server receives the case information. The server stores the received case information in storage. The stored information includes the case content and user identification information.

[0322] Step 4:

[0323] The server calls a generative AI model and analyzes the received project information. The generative AI model (e.g., ChatGPT) is used to extract project-related concerns (e.g., "risk of exceeding budget" or "unclear target market").

[0324] Step 5:

[0325] The server calls up the supervisor's personalized decision-making model and evaluates the extracted concerns. The decision-making model, which has learned the supervisor's decision-making process and values, evaluates the priority of the concerns (for example, the risk of exceeding the budget is the top priority).

[0326] Step 6:

[0327] The server sends the evaluation results to the subordinate's terminal, where they are displayed as feedback.

[0328] Step 7:

[0329] The device displays the received feedback, allowing users to see the priority and rating of concerns on the device screen.

[0330] Step 8:

[0331] Users can re-edit the project information based on the feedback. For example, if they are told that there is a high risk of going over budget, they can review the budget plan and add more details.

[0332] Step 9:

[0333] The terminal uploads the re-edited project information to the project management system, and the re-edited new project information is saved in the project management system.

[0334] Step 10:

[0335] The server tracks the progress of the project and notifies the supervisor's digital twin as needed, so that specific progress and issues are shared with the supervisor's digital twin in real time, allowing necessary action to be taken promptly.

[0336] This process allows the boss's digital twin to function effectively, enabling subordinates to move projects forward quickly and efficiently.

[0337] Example 1

[0338] 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."

[0339] In modern companies, efficient communication and decision-making between superiors and subordinates is extremely important. Especially when large-scale projects or multiple projects are underway simultaneously, it can be difficult for superiors to accurately grasp the details of all projects and make decisions quickly. Furthermore, delays in reporting and feedback from subordinates to superiors can sometimes delay the progress of a project. There is a need for a system that can eliminate these inefficient communications and support rapid and accurate decision-making.

[0340] 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.

[0341] In this invention, the server includes means for providing a user interface for a subordinate to input project information, means for receiving the input project information and extracting related concerns using a generative artificial intelligence model, means for evaluating the project information based on the extracted concerns using the supervisor's personalized judgment model, means for sending and displaying the evaluation results to the subordinate's user terminal, means for the subordinate to re-edit the project information based on the evaluation results and upload it to a project management system, and means for tracking project progress and notifying the supervisor's digital twin as necessary. This enables rapid reception and analysis of project information from subordinates, efficient feedback to the supervisor, real-time tracking of project progress, and timely notification to the supervisor's digital twin.

[0342] "Project information" refers to detailed information about a project or task, specifically including the project name, summary, goals, deadline, budget, etc.

[0343] A "user interface" is a means by which a user directly interacts with a computer system, and primarily includes graphical user interfaces and web interfaces.

[0344] A "server" refers to a computer system that provides data or services in response to requests from clients.

[0345] A "generative artificial intelligence model" refers to an AI model that has the ability to generate new information or patterns from given data; an example is a natural language generation model.

[0346] "Concerns" are elements that indicate risks or potential problems with the project.

[0347] An "individualized boss judgment model" refers to an AI model that learns the decision-making process and values ​​of a specific boss and makes evaluations based on that.

[0348] "Evaluation results" refer to the analysis results and recommendations obtained using generative artificial intelligence models and judgment models.

[0349] A "user terminal" is a device that is directly operated by a user, and examples include computers and smartphones.

[0350] "Project management system" refers to a software tool used to manage the planning, execution, monitoring, and closure of projects.

[0351] "Project progress" refers to information showing the progress of a project and its current achievement status.

[0352] "Digital twin" refers to a digital representation of a physical entity, and in this context refers to an AI agent that mimics the decisions and actions of its superior.

[0353] This invention is a system for improving communication between superiors and subordinates and for accelerating case processing and decision-making within a company. This system is realized using subordinate terminals, a server, and a generative artificial intelligence model.

[0354] First, the subordinate uses his / her own device (e.g., a computer or smartphone) to enter project information. The user interface for this input is provided through a dedicated application or web browser. The subordinate enters details such as the project name, summary, goals, deadline, and budget into the form and clicks the "Submit" button, which sends the information to the server.

[0355] The server stores the received case information in a database and uses a generative artificial intelligence model (e.g., ChatGPT) to extract concerns related to the case. Concern extraction involves inputting the case information into the ChatGPT API and obtaining the extracted results in text format. For example, the prompt could be, "Please extract potential risks and issues from the given marketing campaign proposal."

[0356] Next, the server applies the supervisor's personalized decision model (e.g., TensorFlow model) to the extracted concerns and evaluates their importance. Here, the server loads the decision model and inputs the concern data to obtain the evaluation results. For example, a prompt request such as "Please evaluate the importance of this risk using your supervisor's decision model" can be used.

[0357] The evaluation results are sent from the server to the subordinate's device and displayed on the user interface. The subordinate then re-edits the project information based on the feedback and re-uploads it to the project management system. For example, if a risk of going over budget is pointed out, the subordinate will review the budget and enter additional information.

[0358] The progress of the project is continuously tracked by the server, which periodically retrieves and updates status information using the project management system's APIs. If necessary, the server also sends notifications to the supervisor's digital twin. If a notification is deemed necessary, the notification is sent via email or a chat tool (e.g., Slack).

[0359] Through this process, the system can quickly advance projects without direct intervention from superiors, improving communication efficiency within the company and speeding up decision-making.

[0360] As a concrete example, when a subordinate enters a proposal for a new marketing campaign into the system, the proposal is sent to the server. The server uses a generative artificial intelligence model to extract concerns such as "risk of going over budget" and "unclear target market." These concerns are evaluated using the supervisor's judgment model, resulting in an evaluation result that prioritizes "risk of going over budget." Based on this result, the subordinate reorganizes the budget and clarifies the target market. This revised proposal is uploaded to a project management system, and the server tracks progress and sends notifications to the supervisor's digital twin as needed. As a result, communication within the company becomes more efficient and business progress is accelerated.

[0361] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0362] Step 1:

[0363] The subordinate, who is the user, enters project information using a user interface on his or her own device. This user interface is provided through a dedicated application or a web browser. For example, the subordinate enters details such as the project name, summary, goals, deadline, and budget into a form and clicks the "Submit" button.

[0364] Input: Project information (project name, summary, goals, deadline, budget, etc.)

[0365] Output: Data sent from the terminal to the server (JSON format job information)

[0366] Step 2:

[0367] The terminal sends the input job information to the server in real time using an HTTP request.

[0368] Input: Data sent from the device (JSON format job information)

[0369] Output: Data received by the server (JSON format job information)

[0370] Step 3:

[0371] The server stores the received job information in a database and starts the analysis process. The server inserts the job information into the database and then adds it to a queue for the analysis process.

[0372] Input: Data received from the server (JSON format job information)

[0373] Output: Case information saved in the database

[0374] Step 4:

[0375] The server uses a generative artificial intelligence model (e.g., ChatGPT) to extract concerns from the case information. To extract concerns, the server passes the case information as input to the ChatGPT API and obtains the extracted results in text format.

[0376] Input: Case information stored in the database

[0377] Output: Extracted concerns (text format)

[0378] Step 5:

[0379] The server applies the supervisor's personalized judgment model (e.g., TensorFlow model) to the extracted concerns and evaluates their importance. The server loads the judgment model and inputs the concern data to obtain the evaluation results.

[0380] Input: Extracted concerns (text format)

[0381] Output: Evaluation results (importance of concerns and priority of countermeasures, in text format)

[0382] Step 6:

[0383] The server sends the evaluation results to the subordinate's device and displays them as feedback.The server sends the evaluation results in JSON format to the device, and the device displays the results in the user interface.

[0384] Input: Evaluation results (importance of concerns and priority of countermeasures, in text format)

[0385] Output: Evaluation results displayed on the user's device (JSON format)

[0386] Step 7:

[0387] The subordinate, who is the user, re-edits the project information based on the feedback and re-uploads it to the project management system. The subordinate enters the re-edited information and clicks the "Submit" button again to upload it to the project management system.

[0388] Input: Re-edited project information (JSON format)

[0389] Output: Project information stored in the project management system

[0390] Step 8:

[0391] The server continuously tracks the progress of the project through the project management system, periodically calling the project management system's APIs to retrieve and update status information.

[0392] Input: Project management system status information

[0393] Output: Progress data stored on the server

[0394] Step 9:

[0395] If the server determines that a notification should be sent to the supervisor's digital twin based on the project's progress, it will send the notification via email or chat tool.

[0396] Input: Progress data stored on the server

[0397] Output: Notification sent to the manager's digital twin (via email or chat tool)

[0398] The above processing steps enable quick receipt and analysis of project information from subordinates, efficient feedback to superiors, real-time tracking of project progress, and timely notification to the superior's digital twin.

[0399] (Application example 1)

[0400] 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."

[0401] Conventional systems for handling and deciding on projects within companies are prone to lack of communication between subordinates and superiors and delays in the transmission of information, making it difficult to speed up decision-making and efficiently handle projects. Similar communication problems also arise in the field of content planning and distribution between planning teams and management teams. As a result, content production and distribution are frequently delayed. It is necessary to solve these issues and achieve more efficient communication and faster decision-making within companies and in the content field.

[0402] 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.

[0403] In this invention, the server includes means for providing a user interface for subordinates and content planning teams to input project information and content planning information, means for receiving the input project information and content planning information and extracting related concerns using a generative artificial intelligence model, means for evaluating the project information and content planning information based on the extracted concerns using an individualized judgment model for superiors and management, means for transmitting and displaying the evaluation results to user terminals of the subordinates and content planning teams, and means for the subordinates and content planning teams to re-edit the project information and content planning information based on the evaluation results and upload it to a project management system and a content distribution system. This enables more efficient communication and faster decision-making within a company and in the content field.

[0404] "User interface" is a general term for the display devices and operating means that allow a user to input information and interact with a system.

[0405] "Project information" is a term that refers to detailed data and information about a project or business.

[0406] A "generative artificial intelligence model" is a machine learning algorithm that uses natural language processing to extract meaning and patterns from input data and generate responses.

[0407] "Concerns" are elements that could pose risks or problems in a project or content plan.

[0408] A "decision model" is an algorithm that has learned the decision-making process and values ​​of a specific manager or management.

[0409] "Evaluation results" refer to the results of the assessment of the importance of concerns and the priority of countermeasures using a judgment model.

[0410] "Terminal" means a device for accessing and operating the system through a user interface.

[0411] "Feedback" refers to information returned to the user, such as evaluation results or other responses from the system.

[0412] A "project management system" is software for planning, executing, and managing the progress of projects.

[0413] "Content planning information" refers to detailed plans and data regarding content production and distribution.

[0414] A "content distribution system" is a system for distributing digital content and providing it to viewers.

[0415] A "digital twin" is a virtual model of a physical object that is reflected in the digital world in real time.

[0416] The present invention relates to a system for improving communication efficiency and speeding up decision-making in the fields of corporate and content management. This system is realized through the following components and processes.

[0417] First, a user interface is provided for subordinates and content planning teams to input project information and content planning information. Through this interface, users can input information using their own devices (e.g., computers or smartphones). This information is then sent to the server.

[0418] The server then analyzes the received project information and content planning information. Specifically, it uses a generative artificial intelligence model (e.g., ChatGPT) to extract concerns about the project or content planning. The extracted concerns are elements that indicate project risks or potential problems.

[0419] Based on the extracted concerns, the server calls up individualized decision-making models for supervisors and management. These decision-making models learn the decision-making processes and values ​​of specific supervisors and management, and evaluate the priority of the concerns. The evaluation results provide the importance of the concerns and the priority of countermeasures.

[0420] The evaluation results are sent to the user devices of subordinates and the content planning team and displayed as feedback. This allows users to re-edit project information and content planning information based on the evaluation results. For example, if a "risk of going over budget" is pointed out, information can be added to review the budget. The re-edited information is uploaded to the project management system and content distribution system.

[0421] Additionally, the server tracks project progress and content delivery progress, notifying the digital twins of supervisors and management when necessary, allowing for swift action at points where supervisor or management intervention or review is required.

[0422] For example, when a subordinate enters a proposal for a new marketing campaign, the proposal is sent to a server. The server uses a generative artificial intelligence model to extract concerns such as "risk of going over budget" and "unclear target market." These concerns are evaluated using the supervisor's judgment model, resulting in an evaluation result that prioritizes, for example, "risk of going over budget." Based on this evaluation result, the subordinate can reorganize the budget and clarify the target market. The re-edited proposal is then uploaded to a project management system.

[0423] An example of a prompt might be, "Please extract the issues and concerns from the following content planning information: advertising budget is 500,000 yen, the main target is young people in their late 20s, and the results analysis needs to be completed by the end of March."

[0424] This system will improve communication efficiency within companies and in the content field, enabling faster and more optimal decision-making.

[0425] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0426] Step 1:

[0427] A user uses a terminal to input project information or content plan information. The input information is sent to the server through a user interface. The input data here is the details of a specific project or content plan.

[0428] Step 2:

[0429] The server analyzes the received case information or content planning information. It uses a generative AI model (e.g., ChatGPT) to extract relevant concerns from the input data. It uses prompts to identify concerns and list them. Data processing based on the input data involves analyzing the text and reading the concerns using natural language processing. The output is a list of concerns.

[0430] Step 3:

[0431] The server calls the supervisor's and management's individualized decision-making model based on the extracted concerns. The decision-making model is an algorithm that evaluates the importance and priority of the extracted concerns. The input data is a list of concerns, and the output is the evaluation results of the concerns. This evaluation reflects the decision-making process and values ​​of the specific supervisor or management.

[0432] Step 4:

[0433] The server sends the evaluation results to the user. The evaluation results are displayed as feedback on the user's device. The user re-edits the project information and content plan information based on the evaluation results. The input data is the evaluation results, and the output is the revised project information and content plan information.

[0434] Step 5:

[0435] The user sends a command to upload the modified project information or content planning information to the project management system or content distribution system. The server then uploads the modified information to the corresponding system. The input data is the modified information, and the output data is the new information stored in the project management system or content distribution system.

[0436] Step 6:

[0437] The server tracks the progress of the project and the progress of the content delivery. When necessary, it sends notifications to the digital twins of superiors and management. These notifications include important progress and issues in the project and content delivery. The input data is progress information, and the output is notification messages to the digital twins.

[0438] By following the above processing steps, the system can improve the efficiency of communication and control within a company and in the content field, and support quick and appropriate decision-making.

[0439] 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.

[0440] This invention relates to a system for streamlining communication between superiors and subordinates and for accelerating internal company case processing and decision-making. This system incorporates an emotion engine that recognizes the user's emotions, providing optimal feedback with even greater accuracy.

[0441] First, it provides a user interface for subordinates to input project information. Through this interface, subordinates can use their own devices to input detailed information about projects and tasks, including specific plans and issues. The input information is then sent from the device to the server.

[0442] The server then analyzes the received project information and uses a generative artificial intelligence model (e.g., ChatGPT) to extract project-related concerns, such as project risks and potential issues.

[0443] Furthermore, the server recognizes the user's emotions using an emotion engine, which analyzes the user's input, voice and facial expressions to identify the user's emotional state (e.g., stress, anger, satisfaction).

[0444] Based on the extracted concerns and the user's emotional information, the server invokes the supervisor's personalized judgment model. This judgment model learns the specific supervisor's decision-making process and values ​​and evaluates the case information based on the concerns. The evaluation results are adjusted according to the user's emotional state, allowing for more appropriate and personalized feedback.

[0445] The evaluation results are then sent to the subordinate's user device and displayed. This allows the subordinate to check the evaluation results and identify areas for improvement and risks. For example, if a risk of budget overrun is pointed out and the user is feeling stressed, the system will provide feedback including specific measures to mitigate the risk and the provision of support resources.

[0446] The subordinates re-edit the project information based on this feedback. The re-edited project information is sent back to the server from the terminal and uploaded to the project management system. The re-edited information and evaluation results are saved in the database.

[0447] Finally, the server tracks project progress and notifies the supervisor's digital twin when necessary, allowing the supervisor to intervene quickly. This process allows subordinates to work efficiently and receive appropriate support from their supervisor's virtual twin.

[0448] The processing flow will be explained below.

[0449] Step 1:

[0450] The user enters project information. The user uses the device's user interface to enter information about a new project. This input process includes project details, objectives, budget, etc. For example, a user might enter "Proposal for a new marketing campaign."

[0451] Step 2:

[0452] The terminal sends the case information entered by the user to the server. When the user presses the "Send" button, the case information is sent from the user terminal to the server via the network.

[0453] Step 3:

[0454] The server receives the case information. The server receives the entered information and stores it in an internal database. This information is used for subsequent processing.

[0455] Step 4:

[0456] The server uses a generative AI model to analyze project information and extract concerns. Project information is input into the generative AI model (e.g., ChatGPT), which extracts risks and issues related to the project. For example, "risk of exceeding budget" and "uncertain target market" are extracted.

[0457] Step 5:

[0458] The server recognizes the user's emotions using an emotion engine, which analyzes the facial expressions and voice data the user shows when inputting to identify the user's emotional state (e.g., stress, excitement, satisfaction).

[0459] Step 6:

[0460] The server calls the supervisor's personalized judgment model. The supervisor's judgment model evaluates the concerns using the extracted concerns and the recognized user's emotional information as input. For example, it may evaluate that the "risk of exceeding the budget" is high and therefore should be addressed as a priority.

[0461] Step 7:

[0462] The server sends the evaluation results to the subordinate's user device and displays them. The evaluation results are adjusted according to the user's emotional state and sent to the subordinate's device as optimal feedback. The device displays this and provides specific suggestions for improvement and risk countermeasures.

[0463] Step 8:

[0464] The device displays the received feedback, and the user can check the feedback on the device screen and understand any issues or suggestions for improvement.

[0465] Step 9:

[0466] Users can then re-edit the project information based on the feedback. For example, if they are told there is a high risk of going over budget, they can re-edit the budget plan and add information to clarify the target market details.

[0467] Step 10:

[0468] The terminal uploads the re-edited project information to the project management system, where the new re-edited project information is saved and available for other related departments to refer to.

[0469] Step 11:

[0470] The server tracks the progress of the project and notifies the supervisor's digital twin as needed. If there are any outstanding issues or progress on the project, the supervisor's digital twin is notified in real time, allowing appropriate action to be taken promptly.

[0471] This process allows subordinates to work on projects quickly and efficiently and receive appropriate support through their superiors' digital twins, improving communication and decision-making within the company and speeding up business progress.

[0472] Example 2

[0473] 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."

[0474] Traditional corporate communication systems make it difficult for superiors and subordinates to work together efficiently, often resulting in delays in project processing and decision-making. Another issue is that they are unable to properly grasp the emotional state of subordinates, making it difficult to provide appropriate feedback or support. This leads to inadequate understanding of project risk management and progress, resulting in reduced work efficiency.

[0475] 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.

[0476] In this invention, the server includes means for providing a user interface for a subordinate to input project information, means for receiving the input project information and extracting related concerns using a generative artificial intelligence model, means for analyzing the user's input content, voice data, or video and driving an emotion engine that identifies the user's emotional state, means for evaluating the project information based on the extracted concerns using the supervisor's personalized judgment model, means for transmitting and displaying the evaluation results to the subordinate's user terminal, and means for the subordinate to re-edit the project information based on the evaluation results and upload it to the project management system. This enables the provision of optimal feedback that takes the user's emotional state into consideration and the efficient management of project progress.

[0477] A "user interface" is a means of providing an operation screen or input form for subordinates to input case information.

[0478] "Project information" is data that includes detailed information about projects and tasks, specific plans, pending issues, and the like.

[0479] A "generative artificial intelligence model" refers to an artificial intelligence algorithm or system that analyzes input information and extracts relevant concerns.

[0480] "Concerns" refer to factors that require attention, such as project risks or potential problems.

[0481] An "emotion engine" refers to a system or algorithm that analyzes user input, audio data, or video to identify a user's emotional state.

[0482] "Emotional state" refers to the user's psychological state, such as stress, anger, or satisfaction.

[0483] A "decision model" refers to an individualized algorithm or system that learns a superior's decision-making process and values ​​and then evaluates case information.

[0484] "Evaluation results" refers to feedback generated based on the extracted concerns and the user's emotional state.

[0485] "User terminal" refers to devices such as computers and smartphones used by subordinates.

[0486] A "project management system" refers to a platform or software for managing project progress, tasks, and resources.

[0487] A "digital twin" is a system that acts as a virtual avatar of a manager, receiving real-time project progress and important notifications.

[0488] This invention relates to a system for streamlining communication between superiors and subordinates and for accelerating internal company case processing and decision-making. This system incorporates an emotion engine that recognizes the user's emotions, providing optimal feedback with even greater accuracy.

[0489] First, a user interface is provided for subordinates to input project information. Subordinates use their own devices to input detailed information about projects and tasks. Using this interface, they can easily input specific plans and issues. The input information is sent from the device to the server. The user interface is implemented as a web application that runs on a general web browser.

[0490] The server then analyzes the received project information and uses a generative artificial intelligence model (e.g., a GPT model) to extract project-related concerns. These concerns represent project risks and potential issues, such as the risk of budget overruns or schedule delays.

[0491] Additionally, the server uses an emotion engine to recognize the user's emotions. The emotion engine analyzes user input, voice data, or camera footage to identify the user's emotional state (e.g., stress, anger, satisfaction). The specific software used for this emotion analysis includes Azure Cognitive Services' facial recognition and voice emotion analysis APIs.

[0492] Based on the extracted concerns and the user's emotional information, the server invokes the supervisor's personalized judgment model. This judgment model learns the specific supervisor's decision-making process and values ​​and evaluates the case information based on the concerns. The evaluation results are adjusted according to the user's emotional state, providing more appropriate and personalized feedback.

[0493] The evaluation results are then sent to the subordinate's user device and displayed. The subordinate can review the evaluation results and identify areas for improvement and risks. For example, the system may point out the risk of exceeding the budget and suggest specific measures to mitigate the risk. As a concrete example, feedback is generated using prompt sentences such as the following:

[0494] Example prompt sentence:

[0495] "Project A's budget is 10% over schedule. I'd like some suggestions for improvement that take into account the current progress and a breakdown of how the budget is being used. I'm also feeling quite stressed about this situation."

[0496] The subordinates then re-edit the project information based on this feedback. The re-edited information is then sent back to the server from the terminal and uploaded to the project management system (e.g., Jira or Asana). This re-edited information and the evaluation results are then stored in the server's database.

[0497] Finally, the server tracks project progress and notifies the supervisor's digital twin of any significant changes or risks, allowing the supervisor to intervene quickly and provide the necessary support. This process ensures that subordinates can efficiently progress through projects and receive appropriate support from their supervisors.

[0498] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0499] Step 1:

[0500] A user uses their own device to input detailed project and work information into the user interface. This input includes specific plans and concerns. For example, they might input information such as, "Project A's budget is 5 million yen, and it is currently 80% complete. One concern is that it may exceed the budget." This information is sent from the device to the server (input: project information entered into the subordinate's device, output: project information sent to the server).

[0501] Step 2:

[0502] The server receives project information sent from the terminal. To analyze the received information, the server uses a generative artificial intelligence model (e.g., GPT model). This model extracts concerns related to the project. For example, concerns such as "possibility of budget overruns" and "progress delays" are automatically extracted from the input information (Input: Project information sent from the terminal, Output: Extracted concerns).

[0503] Step 3:

[0504] The server uses an emotion engine to recognize the user's emotions. The emotion engine analyzes the user's input (text data), voice data, or camera footage to identify the user's emotional state (e.g., stress, anger, satisfaction). For example, it detects that the user is "feeling stressed" from the input text or voice tone (input: user's text data, voice data, video; output: identified emotional state).

[0505] Step 4:

[0506] The server calls the manager's personalized decision-making model based on the extracted concerns and the user's recognized emotional state. This model learns the decision-making process and values ​​of the specific manager and makes an evaluation based on the extracted concerns. For example, it may make an evaluation such as, "There is a high risk of exceeding the budget, but the user is feeling stressed, so we will propose specific countermeasures" (Input: extracted concerns, identified emotional state, Output: evaluation result).

[0507] Step 5:

[0508] The server sends the generated evaluation results to the subordinate's user device. The device displays the evaluation results, which the subordinate can then review. Based on the evaluation results, the subordinate can identify areas for improvement and risks. For example, a specific suggestion such as "There is a high risk of exceeding the budget, so urgent cost-cutting measures are required" may be displayed (Input: Evaluation results, Output: Evaluation results displayed on the subordinate's device).

[0509] Step 6:

[0510] The subordinate re-edits the project information based on the feedback. The re-edited information is sent back to the server from the subordinate's device. The re-edited project information is uploaded to a project management system (e.g., Jira or Asana) and saved in a database. For example, information such as "A new budget adjustment plan has been created and the progress schedule has been revised" is re-edited (Input: Re-edited project information by subordinate, Output: Re-edited project information sent to the server).

[0511] Step 7:

[0512] The server tracks the progress of the project. If any important changes or risks occur, it notifies the supervisor's digital twin. This allows the supervisor to intervene quickly and provide the necessary support. For example, a notification may be sent to the supervisor's digital twin saying, "The budget adjustments were successful, but a new risk has emerged that requires action." (Input: Project progress information; Output: Notification sent to the supervisor's digital twin).

[0513] (Application example 2)

[0514] 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."

[0515] In modern factories, improving production line efficiency and responding quickly to maintenance work are extremely important, but insufficient sharing of relevant information and delayed decision-making are problems. Furthermore, insufficient communication between workers and managers can prevent appropriate support and feedback, leading to reduced productivity. Given this background, there is a need for a system that can improve the work environment and production efficiency by recognizing emotional states and providing personalized feedback.

[0516] 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.

[0517] In this invention, the server includes means for providing a user interface for a subordinate to input project information, means for receiving the input project information and extracting related concerns using a generative artificial intelligence model, means for using an emotion engine to recognize the worker's emotional state, means for evaluating the project information based on the extracted concerns and emotional information using a supervisor's personalized judgment model, means for sending and displaying the evaluation results to the subordinate's user terminal, and means for the subordinate to re-edit the project information based on the evaluation results and upload it to the project management system. This enables efficient communication between workers and managers, enables rapid problem detection and resolution, and improves production efficiency.

[0518] "Subordinates" refers to employees who work in the factory.

[0519] "Project information" refers to information including specific plans and pending issues related to production lines and maintenance operations.

[0520] "User interface" refers to the screens and operating means provided as a means for subordinates to input project information.

[0521] "Generative artificial intelligence model" refers to a system that uses a generative AI model to extract relevant concerns from input information.

[0522] "Related concerns" refer to the risks and potential problems of the project.

[0523] The "emotion engine" refers to a system that recognizes the emotional state of workers by analyzing voice, facial expressions, input, etc.

[0524] An "individualized judgment model" refers to a system that learns the decision-making process and values ​​of a specific supervisor and evaluates case information based on extracted concerns and emotional information.

[0525] "Evaluation results" refers to feedback and improvement suggestions generated as a result of evaluating case information based on concerns and emotional information.

[0526] "User terminal" refers to the computers and mobile devices used by subordinates.

[0527] A "project management system" refers to a system for managing project information and evaluation results.

[0528] "Database" refers to an electronic data storage system for storing evaluation results and project information.

[0529] A "digital twin" is a system that acts as a virtual avatar of a manager, tracking project progress and intervening as needed.

[0530] A system for implementing the present invention incorporates a series of means for improving work efficiency, and the system uses a user interface, a generative artificial intelligence model, an emotion engine, an individualized decision model, a database, a project management system, and a digital twin.

[0531] First, the server provides a user interface for subordinates to input project information. This interface allows workers to enter detailed information about the status of the production line and maintenance work. For example, a worker might enter, "The production line is running slowly and is likely to exceed its budget."

[0532] The entered project information is then sent from the user's device to a server. The server uses a generative AI model to extract relevant concerns from the project information. This generative AI model uses OpenAI's API and ChatGPT. During this process, the server identifies project risks and potential issues from the submitted text.

[0533] Furthermore, the server uses an emotion engine to recognize the worker's emotional state. The emotion engine analyzes the worker's input, voice, facial expressions, etc. to identify emotional states such as stress or satisfaction. For example, the server may recognize the worker's input as indicating stress.

[0534] The server then invokes the supervisor's personalized decision-making model based on the extracted concerns and emotional information. This decision-making model, which has learned the specific supervisor's decision-making process and values, evaluates the case information. The evaluation results are sent to the user's device and displayed to the worker. Specific feedback is provided, such as, "Consider how to reallocate your team and resources to optimize the speed of the production line. We also recommend that you consider new tools for budget management."

[0535] Based on this feedback, the worker re-edits the project information and sends it back to the server from the user's device. This re-edited information is then uploaded to the project management system.

[0536] The evaluation results and re-edited case information are stored in a database, and the server tracks the project's progress. When necessary, the server notifies the supervisor's digital twin, which acts as the supervisor's virtual avatar and allows for quick intervention in the project.

[0537] The following sentences are used as examples of prompt sentences:

[0538] Text format:

[0539] "Please extract the concerns or problems from the following text: Our production line is slow and we are on the verge of going over budget."

[0540] "Please provide feedback based on your feelings of stress based on the following concerns: The production line is slow and we're on the verge of going over budget."

[0541] This allows for efficient communication between workers and managers, enabling problems to be detected and resolved quickly, improving production efficiency.

[0542] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0543] Step 1:

[0544] A user interface is used by subordinates to input job information. Workers enter detailed information about the production line and maintenance tasks through user terminals. This input text is saved for later processing. Inputs include specific job information such as, "The production line is running slowly and is on the verge of exceeding budget."

[0545] Step 2:

[0546] The input project information is sent from the user's device to a server. The server temporarily stores the received information and uses a generative artificial intelligence model to extract relevant concerns from the project information. Specifically, it uses OpenAI's API and ChatGPT to analyze the input text and identify potential problems and risks. The output generates concerns such as "slowdown in production line speed" and "risk of budget overruns."

[0547] Step 3:

[0548] After extracting concerns, the server uses an emotion engine to recognize the worker's emotional state. It analyzes the user's input, voice, facial expressions, etc. to identify the emotional state. For example, it identifies emotions such as "stress" or "anxiety" from the input text. The input is the text sent in the previous step, and emotional information is generated as the output.

[0549] Step 4:

[0550] The server calls up the supervisor's personalized judgment model based on the extracted concerns and emotional information. This judgment model has learned the decision-making process and values ​​of the specific supervisor and evaluates the case information. The generative AI model is then used again for evaluation. For example, the supervisor's judgment model is used to generate "specific proposals for optimizing the speed of the production line." The input is the concerns and emotional information, and the output is the evaluation result.

[0551] Step 5:

[0552] The evaluation results are sent to the user's terminal. Specific feedback displayed on the subordinate's terminal may include, "Consider how to reallocate resources to optimize the speed of the production line." This allows the worker to identify areas for improvement or risks. The input is the evaluation results, and the output is feedback.

[0553] Step 6:

[0554] The subordinate re-edits the project information based on the feedback they receive. This re-edited information is then sent back to the server from the user's device. The server then uploads the re-edited information to the project management system and stores it in the database. The input is the re-edited project information, and the output is the updated project information stored in the database.

[0555] Step 7:

[0556] The server tracks the progress of the project. Based on the information stored in the project management system, it monitors progress in real time and sends notifications to the manager's digital twin as needed. This digital twin acts as the manager's virtual avatar, enabling quick intervention in the project. The input is project progress data, and the output is notifications.

[0557] 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.

[0558] 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.

[0559] 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.

[0560] [Third embodiment]

[0561] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0562] 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.

[0563] 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).

[0564] 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.

[0565] 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.

[0566] 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).

[0567] 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.

[0568] 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.

[0569] 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.

[0570] 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.

[0571] 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.

[0572] 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."

[0573] The present invention provides a system for improving the efficiency of communication between superiors and subordinates and for facilitating the processing of cases and decision-making within a company. This system is realized through the following components and processes.

[0574] First, a user interface is provided for subordinates to input project information. Through this interface, subordinates can input detailed information about projects and tasks using their own devices (e.g., computers or smartphones). This information is then sent to the server.

[0575] The server then analyzes the received project information. First, it uses a generative AI model (e.g., ChatGPT) to extract concerns about the project. These concerns indicate risks and potential problems for the project.

[0576] Based on the extracted concerns, the server calls up the supervisor's personalized decision-making model. This decision-making model learns the decision-making process and values ​​of the specific supervisor and evaluates the priority of the concerns. The evaluation results provide the importance of the concerns and the priority of countermeasures.

[0577] The evaluation results are sent to the subordinate's user device and displayed as feedback. This allows the subordinate to re-edit the project information based on the evaluation results. For example, if a risk of exceeding the budget is pointed out, the subordinate can add information to review the budget. The re-edited project information is then uploaded to the project management system.

[0578] Additionally, the server tracks project progress and notifies the supervisor's digital twin when necessary, allowing for quick action at points where supervisor intervention or confirmation is required.

[0579] As a concrete example, when a subordinate inputs a proposal for a new marketing campaign into the system, the proposal is sent to the server. The server uses a generative artificial intelligence model to extract concerns such as "risk of going over budget" and "unclear target market." These concerns are evaluated using the supervisor's judgment model, resulting in an evaluation result that prioritizes "risk of going over budget." Based on this result, the subordinate can reorganize the budget and clarify the target market. The re-edited proposal is then uploaded to the project management system.

[0580] This system allows the supervisor's digital twin to function effectively, allowing projects to progress quickly without the supervisor's direct intervention, resulting in more efficient communication within the company and improving the speed at which business is promoted.

[0581] The processing flow will be explained below.

[0582] Step 1:

[0583] The user inputs project information. Using the user interface of the device, the user inputs detailed project information, such as a proposal for a new marketing campaign. This input information is collected by the device as text data.

[0584] Step 2:

[0585] The terminal sends the case information entered by the user to the server. When the user presses the "Send" button, the case information is sent to the server via the Internet or a network.

[0586] Step 3:

[0587] The server receives the case information. The server stores the received case information in storage. The stored information includes the case content and user identification information.

[0588] Step 4:

[0589] The server calls a generative AI model and analyzes the received project information. The generative AI model (e.g., ChatGPT) is used to extract project-related concerns (e.g., "risk of exceeding budget" or "unclear target market").

[0590] Step 5:

[0591] The server calls up the supervisor's personalized decision-making model and evaluates the extracted concerns. The decision-making model, which has learned the supervisor's decision-making process and values, evaluates the priority of the concerns (for example, the risk of exceeding the budget is the top priority).

[0592] Step 6:

[0593] The server sends the evaluation results to the subordinate's terminal, where they are displayed as feedback.

[0594] Step 7:

[0595] The device displays the received feedback, allowing users to see the priority and rating of concerns on the device screen.

[0596] Step 8:

[0597] Users can re-edit the project information based on the feedback. For example, if they are told that there is a high risk of going over budget, they can review the budget plan and add more details.

[0598] Step 9:

[0599] The terminal uploads the re-edited project information to the project management system, and the re-edited new project information is saved in the project management system.

[0600] Step 10:

[0601] The server tracks the progress of the project and notifies the supervisor's digital twin as needed, so that specific progress and issues are shared with the supervisor's digital twin in real time, allowing necessary action to be taken promptly.

[0602] This process allows the boss's digital twin to function effectively, enabling subordinates to move projects forward quickly and efficiently.

[0603] Example 1

[0604] 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."

[0605] In modern companies, efficient communication and decision-making between superiors and subordinates is extremely important. Especially when large-scale projects or multiple projects are underway simultaneously, it can be difficult for superiors to accurately grasp the details of all projects and make decisions quickly. Furthermore, delays in reporting and feedback from subordinates to superiors can sometimes delay the progress of a project. There is a need for a system that can eliminate these inefficient communications and support rapid and accurate decision-making.

[0606] 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.

[0607] In this invention, the server includes means for providing a user interface for a subordinate to input project information, means for receiving the input project information and extracting related concerns using a generative artificial intelligence model, means for evaluating the project information based on the extracted concerns using the supervisor's personalized judgment model, means for sending and displaying the evaluation results to the subordinate's user terminal, means for the subordinate to re-edit the project information based on the evaluation results and upload it to a project management system, and means for tracking project progress and notifying the supervisor's digital twin as necessary. This enables rapid reception and analysis of project information from subordinates, efficient feedback to the supervisor, real-time tracking of project progress, and timely notification to the supervisor's digital twin.

[0608] "Project information" refers to detailed information about a project or task, specifically including the project name, summary, goals, deadline, budget, etc.

[0609] A "user interface" is a means by which a user directly interacts with a computer system, and primarily includes graphical user interfaces and web interfaces.

[0610] A "server" refers to a computer system that provides data or services in response to requests from clients.

[0611] A "generative artificial intelligence model" refers to an AI model that has the ability to generate new information or patterns from given data; an example is a natural language generation model.

[0612] "Concerns" are elements that indicate risks or potential problems with the project.

[0613] An "individualized boss judgment model" refers to an AI model that learns the decision-making process and values ​​of a specific boss and makes evaluations based on that.

[0614] "Evaluation results" refer to the analysis results and recommendations obtained using generative artificial intelligence models and judgment models.

[0615] A "user terminal" is a device that is directly operated by a user, and examples include computers and smartphones.

[0616] "Project management system" refers to a software tool used to manage the planning, execution, monitoring, and closure of projects.

[0617] "Project progress" refers to information showing the progress of a project and its current achievement status.

[0618] "Digital twin" refers to a digital representation of a physical entity, and in this context refers to an AI agent that mimics the decisions and actions of its superior.

[0619] This invention is a system for improving communication between superiors and subordinates and for accelerating case processing and decision-making within a company. This system is realized using subordinate terminals, a server, and a generative artificial intelligence model.

[0620] First, the subordinate uses his / her own device (e.g., a computer or smartphone) to enter project information. The user interface for this input is provided through a dedicated application or web browser. The subordinate enters details such as the project name, summary, goals, deadline, and budget into the form and clicks the "Submit" button, which sends the information to the server.

[0621] The server stores the received case information in a database and uses a generative artificial intelligence model (e.g., ChatGPT) to extract concerns related to the case. Concern extraction involves inputting the case information into the ChatGPT API and obtaining the extracted results in text format. For example, the prompt could be, "Please extract potential risks and issues from the given marketing campaign proposal."

[0622] Next, the server applies the supervisor's personalized decision model (e.g., TensorFlow model) to the extracted concerns and evaluates their importance. Here, the server loads the decision model and inputs the concern data to obtain the evaluation results. For example, a prompt request such as "Please evaluate the importance of this risk using your supervisor's decision model" can be used.

[0623] The evaluation results are sent from the server to the subordinate's device and displayed on the user interface. The subordinate then re-edits the project information based on the feedback and re-uploads it to the project management system. For example, if a risk of going over budget is pointed out, the subordinate will review the budget and enter additional information.

[0624] The progress of the project is continuously tracked by the server, which periodically retrieves and updates status information using the project management system's APIs. If necessary, the server also sends notifications to the supervisor's digital twin. If a notification is deemed necessary, the notification is sent via email or a chat tool (e.g., Slack).

[0625] Through this process, the system can quickly advance projects without direct intervention from superiors, improving communication efficiency within the company and speeding up decision-making.

[0626] As a concrete example, when a subordinate enters a proposal for a new marketing campaign into the system, the proposal is sent to the server. The server uses a generative artificial intelligence model to extract concerns such as "risk of going over budget" and "unclear target market." These concerns are evaluated using the supervisor's judgment model, resulting in an evaluation result that prioritizes "risk of going over budget." Based on this result, the subordinate reorganizes the budget and clarifies the target market. This revised proposal is uploaded to a project management system, and the server tracks progress and sends notifications to the supervisor's digital twin as needed. As a result, communication within the company becomes more efficient and business progress is accelerated.

[0627] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0628] Step 1:

[0629] The subordinate, who is the user, enters project information using a user interface on his or her own device. This user interface is provided through a dedicated application or a web browser. For example, the subordinate enters details such as the project name, summary, goals, deadline, and budget into a form and clicks the "Submit" button.

[0630] Input: Project information (project name, summary, goals, deadline, budget, etc.)

[0631] Output: Data sent from the terminal to the server (JSON format job information)

[0632] Step 2:

[0633] The terminal sends the input job information to the server in real time using an HTTP request.

[0634] Input: Data sent from the device (JSON format job information)

[0635] Output: Data received by the server (JSON format job information)

[0636] Step 3:

[0637] The server stores the received job information in a database and starts the analysis process. The server inserts the job information into the database and then adds it to a queue for the analysis process.

[0638] Input: Data received from the server (JSON format job information)

[0639] Output: Case information saved in the database

[0640] Step 4:

[0641] The server uses a generative artificial intelligence model (e.g., ChatGPT) to extract concerns from the case information. To extract concerns, the server passes the case information as input to the ChatGPT API and obtains the extracted results in text format.

[0642] Input: Case information stored in the database

[0643] Output: Extracted concerns (text format)

[0644] Step 5:

[0645] The server applies the supervisor's personalized judgment model (e.g., TensorFlow model) to the extracted concerns and evaluates their importance. The server loads the judgment model and inputs the concern data to obtain the evaluation results.

[0646] Input: Extracted concerns (text format)

[0647] Output: Evaluation results (importance of concerns and priority of countermeasures, in text format)

[0648] Step 6:

[0649] The server sends the evaluation results to the subordinate's device and displays them as feedback.The server sends the evaluation results in JSON format to the device, and the device displays the results in the user interface.

[0650] Input: Evaluation results (importance of concerns and priority of countermeasures, in text format)

[0651] Output: Evaluation results displayed on the user's device (JSON format)

[0652] Step 7:

[0653] The subordinate, who is the user, re-edits the project information based on the feedback and re-uploads it to the project management system. The subordinate enters the re-edited information and clicks the "Submit" button again to upload it to the project management system.

[0654] Input: Re-edited project information (JSON format)

[0655] Output: Project information stored in the project management system

[0656] Step 8:

[0657] The server continuously tracks the progress of the project through the project management system, periodically calling the project management system's APIs to retrieve and update status information.

[0658] Input: Project management system status information

[0659] Output: Progress data stored on the server

[0660] Step 9:

[0661] If the server determines that a notification should be sent to the supervisor's digital twin based on the project's progress, it will send the notification via email or chat tool.

[0662] Input: Progress data stored on the server

[0663] Output: Notification sent to the manager's digital twin (via email or chat tool)

[0664] The above processing steps enable quick receipt and analysis of project information from subordinates, efficient feedback to superiors, real-time tracking of project progress, and timely notification to the superior's digital twin.

[0665] (Application example 1)

[0666] 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."

[0667] Conventional systems for handling and deciding on projects within companies are prone to lack of communication between subordinates and superiors and delays in the transmission of information, making it difficult to speed up decision-making and efficiently handle projects. Similar communication problems also arise in the field of content planning and distribution between planning teams and management teams. As a result, content production and distribution are frequently delayed. It is necessary to solve these issues and achieve more efficient communication and faster decision-making within companies and in the content field.

[0668] 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.

[0669] In this invention, the server includes means for providing a user interface for subordinates and content planning teams to input project information and content planning information, means for receiving the input project information and content planning information and extracting related concerns using a generative artificial intelligence model, means for evaluating the project information and content planning information based on the extracted concerns using an individualized judgment model for superiors and management, means for transmitting and displaying the evaluation results to user terminals of the subordinates and content planning teams, and means for the subordinates and content planning teams to re-edit the project information and content planning information based on the evaluation results and upload it to a project management system and a content distribution system. This enables more efficient communication and faster decision-making within a company and in the content field.

[0670] "User interface" is a general term for the display devices and operating means that allow a user to input information and interact with a system.

[0671] "Project information" is a term that refers to detailed data and information about a project or business.

[0672] A "generative artificial intelligence model" is a machine learning algorithm that uses natural language processing to extract meaning and patterns from input data and generate responses.

[0673] "Concerns" are elements that could pose risks or problems in a project or content plan.

[0674] A "decision model" is an algorithm that has learned the decision-making process and values ​​of a specific manager or management.

[0675] "Evaluation results" refer to the results of the assessment of the importance of concerns and the priority of countermeasures using a judgment model.

[0676] "Terminal" means a device for accessing and operating the system through a user interface.

[0677] "Feedback" refers to information returned to the user, such as evaluation results or other responses from the system.

[0678] A "project management system" is software for planning, executing, and managing the progress of projects.

[0679] "Content planning information" refers to detailed plans and data regarding content production and distribution.

[0680] A "content distribution system" is a system for distributing digital content and providing it to viewers.

[0681] A "digital twin" is a virtual model of a physical object that is reflected in the digital world in real time.

[0682] The present invention relates to a system for improving communication efficiency and speeding up decision-making in the fields of corporate and content management. This system is realized through the following components and processes.

[0683] First, a user interface is provided for subordinates and content planning teams to input project information and content planning information. Through this interface, users can input information using their own devices (e.g., computers or smartphones). This information is then sent to the server.

[0684] The server then analyzes the received project information and content planning information. Specifically, it uses a generative artificial intelligence model (e.g., ChatGPT) to extract concerns about the project or content planning. The extracted concerns are elements that indicate project risks or potential problems.

[0685] Based on the extracted concerns, the server calls up individualized decision-making models for supervisors and management. These decision-making models learn the decision-making processes and values ​​of specific supervisors and management, and evaluate the priority of the concerns. The evaluation results provide the importance of the concerns and the priority of countermeasures.

[0686] The evaluation results are sent to the user devices of subordinates and the content planning team and displayed as feedback. This allows users to re-edit project information and content planning information based on the evaluation results. For example, if a "risk of going over budget" is pointed out, information can be added to review the budget. The re-edited information is uploaded to the project management system and content distribution system.

[0687] Additionally, the server tracks project progress and content delivery progress, notifying the digital twins of supervisors and management when necessary, allowing for swift action at points where supervisor or management intervention or review is required.

[0688] For example, when a subordinate enters a proposal for a new marketing campaign, the proposal is sent to a server. The server uses a generative artificial intelligence model to extract concerns such as "risk of going over budget" and "unclear target market." These concerns are evaluated using the supervisor's judgment model, resulting in an evaluation result that prioritizes, for example, "risk of going over budget." Based on this evaluation result, the subordinate can reorganize the budget and clarify the target market. The re-edited proposal is then uploaded to a project management system.

[0689] An example of a prompt might be, "Please extract the issues and concerns from the following content planning information: advertising budget is 500,000 yen, the main target is young people in their late 20s, and the results analysis needs to be completed by the end of March."

[0690] This system will improve communication efficiency within companies and in the content field, enabling faster and more optimal decision-making.

[0691] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0692] Step 1:

[0693] A user uses a terminal to input project information or content plan information. The input information is sent to the server through a user interface. The input data here is the details of a specific project or content plan.

[0694] Step 2:

[0695] The server analyzes the received case information or content planning information. It uses a generative AI model (e.g., ChatGPT) to extract relevant concerns from the input data. It uses prompts to identify concerns and list them. Data processing based on the input data involves analyzing the text and reading the concerns using natural language processing. The output is a list of concerns.

[0696] Step 3:

[0697] The server calls the supervisor's and management's individualized decision-making model based on the extracted concerns. The decision-making model is an algorithm that evaluates the importance and priority of the extracted concerns. The input data is a list of concerns, and the output is the evaluation results of the concerns. This evaluation reflects the decision-making process and values ​​of the specific supervisor or management.

[0698] Step 4:

[0699] The server sends the evaluation results to the user. The evaluation results are displayed as feedback on the user's device. The user re-edits the project information and content plan information based on the evaluation results. The input data is the evaluation results, and the output is the revised project information and content plan information.

[0700] Step 5:

[0701] The user sends a command to upload the modified project information or content planning information to the project management system or content distribution system. The server then uploads the modified information to the corresponding system. The input data is the modified information, and the output data is the new information stored in the project management system or content distribution system.

[0702] Step 6:

[0703] The server tracks the progress of the project and the progress of the content delivery. When necessary, it sends notifications to the digital twins of superiors and management. These notifications include important progress and issues in the project and content delivery. The input data is progress information, and the output is notification messages to the digital twins.

[0704] By following the above processing steps, the system can improve the efficiency of communication and control within a company and in the content field, and support quick and appropriate decision-making.

[0705] 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.

[0706] This invention relates to a system for streamlining communication between superiors and subordinates and for accelerating internal company case processing and decision-making. This system incorporates an emotion engine that recognizes the user's emotions, providing optimal feedback with even greater accuracy.

[0707] First, it provides a user interface for subordinates to input project information. Through this interface, subordinates can use their own devices to input detailed information about projects and tasks, including specific plans and issues. The input information is then sent from the device to the server.

[0708] The server then analyzes the received project information and uses a generative artificial intelligence model (e.g., ChatGPT) to extract project-related concerns, such as project risks and potential issues.

[0709] Furthermore, the server recognizes the user's emotions using an emotion engine, which analyzes the user's input, voice and facial expressions to identify the user's emotional state (e.g., stress, anger, satisfaction).

[0710] Based on the extracted concerns and the user's emotional information, the server invokes the supervisor's personalized judgment model. This judgment model learns the specific supervisor's decision-making process and values ​​and evaluates the case information based on the concerns. The evaluation results are adjusted according to the user's emotional state, allowing for more appropriate and personalized feedback.

[0711] The evaluation results are then sent to the subordinate's user device and displayed. This allows the subordinate to check the evaluation results and identify areas for improvement and risks. For example, if a risk of budget overrun is pointed out and the user is feeling stressed, the system will provide feedback including specific measures to mitigate the risk and the provision of support resources.

[0712] The subordinates re-edit the project information based on this feedback. The re-edited project information is sent back to the server from the terminal and uploaded to the project management system. The re-edited information and evaluation results are saved in the database.

[0713] Finally, the server tracks project progress and notifies the supervisor's digital twin when necessary, allowing the supervisor to intervene quickly. This process allows subordinates to work efficiently and receive appropriate support from their supervisor's virtual twin.

[0714] The processing flow will be explained below.

[0715] Step 1:

[0716] The user enters project information. The user uses the device's user interface to enter information about a new project. This input process includes project details, objectives, budget, etc. For example, a user might enter "Proposal for a new marketing campaign."

[0717] Step 2:

[0718] The terminal sends the case information entered by the user to the server. When the user presses the "Send" button, the case information is sent from the user terminal to the server via the network.

[0719] Step 3:

[0720] The server receives the case information. The server receives the entered information and stores it in an internal database. This information is used for subsequent processing.

[0721] Step 4:

[0722] The server uses a generative AI model to analyze project information and extract concerns. Project information is input into the generative AI model (e.g., ChatGPT), which extracts risks and issues related to the project. For example, "risk of exceeding budget" and "uncertain target market" are extracted.

[0723] Step 5:

[0724] The server recognizes the user's emotions using an emotion engine, which analyzes the facial expressions and voice data the user shows when inputting to identify the user's emotional state (e.g., stress, excitement, satisfaction).

[0725] Step 6:

[0726] The server calls the supervisor's personalized judgment model. The supervisor's judgment model evaluates the concerns using the extracted concerns and the recognized user's emotional information as input. For example, it may evaluate that the "risk of exceeding the budget" is high and therefore should be addressed as a priority.

[0727] Step 7:

[0728] The server sends the evaluation results to the subordinate's user device and displays them. The evaluation results are adjusted according to the user's emotional state and sent to the subordinate's device as optimal feedback. The device displays this and provides specific suggestions for improvement and risk countermeasures.

[0729] Step 8:

[0730] The device displays the received feedback, and the user can check the feedback on the device screen and understand any issues or suggestions for improvement.

[0731] Step 9:

[0732] Users can then re-edit the project information based on the feedback. For example, if they are told there is a high risk of going over budget, they can re-edit the budget plan and add information to clarify the target market details.

[0733] Step 10:

[0734] The terminal uploads the re-edited project information to the project management system, where the new re-edited project information is saved and available for other related departments to refer to.

[0735] Step 11:

[0736] The server tracks the progress of the project and notifies the supervisor's digital twin as needed. If there are any outstanding issues or progress on the project, the supervisor's digital twin is notified in real time, allowing appropriate action to be taken promptly.

[0737] This process allows subordinates to work on projects quickly and efficiently and receive appropriate support through their superiors' digital twins, improving communication and decision-making within the company and speeding up business progress.

[0738] Example 2

[0739] 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."

[0740] Traditional corporate communication systems make it difficult for superiors and subordinates to work together efficiently, often resulting in delays in project processing and decision-making. Another issue is that they are unable to properly grasp the emotional state of subordinates, making it difficult to provide appropriate feedback or support. This leads to inadequate understanding of project risk management and progress, resulting in reduced work efficiency.

[0741] 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.

[0742] In this invention, the server includes means for providing a user interface for a subordinate to input project information, means for receiving the input project information and extracting related concerns using a generative artificial intelligence model, means for analyzing the user's input content, voice data, or video and driving an emotion engine that identifies the user's emotional state, means for evaluating the project information based on the extracted concerns using the supervisor's personalized judgment model, means for transmitting and displaying the evaluation results to the subordinate's user terminal, and means for the subordinate to re-edit the project information based on the evaluation results and upload it to the project management system. This enables the provision of optimal feedback that takes the user's emotional state into consideration and the efficient management of project progress.

[0743] A "user interface" is a means of providing an operation screen or input form for subordinates to input case information.

[0744] "Project information" is data that includes detailed information about projects and tasks, specific plans, pending issues, and the like.

[0745] A "generative artificial intelligence model" refers to an artificial intelligence algorithm or system that analyzes input information and extracts relevant concerns.

[0746] "Concerns" refer to factors that require attention, such as project risks or potential problems.

[0747] An "emotion engine" refers to a system or algorithm that analyzes user input, audio data, or video to identify a user's emotional state.

[0748] "Emotional state" refers to the user's psychological state, such as stress, anger, or satisfaction.

[0749] A "decision model" refers to an individualized algorithm or system that learns a superior's decision-making process and values ​​and then evaluates case information.

[0750] "Evaluation results" refers to feedback generated based on the extracted concerns and the user's emotional state.

[0751] "User terminal" refers to devices such as computers and smartphones used by subordinates.

[0752] A "project management system" refers to a platform or software for managing project progress, tasks, and resources.

[0753] A "digital twin" is a system that acts as a virtual avatar of a manager, receiving real-time project progress and important notifications.

[0754] This invention relates to a system for streamlining communication between superiors and subordinates and for accelerating internal company case processing and decision-making. This system incorporates an emotion engine that recognizes the user's emotions, providing optimal feedback with even greater accuracy.

[0755] First, a user interface is provided for subordinates to input project information. Subordinates use their own devices to input detailed information about projects and tasks. Using this interface, they can easily input specific plans and issues. The input information is sent from the device to the server. The user interface is implemented as a web application that runs on a general web browser.

[0756] The server then analyzes the received project information and uses a generative artificial intelligence model (e.g., a GPT model) to extract project-related concerns. These concerns represent project risks and potential issues, such as the risk of budget overruns or schedule delays.

[0757] Additionally, the server uses an emotion engine to recognize the user's emotions. The emotion engine analyzes user input, voice data, or camera footage to identify the user's emotional state (e.g., stress, anger, satisfaction). The specific software used for this emotion analysis includes Azure Cognitive Services' facial recognition and voice emotion analysis APIs.

[0758] Based on the extracted concerns and the user's emotional information, the server invokes the supervisor's personalized judgment model. This judgment model learns the specific supervisor's decision-making process and values ​​and evaluates the case information based on the concerns. The evaluation results are adjusted according to the user's emotional state, providing more appropriate and personalized feedback.

[0759] The evaluation results are then sent to the subordinate's user device and displayed. The subordinate can review the evaluation results and identify areas for improvement and risks. For example, the system may point out the risk of exceeding the budget and suggest specific measures to mitigate the risk. As a concrete example, feedback is generated using prompt sentences such as the following:

[0760] Example prompt sentence:

[0761] "Project A's budget is 10% over schedule. I'd like some suggestions for improvement that take into account the current progress and a breakdown of how the budget is being used. I'm also feeling quite stressed about this situation."

[0762] The subordinates then re-edit the project information based on this feedback. The re-edited information is then sent back to the server from the terminal and uploaded to the project management system (e.g., Jira or Asana). This re-edited information and the evaluation results are then stored in the server's database.

[0763] Finally, the server tracks project progress and notifies the supervisor's digital twin of any significant changes or risks, allowing the supervisor to intervene quickly and provide the necessary support. This process ensures that subordinates can efficiently progress through projects and receive appropriate support from their supervisors.

[0764] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0765] Step 1:

[0766] A user uses their own device to input detailed project and work information into the user interface. This input includes specific plans and concerns. For example, they might input information such as, "Project A's budget is 5 million yen, and it is currently 80% complete. One concern is that it may exceed the budget." This information is sent from the device to the server (input: project information entered into the subordinate's device, output: project information sent to the server).

[0767] Step 2:

[0768] The server receives project information sent from the terminal. To analyze the received information, the server uses a generative artificial intelligence model (e.g., GPT model). This model extracts concerns related to the project. For example, concerns such as "possibility of budget overruns" and "progress delays" are automatically extracted from the input information (Input: Project information sent from the terminal, Output: Extracted concerns).

[0769] Step 3:

[0770] The server uses an emotion engine to recognize the user's emotions. The emotion engine analyzes the user's input (text data), voice data, or camera footage to identify the user's emotional state (e.g., stress, anger, satisfaction). For example, it detects that the user is "feeling stressed" from the input text or voice tone (input: user's text data, voice data, video; output: identified emotional state).

[0771] Step 4:

[0772] The server calls the manager's personalized decision-making model based on the extracted concerns and the user's recognized emotional state. This model learns the decision-making process and values ​​of the specific manager and makes an evaluation based on the extracted concerns. For example, it may make an evaluation such as, "There is a high risk of exceeding the budget, but the user is feeling stressed, so we will propose specific countermeasures" (Input: extracted concerns, identified emotional state, Output: evaluation result).

[0773] Step 5:

[0774] The server sends the generated evaluation results to the subordinate's user device. The device displays the evaluation results, which the subordinate can then review. Based on the evaluation results, the subordinate can identify areas for improvement and risks. For example, a specific suggestion such as "There is a high risk of exceeding the budget, so urgent cost-cutting measures are required" may be displayed (Input: Evaluation results, Output: Evaluation results displayed on the subordinate's device).

[0775] Step 6:

[0776] The subordinate re-edits the project information based on the feedback. The re-edited information is sent back to the server from the subordinate's device. The re-edited project information is uploaded to a project management system (e.g., Jira or Asana) and saved in a database. For example, information such as "A new budget adjustment plan has been created and the progress schedule has been revised" is re-edited (Input: Re-edited project information by subordinate, Output: Re-edited project information sent to the server).

[0777] Step 7:

[0778] The server tracks the progress of the project. If any important changes or risks occur, it notifies the supervisor's digital twin. This allows the supervisor to intervene quickly and provide the necessary support. For example, a notification may be sent to the supervisor's digital twin saying, "The budget adjustments were successful, but a new risk has emerged that requires action." (Input: Project progress information; Output: Notification sent to the supervisor's digital twin).

[0779] (Application example 2)

[0780] 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."

[0781] In modern factories, improving production line efficiency and responding quickly to maintenance work are extremely important, but insufficient sharing of relevant information and delayed decision-making are problems. Furthermore, insufficient communication between workers and managers can prevent appropriate support and feedback, leading to reduced productivity. Given this background, there is a need for a system that can improve the work environment and production efficiency by recognizing emotional states and providing personalized feedback.

[0782] 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.

[0783] In this invention, the server includes means for providing a user interface for a subordinate to input project information, means for receiving the input project information and extracting related concerns using a generative artificial intelligence model, means for using an emotion engine to recognize the worker's emotional state, means for evaluating the project information based on the extracted concerns and emotional information using a supervisor's personalized judgment model, means for sending and displaying the evaluation results to the subordinate's user terminal, and means for the subordinate to re-edit the project information based on the evaluation results and upload it to the project management system. This enables efficient communication between workers and managers, enables rapid problem detection and resolution, and improves production efficiency.

[0784] "Subordinates" refers to employees who work in the factory.

[0785] "Project information" refers to information including specific plans and pending issues related to production lines and maintenance operations.

[0786] "User interface" refers to the screens and operating means provided as a means for subordinates to input project information.

[0787] "Generative artificial intelligence model" refers to a system that uses a generative AI model to extract relevant concerns from input information.

[0788] "Related concerns" refer to the risks and potential problems of the project.

[0789] The "emotion engine" refers to a system that recognizes the emotional state of workers by analyzing voice, facial expressions, input, etc.

[0790] An "individualized judgment model" refers to a system that learns the decision-making process and values ​​of a specific supervisor and evaluates case information based on extracted concerns and emotional information.

[0791] "Evaluation results" refers to feedback and improvement suggestions generated as a result of evaluating case information based on concerns and emotional information.

[0792] "User terminal" refers to the computers and mobile devices used by subordinates.

[0793] A "project management system" refers to a system for managing project information and evaluation results.

[0794] "Database" refers to an electronic data storage system for storing evaluation results and project information.

[0795] A "digital twin" is a system that acts as a virtual avatar of a manager, tracking project progress and intervening as needed.

[0796] A system for implementing the present invention incorporates a series of means for improving work efficiency, and the system uses a user interface, a generative artificial intelligence model, an emotion engine, an individualized decision model, a database, a project management system, and a digital twin.

[0797] First, the server provides a user interface for subordinates to input project information. This interface allows workers to enter detailed information about the status of the production line and maintenance work. For example, a worker might enter, "The production line is running slowly and is likely to exceed its budget."

[0798] The entered project information is then sent from the user's device to a server. The server uses a generative AI model to extract relevant concerns from the project information. This generative AI model uses OpenAI's API and ChatGPT. During this process, the server identifies project risks and potential issues from the submitted text.

[0799] Furthermore, the server uses an emotion engine to recognize the worker's emotional state. The emotion engine analyzes the worker's input, voice, facial expressions, etc. to identify emotional states such as stress or satisfaction. For example, the server may recognize the worker's input as indicating stress.

[0800] The server then invokes the supervisor's personalized decision-making model based on the extracted concerns and emotional information. This decision-making model, which has learned the specific supervisor's decision-making process and values, evaluates the case information. The evaluation results are sent to the user's device and displayed to the worker. Specific feedback is provided, such as, "Consider how to reallocate your team and resources to optimize the speed of the production line. We also recommend that you consider new tools for budget management."

[0801] Based on this feedback, the worker re-edits the project information and sends it back to the server from the user's device. This re-edited information is then uploaded to the project management system.

[0802] The evaluation results and re-edited case information are stored in a database, and the server tracks the project's progress. When necessary, the server notifies the supervisor's digital twin, which acts as the supervisor's virtual avatar and allows for quick intervention in the project.

[0803] The following sentences are used as examples of prompt sentences:

[0804] Text format:

[0805] "Please extract the concerns or problems from the following text: Our production line is slow and we are on the verge of going over budget."

[0806] "Please provide feedback based on your feelings of stress based on the following concerns: The production line is slow and we're on the verge of going over budget."

[0807] This allows for efficient communication between workers and managers, enabling problems to be detected and resolved quickly, improving production efficiency.

[0808] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0809] Step 1:

[0810] A user interface is used by subordinates to input job information. Workers enter detailed information about the production line and maintenance tasks through user terminals. This input text is saved for later processing. Inputs include specific job information such as, "The production line is running slowly and is on the verge of exceeding budget."

[0811] Step 2:

[0812] The input project information is sent from the user's device to a server. The server temporarily stores the received information and uses a generative artificial intelligence model to extract relevant concerns from the project information. Specifically, it uses OpenAI's API and ChatGPT to analyze the input text and identify potential problems and risks. The output generates concerns such as "slowdown in production line speed" and "risk of budget overruns."

[0813] Step 3:

[0814] After extracting concerns, the server uses an emotion engine to recognize the worker's emotional state. It analyzes the user's input, voice, facial expressions, etc. to identify the emotional state. For example, it identifies emotions such as "stress" or "anxiety" from the input text. The input is the text sent in the previous step, and emotional information is generated as the output.

[0815] Step 4:

[0816] The server calls up the supervisor's personalized judgment model based on the extracted concerns and emotional information. This judgment model has learned the decision-making process and values ​​of the specific supervisor and evaluates the case information. The generative AI model is then used again for evaluation. For example, the supervisor's judgment model is used to generate "specific proposals for optimizing the speed of the production line." The input is the concerns and emotional information, and the output is the evaluation result.

[0817] Step 5:

[0818] The evaluation results are sent to the user's terminal. Specific feedback displayed on the subordinate's terminal may include, "Consider how to reallocate resources to optimize the speed of the production line." This allows the worker to identify areas for improvement or risks. The input is the evaluation results, and the output is feedback.

[0819] Step 6:

[0820] The subordinate re-edits the project information based on the feedback they receive. This re-edited information is then sent back to the server from the user's device. The server then uploads the re-edited information to the project management system and stores it in the database. The input is the re-edited project information, and the output is the updated project information stored in the database.

[0821] Step 7:

[0822] The server tracks the progress of the project. Based on the information stored in the project management system, it monitors progress in real time and sends notifications to the manager's digital twin as needed. This digital twin acts as the manager's virtual avatar, enabling quick intervention in the project. The input is project progress data, and the output is notifications.

[0823] 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.

[0824] 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.

[0825] 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.

[0826] [Fourth embodiment]

[0827] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0828] 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.

[0829] 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).

[0830] 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.

[0831] 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.

[0832] 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).

[0833] 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.

[0834] 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.

[0835] 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.

[0836] 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.

[0837] 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.

[0838] 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.

[0839] 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."

[0840] The present invention provides a system for improving the efficiency of communication between superiors and subordinates and for facilitating the processing of cases and decision-making within a company. This system is realized through the following components and processes.

[0841] First, a user interface is provided for subordinates to input project information. Through this interface, subordinates can input detailed information about projects and tasks using their own devices (e.g., computers or smartphones). This information is then sent to the server.

[0842] The server then analyzes the received project information. First, it uses a generative AI model (e.g., ChatGPT) to extract concerns about the project. These concerns indicate risks and potential problems for the project.

[0843] Based on the extracted concerns, the server calls up the supervisor's personalized decision-making model. This decision-making model learns the decision-making process and values ​​of the specific supervisor and evaluates the priority of the concerns. The evaluation results provide the importance of the concerns and the priority of countermeasures.

[0844] The evaluation results are sent to the subordinate's user device and displayed as feedback. This allows the subordinate to re-edit the project information based on the evaluation results. For example, if a risk of exceeding the budget is pointed out, the subordinate can add information to review the budget. The re-edited project information is then uploaded to the project management system.

[0845] Additionally, the server tracks project progress and notifies the supervisor's digital twin when necessary, allowing for quick action at points where supervisor intervention or confirmation is required.

[0846] As a concrete example, when a subordinate inputs a proposal for a new marketing campaign into the system, the proposal is sent to the server. The server uses a generative artificial intelligence model to extract concerns such as "risk of going over budget" and "unclear target market." These concerns are evaluated using the supervisor's judgment model, resulting in an evaluation result that prioritizes "risk of going over budget." Based on this result, the subordinate can reorganize the budget and clarify the target market. The re-edited proposal is then uploaded to the project management system.

[0847] This system allows the supervisor's digital twin to function effectively, allowing projects to progress quickly without the supervisor's direct intervention, resulting in more efficient communication within the company and improving the speed at which business is promoted.

[0848] The processing flow will be explained below.

[0849] Step 1:

[0850] The user inputs project information. Using the user interface of the device, the user inputs detailed project information, such as a proposal for a new marketing campaign. This input information is collected by the device as text data.

[0851] Step 2:

[0852] The terminal sends the case information entered by the user to the server. When the user presses the "Send" button, the case information is sent to the server via the Internet or a network.

[0853] Step 3:

[0854] The server receives the case information. The server stores the received case information in storage. The stored information includes the case content and user identification information.

[0855] Step 4:

[0856] The server calls a generative AI model and analyzes the received project information. The generative AI model (e.g., ChatGPT) is used to extract project-related concerns (e.g., "risk of exceeding budget" or "unclear target market").

[0857] Step 5:

[0858] The server calls up the supervisor's personalized decision-making model and evaluates the extracted concerns. The decision-making model, which has learned the supervisor's decision-making process and values, evaluates the priority of the concerns (for example, the risk of exceeding the budget is the top priority).

[0859] Step 6:

[0860] The server sends the evaluation results to the subordinate's terminal, where they are displayed as feedback.

[0861] Step 7:

[0862] The device displays the received feedback, allowing users to see the priority and rating of concerns on the device screen.

[0863] Step 8:

[0864] Users can re-edit the project information based on the feedback. For example, if they are told that there is a high risk of going over budget, they can review the budget plan and add more details.

[0865] Step 9:

[0866] The terminal uploads the re-edited project information to the project management system, and the re-edited new project information is saved in the project management system.

[0867] Step 10:

[0868] The server tracks the progress of the project and notifies the supervisor's digital twin as needed, so that specific progress and issues are shared with the supervisor's digital twin in real time, allowing necessary action to be taken promptly.

[0869] This process allows the boss's digital twin to function effectively, enabling subordinates to move projects forward quickly and efficiently.

[0870] Example 1

[0871] 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."

[0872] In modern companies, efficient communication and decision-making between superiors and subordinates is extremely important. Especially when large-scale projects or multiple projects are underway simultaneously, it can be difficult for superiors to accurately grasp the details of all projects and make decisions quickly. Furthermore, delays in reporting and feedback from subordinates to superiors can sometimes delay the progress of a project. There is a need for a system that can eliminate these inefficient communications and support rapid and accurate decision-making.

[0873] 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.

[0874] In this invention, the server includes means for providing a user interface for a subordinate to input project information, means for receiving the input project information and extracting related concerns using a generative artificial intelligence model, means for evaluating the project information based on the extracted concerns using the supervisor's personalized judgment model, means for sending and displaying the evaluation results to the subordinate's user terminal, means for the subordinate to re-edit the project information based on the evaluation results and upload it to a project management system, and means for tracking project progress and notifying the supervisor's digital twin as necessary. This enables rapid reception and analysis of project information from subordinates, efficient feedback to the supervisor, real-time tracking of project progress, and timely notification to the supervisor's digital twin.

[0875] "Project information" refers to detailed information about a project or task, specifically including the project name, summary, goals, deadline, budget, etc.

[0876] A "user interface" is a means by which a user directly interacts with a computer system, and primarily includes graphical user interfaces and web interfaces.

[0877] A "server" refers to a computer system that provides data or services in response to requests from clients.

[0878] A "generative artificial intelligence model" refers to an AI model that has the ability to generate new information or patterns from given data; an example is a natural language generation model.

[0879] "Concerns" are elements that indicate risks or potential problems with the project.

[0880] An "individualized boss judgment model" refers to an AI model that learns the decision-making process and values ​​of a specific boss and makes evaluations based on that.

[0881] "Evaluation results" refer to the analysis results and recommendations obtained using generative artificial intelligence models and judgment models.

[0882] A "user terminal" is a device that is directly operated by a user, and examples include computers and smartphones.

[0883] "Project management system" refers to a software tool used to manage the planning, execution, monitoring, and closure of projects.

[0884] "Project progress" refers to information showing the progress of a project and its current achievement status.

[0885] "Digital twin" refers to a digital representation of a physical entity, and in this context refers to an AI agent that mimics the decisions and actions of its superior.

[0886] This invention is a system for improving communication between superiors and subordinates and for accelerating case processing and decision-making within a company. This system is realized using subordinate terminals, a server, and a generative artificial intelligence model.

[0887] First, the subordinate uses his / her own device (e.g., a computer or smartphone) to enter project information. The user interface for this input is provided through a dedicated application or web browser. The subordinate enters details such as the project name, summary, goals, deadline, and budget into the form and clicks the "Submit" button, which sends the information to the server.

[0888] The server stores the received case information in a database and uses a generative artificial intelligence model (e.g., ChatGPT) to extract concerns related to the case. Concern extraction involves inputting the case information into the ChatGPT API and obtaining the extracted results in text format. For example, the prompt could be, "Please extract potential risks and issues from the given marketing campaign proposal."

[0889] Next, the server applies the supervisor's personalized decision model (e.g., TensorFlow model) to the extracted concerns and evaluates their importance. Here, the server loads the decision model and inputs the concern data to obtain the evaluation results. For example, a prompt request such as "Please evaluate the importance of this risk using your supervisor's decision model" can be used.

[0890] The evaluation results are sent from the server to the subordinate's device and displayed on the user interface. The subordinate then re-edits the project information based on the feedback and re-uploads it to the project management system. For example, if a risk of going over budget is pointed out, the subordinate will review the budget and enter additional information.

[0891] The progress of the project is continuously tracked by the server, which periodically retrieves and updates status information using the project management system's APIs. If necessary, the server also sends notifications to the supervisor's digital twin. If a notification is deemed necessary, the notification is sent via email or a chat tool (e.g., Slack).

[0892] Through this process, the system can quickly advance projects without direct intervention from superiors, improving communication efficiency within the company and speeding up decision-making.

[0893] As a concrete example, when a subordinate enters a proposal for a new marketing campaign into the system, the proposal is sent to the server. The server uses a generative artificial intelligence model to extract concerns such as "risk of going over budget" and "unclear target market." These concerns are evaluated using the supervisor's judgment model, resulting in an evaluation result that prioritizes "risk of going over budget." Based on this result, the subordinate reorganizes the budget and clarifies the target market. This revised proposal is uploaded to a project management system, and the server tracks progress and sends notifications to the supervisor's digital twin as needed. As a result, communication within the company becomes more efficient and business progress is accelerated.

[0894] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0895] Step 1:

[0896] The subordinate, who is the user, enters project information using a user interface on his or her own device. This user interface is provided through a dedicated application or a web browser. For example, the subordinate enters details such as the project name, summary, goals, deadline, and budget into a form and clicks the "Submit" button.

[0897] Input: Project information (project name, summary, goals, deadline, budget, etc.)

[0898] Output: Data sent from the terminal to the server (JSON format job information)

[0899] Step 2:

[0900] The terminal sends the input job information to the server in real time using an HTTP request.

[0901] Input: Data sent from the device (JSON format job information)

[0902] Output: Data received by the server (JSON format job information)

[0903] Step 3:

[0904] The server stores the received job information in a database and starts the analysis process. The server inserts the job information into the database and then adds it to a queue for the analysis process.

[0905] Input: Data received from the server (JSON format job information)

[0906] Output: Case information saved in the database

[0907] Step 4:

[0908] The server uses a generative artificial intelligence model (e.g., ChatGPT) to extract concerns from the case information. To extract concerns, the server passes the case information as input to the ChatGPT API and obtains the extracted results in text format.

[0909] Input: Case information stored in the database

[0910] Output: Extracted concerns (text format)

[0911] Step 5:

[0912] The server applies the supervisor's personalized judgment model (e.g., TensorFlow model) to the extracted concerns and evaluates their importance. The server loads the judgment model and inputs the concern data to obtain the evaluation results.

[0913] Input: Extracted concerns (text format)

[0914] Output: Evaluation results (importance of concerns and priority of countermeasures, in text format)

[0915] Step 6:

[0916] The server sends the evaluation results to the subordinate's device and displays them as feedback.The server sends the evaluation results in JSON format to the device, and the device displays the results in the user interface.

[0917] Input: Evaluation results (importance of concerns and priority of countermeasures, in text format)

[0918] Output: Evaluation results displayed on the user's device (JSON format)

[0919] Step 7:

[0920] The subordinate, who is the user, re-edits the project information based on the feedback and re-uploads it to the project management system. The subordinate enters the re-edited information and clicks the "Submit" button again to upload it to the project management system.

[0921] Input: Re-edited project information (JSON format)

[0922] Output: Project information stored in the project management system

[0923] Step 8:

[0924] The server continuously tracks the progress of the project through the project management system, periodically calling the project management system's APIs to retrieve and update status information.

[0925] Input: Project management system status information

[0926] Output: Progress data stored on the server

[0927] Step 9:

[0928] If the server determines that a notification should be sent to the supervisor's digital twin based on the project's progress, it will send the notification via email or chat tool.

[0929] Input: Progress data stored on the server

[0930] Output: Notification sent to the manager's digital twin (via email or chat tool)

[0931] The above processing steps enable quick receipt and analysis of project information from subordinates, efficient feedback to superiors, real-time tracking of project progress, and timely notification to the superior's digital twin.

[0932] (Application example 1)

[0933] 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."

[0934] Conventional systems for handling and deciding on projects within companies are prone to lack of communication between subordinates and superiors and delays in the transmission of information, making it difficult to speed up decision-making and efficiently handle projects. Similar communication problems also arise in the field of content planning and distribution between planning teams and management teams. As a result, content production and distribution are frequently delayed. It is necessary to solve these issues and achieve more efficient communication and faster decision-making within companies and in the content field.

[0935] 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.

[0936] In this invention, the server includes means for providing a user interface for subordinates and content planning teams to input project information and content planning information, means for receiving the input project information and content planning information and extracting related concerns using a generative artificial intelligence model, means for evaluating the project information and content planning information based on the extracted concerns using an individualized judgment model for superiors and management, means for transmitting and displaying the evaluation results to user terminals of the subordinates and content planning teams, and means for the subordinates and content planning teams to re-edit the project information and content planning information based on the evaluation results and upload it to a project management system and a content distribution system. This enables more efficient communication and faster decision-making within a company and in the content field.

[0937] "User interface" is a general term for the display devices and operating means that allow a user to input information and interact with a system.

[0938] "Project information" is a term that refers to detailed data and information about a project or business.

[0939] A "generative artificial intelligence model" is a machine learning algorithm that uses natural language processing to extract meaning and patterns from input data and generate responses.

[0940] "Concerns" are elements that could pose risks or problems in a project or content plan.

[0941] A "decision model" is an algorithm that has learned the decision-making process and values ​​of a specific manager or management.

[0942] "Evaluation results" refer to the results of the assessment of the importance of concerns and the priority of countermeasures using a judgment model.

[0943] "Terminal" means a device for accessing and operating the system through a user interface.

[0944] "Feedback" refers to information returned to the user, such as evaluation results or other responses from the system.

[0945] A "project management system" is software for planning, executing, and managing the progress of projects.

[0946] "Content planning information" refers to detailed plans and data regarding content production and distribution.

[0947] A "content distribution system" is a system for distributing digital content and providing it to viewers.

[0948] A "digital twin" is a virtual model of a physical object that is reflected in the digital world in real time.

[0949] The present invention relates to a system for improving communication efficiency and speeding up decision-making in the fields of corporate and content management. This system is realized through the following components and processes.

[0950] First, a user interface is provided for subordinates and content planning teams to input project information and content planning information. Through this interface, users can input information using their own devices (e.g., computers or smartphones). This information is then sent to the server.

[0951] The server then analyzes the received project information and content planning information. Specifically, it uses a generative artificial intelligence model (e.g., ChatGPT) to extract concerns about the project or content planning. The extracted concerns are elements that indicate project risks or potential problems.

[0952] Based on the extracted concerns, the server calls up individualized decision-making models for supervisors and management. These decision-making models learn the decision-making processes and values ​​of specific supervisors and management, and evaluate the priority of the concerns. The evaluation results provide the importance of the concerns and the priority of countermeasures.

[0953] The evaluation results are sent to the user devices of subordinates and the content planning team and displayed as feedback. This allows users to re-edit project information and content planning information based on the evaluation results. For example, if a "risk of going over budget" is pointed out, information can be added to review the budget. The re-edited information is uploaded to the project management system and content distribution system.

[0954] Additionally, the server tracks project progress and content delivery progress, notifying the digital twins of supervisors and management when necessary, allowing for swift action at points where supervisor or management intervention or review is required.

[0955] For example, when a subordinate enters a proposal for a new marketing campaign, the proposal is sent to a server. The server uses a generative artificial intelligence model to extract concerns such as "risk of going over budget" and "unclear target market." These concerns are evaluated using the supervisor's judgment model, resulting in an evaluation result that prioritizes, for example, "risk of going over budget." Based on this evaluation result, the subordinate can reorganize the budget and clarify the target market. The re-edited proposal is then uploaded to a project management system.

[0956] An example of a prompt might be, "Please extract the issues and concerns from the following content planning information: advertising budget is 500,000 yen, the main target is young people in their late 20s, and the results analysis needs to be completed by the end of March."

[0957] This system will improve communication efficiency within companies and in the content field, enabling faster and more optimal decision-making.

[0958] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0959] Step 1:

[0960] A user uses a terminal to input project information or content plan information. The input information is sent to the server through a user interface. The input data here is the details of a specific project or content plan.

[0961] Step 2:

[0962] The server analyzes the received case information or content planning information. It uses a generative AI model (e.g., ChatGPT) to extract relevant concerns from the input data. It uses prompts to identify concerns and list them. Data processing based on the input data involves analyzing the text and reading the concerns using natural language processing. The output is a list of concerns.

[0963] Step 3:

[0964] The server calls the supervisor's and management's individualized decision-making model based on the extracted concerns. The decision-making model is an algorithm that evaluates the importance and priority of the extracted concerns. The input data is a list of concerns, and the output is the evaluation results of the concerns. This evaluation reflects the decision-making process and values ​​of the specific supervisor or management.

[0965] Step 4:

[0966] The server sends the evaluation results to the user. The evaluation results are displayed as feedback on the user's device. The user re-edits the project information and content plan information based on the evaluation results. The input data is the evaluation results, and the output is the revised project information and content plan information.

[0967] Step 5:

[0968] The user sends a command to upload the modified project information or content planning information to the project management system or content distribution system. The server then uploads the modified information to the corresponding system. The input data is the modified information, and the output data is the new information stored in the project management system or content distribution system.

[0969] Step 6:

[0970] The server tracks the progress of the project and the progress of the content delivery. When necessary, it sends notifications to the digital twins of superiors and management. These notifications include important progress and issues in the project and content delivery. The input data is progress information, and the output is notification messages to the digital twins.

[0971] By following the above processing steps, the system can improve the efficiency of communication and control within a company and in the content field, and support quick and appropriate decision-making.

[0972] 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.

[0973] This invention relates to a system for streamlining communication between superiors and subordinates and for accelerating internal company case processing and decision-making. This system incorporates an emotion engine that recognizes the user's emotions, providing optimal feedback with even greater accuracy.

[0974] First, it provides a user interface for subordinates to input project information. Through this interface, subordinates can use their own devices to input detailed information about projects and tasks, including specific plans and issues. The input information is then sent from the device to the server.

[0975] The server then analyzes the received project information and uses a generative artificial intelligence model (e.g., ChatGPT) to extract project-related concerns, such as project risks and potential issues.

[0976] Furthermore, the server recognizes the user's emotions using an emotion engine, which analyzes the user's input, voice and facial expressions to identify the user's emotional state (e.g., stress, anger, satisfaction).

[0977] Based on the extracted concerns and the user's emotional information, the server invokes the supervisor's personalized judgment model. This judgment model learns the specific supervisor's decision-making process and values ​​and evaluates the case information based on the concerns. The evaluation results are adjusted according to the user's emotional state, allowing for more appropriate and personalized feedback.

[0978] The evaluation results are then sent to the subordinate's user device and displayed. This allows the subordinate to check the evaluation results and identify areas for improvement and risks. For example, if a risk of budget overrun is pointed out and the user is feeling stressed, the system will provide feedback including specific measures to mitigate the risk and the provision of support resources.

[0979] The subordinates re-edit the project information based on this feedback. The re-edited project information is sent back to the server from the terminal and uploaded to the project management system. The re-edited information and evaluation results are saved in the database.

[0980] Finally, the server tracks project progress and notifies the supervisor's digital twin when necessary, allowing the supervisor to intervene quickly. This process allows subordinates to work efficiently and receive appropriate support from their supervisor's virtual twin.

[0981] The processing flow will be explained below.

[0982] Step 1:

[0983] The user enters project information. The user uses the device's user interface to enter information about a new project. This input process includes project details, objectives, budget, etc. For example, a user might enter "Proposal for a new marketing campaign."

[0984] Step 2:

[0985] The terminal sends the case information entered by the user to the server. When the user presses the "Send" button, the case information is sent from the user terminal to the server via the network.

[0986] Step 3:

[0987] The server receives the case information. The server receives the entered information and stores it in an internal database. This information is used for subsequent processing.

[0988] Step 4:

[0989] The server uses a generative AI model to analyze project information and extract concerns. Project information is input into the generative AI model (e.g., ChatGPT), which extracts risks and issues related to the project. For example, "risk of exceeding budget" and "uncertain target market" are extracted.

[0990] Step 5:

[0991] The server recognizes the user's emotions using an emotion engine, which analyzes the facial expressions and voice data the user shows when inputting to identify the user's emotional state (e.g., stress, excitement, satisfaction).

[0992] Step 6:

[0993] The server calls the supervisor's personalized judgment model. The supervisor's judgment model evaluates the concerns using the extracted concerns and the recognized user's emotional information as input. For example, it may evaluate that the "risk of exceeding the budget" is high and therefore should be addressed as a priority.

[0994] Step 7:

[0995] The server sends the evaluation results to the subordinate's user device and displays them. The evaluation results are adjusted according to the user's emotional state and sent to the subordinate's device as optimal feedback. The device displays this and provides specific suggestions for improvement and risk countermeasures.

[0996] Step 8:

[0997] The device displays the received feedback, and the user can check the feedback on the device screen and understand any issues or suggestions for improvement.

[0998] Step 9:

[0999] Users can then re-edit the project information based on the feedback. For example, if they are told there is a high risk of going over budget, they can re-edit the budget plan and add information to clarify the target market details.

[1000] Step 10:

[1001] The terminal uploads the re-edited project information to the project management system, where the new re-edited project information is saved and available for other related departments to refer to.

[1002] Step 11:

[1003] The server tracks the progress of the project and notifies the supervisor's digital twin as needed. If there are any outstanding issues or progress on the project, the supervisor's digital twin is notified in real time, allowing appropriate action to be taken promptly.

[1004] This process allows subordinates to work on projects quickly and efficiently and receive appropriate support through their superiors' digital twins, improving communication and decision-making within the company and speeding up business progress.

[1005] Example 2

[1006] 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."

[1007] Traditional corporate communication systems make it difficult for superiors and subordinates to work together efficiently, often resulting in delays in project processing and decision-making. Another issue is that they are unable to properly grasp the emotional state of subordinates, making it difficult to provide appropriate feedback or support. This leads to inadequate understanding of project risk management and progress, resulting in reduced work efficiency.

[1008] 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.

[1009] In this invention, the server includes means for providing a user interface for a subordinate to input project information, means for receiving the input project information and extracting related concerns using a generative artificial intelligence model, means for analyzing the user's input content, voice data, or video and driving an emotion engine that identifies the user's emotional state, means for evaluating the project information based on the extracted concerns using the supervisor's personalized judgment model, means for transmitting and displaying the evaluation results to the subordinate's user terminal, and means for the subordinate to re-edit the project information based on the evaluation results and upload it to the project management system. This enables the provision of optimal feedback that takes the user's emotional state into consideration and the efficient management of project progress.

[1010] A "user interface" is a means of providing an operation screen or input form for subordinates to input case information.

[1011] "Project information" is data that includes detailed information about projects and tasks, specific plans, pending issues, and the like.

[1012] A "generative artificial intelligence model" refers to an artificial intelligence algorithm or system that analyzes input information and extracts relevant concerns.

[1013] "Concerns" refer to factors that require attention, such as project risks or potential problems.

[1014] An "emotion engine" refers to a system or algorithm that analyzes user input, audio data, or video to identify a user's emotional state.

[1015] "Emotional state" refers to the user's psychological state, such as stress, anger, or satisfaction.

[1016] A "decision model" refers to an individualized algorithm or system that learns a superior's decision-making process and values ​​and then evaluates case information.

[1017] "Evaluation results" refers to feedback generated based on the extracted concerns and the user's emotional state.

[1018] "User terminal" refers to devices such as computers and smartphones used by subordinates.

[1019] A "project management system" refers to a platform or software for managing project progress, tasks, and resources.

[1020] A "digital twin" is a system that acts as a virtual avatar of a manager, receiving real-time project progress and important notifications.

[1021] This invention relates to a system for streamlining communication between superiors and subordinates and for accelerating internal company case processing and decision-making. This system incorporates an emotion engine that recognizes the user's emotions, providing optimal feedback with even greater accuracy.

[1022] First, a user interface is provided for subordinates to input project information. Subordinates use their own devices to input detailed information about projects and tasks. Using this interface, they can easily input specific plans and issues. The input information is sent from the device to the server. The user interface is implemented as a web application that runs on a general web browser.

[1023] The server then analyzes the received project information and uses a generative artificial intelligence model (e.g., a GPT model) to extract project-related concerns. These concerns represent project risks and potential issues, such as the risk of budget overruns or schedule delays.

[1024] Additionally, the server uses an emotion engine to recognize the user's emotions. The emotion engine analyzes user input, voice data, or camera footage to identify the user's emotional state (e.g., stress, anger, satisfaction). The specific software used for this emotion analysis includes Azure Cognitive Services' facial recognition and voice emotion analysis APIs.

[1025] Based on the extracted concerns and the user's emotional information, the server invokes the supervisor's personalized judgment model. This judgment model learns the specific supervisor's decision-making process and values ​​and evaluates the case information based on the concerns. The evaluation results are adjusted according to the user's emotional state, providing more appropriate and personalized feedback.

[1026] The evaluation results are then sent to the subordinate's user device and displayed. The subordinate can review the evaluation results and identify areas for improvement and risks. For example, the system may point out the risk of exceeding the budget and suggest specific measures to mitigate the risk. As a concrete example, feedback is generated using prompt sentences such as the following:

[1027] Example prompt sentence:

[1028] "Project A's budget is 10% over schedule. I'd like some suggestions for improvement that take into account the current progress and a breakdown of how the budget is being used. I'm also feeling quite stressed about this situation."

[1029] The subordinates then re-edit the project information based on this feedback. The re-edited information is then sent back to the server from the terminal and uploaded to the project management system (e.g., Jira or Asana). This re-edited information and the evaluation results are then stored in the server's database.

[1030] Finally, the server tracks project progress and notifies the supervisor's digital twin of any significant changes or risks, allowing the supervisor to intervene quickly and provide the necessary support. This process ensures that subordinates can efficiently progress through projects and receive appropriate support from their supervisors.

[1031] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1032] Step 1:

[1033] A user uses their own device to input detailed project and work information into the user interface. This input includes specific plans and concerns. For example, they might input information such as, "Project A's budget is 5 million yen, and it is currently 80% complete. One concern is that it may exceed the budget." This information is sent from the device to the server (input: project information entered into the subordinate's device, output: project information sent to the server).

[1034] Step 2:

[1035] The server receives project information sent from the terminal. To analyze the received information, the server uses a generative artificial intelligence model (e.g., GPT model). This model extracts concerns related to the project. For example, concerns such as "possibility of budget overruns" and "progress delays" are automatically extracted from the input information (Input: Project information sent from the terminal, Output: Extracted concerns).

[1036] Step 3:

[1037] The server uses an emotion engine to recognize the user's emotions. The emotion engine analyzes the user's input (text data), voice data, or camera footage to identify the user's emotional state (e.g., stress, anger, satisfaction). For example, it detects that the user is "feeling stressed" from the input text or voice tone (input: user's text data, voice data, video; output: identified emotional state).

[1038] Step 4:

[1039] The server calls the manager's personalized decision-making model based on the extracted concerns and the user's recognized emotional state. This model learns the decision-making process and values ​​of the specific manager and makes an evaluation based on the extracted concerns. For example, it may make an evaluation such as, "There is a high risk of exceeding the budget, but the user is feeling stressed, so we will propose specific countermeasures" (Input: extracted concerns, identified emotional state, Output: evaluation result).

[1040] Step 5:

[1041] The server sends the generated evaluation results to the subordinate's user device. The device displays the evaluation results, which the subordinate can then review. Based on the evaluation results, the subordinate can identify areas for improvement and risks. For example, a specific suggestion such as "There is a high risk of exceeding the budget, so urgent cost-cutting measures are required" may be displayed (Input: Evaluation results, Output: Evaluation results displayed on the subordinate's device).

[1042] Step 6:

[1043] The subordinate re-edits the project information based on the feedback. The re-edited information is sent back to the server from the subordinate's device. The re-edited project information is uploaded to a project management system (e.g., Jira or Asana) and saved in a database. For example, information such as "A new budget adjustment plan has been created and the progress schedule has been revised" is re-edited (Input: Re-edited project information by subordinate, Output: Re-edited project information sent to the server).

[1044] Step 7:

[1045] The server tracks the progress of the project. If any important changes or risks occur, it notifies the supervisor's digital twin. This allows the supervisor to intervene quickly and provide the necessary support. For example, a notification may be sent to the supervisor's digital twin saying, "The budget adjustments were successful, but a new risk has emerged that requires action." (Input: Project progress information; Output: Notification sent to the supervisor's digital twin).

[1046] (Application example 2)

[1047] 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."

[1048] In modern factories, improving production line efficiency and responding quickly to maintenance work are extremely important, but insufficient sharing of relevant information and delayed decision-making are problems. Furthermore, insufficient communication between workers and managers can prevent appropriate support and feedback, leading to reduced productivity. Given this background, there is a need for a system that can improve the work environment and production efficiency by recognizing emotional states and providing personalized feedback.

[1049] 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.

[1050] In this invention, the server includes means for providing a user interface for a subordinate to input project information, means for receiving the input project information and extracting related concerns using a generative artificial intelligence model, means for using an emotion engine to recognize the worker's emotional state, means for evaluating the project information based on the extracted concerns and emotional information using a supervisor's personalized judgment model, means for sending and displaying the evaluation results to the subordinate's user terminal, and means for the subordinate to re-edit the project information based on the evaluation results and upload it to the project management system. This enables efficient communication between workers and managers, enables rapid problem detection and resolution, and improves production efficiency.

[1051] "Subordinates" refers to employees who work in the factory.

[1052] "Project information" refers to information including specific plans and pending issues related to production lines and maintenance operations.

[1053] "User interface" refers to the screens and operating means provided as a means for subordinates to input project information.

[1054] "Generative artificial intelligence model" refers to a system that uses a generative AI model to extract relevant concerns from input information.

[1055] "Related concerns" refer to the risks and potential problems of the project.

[1056] The "emotion engine" refers to a system that recognizes the emotional state of workers by analyzing voice, facial expressions, input, etc.

[1057] An "individualized judgment model" refers to a system that learns the decision-making process and values ​​of a specific supervisor and evaluates case information based on extracted concerns and emotional information.

[1058] "Evaluation results" refers to feedback and improvement suggestions generated as a result of evaluating case information based on concerns and emotional information.

[1059] "User terminal" refers to the computers and mobile devices used by subordinates.

[1060] A "project management system" refers to a system for managing project information and evaluation results.

[1061] "Database" refers to an electronic data storage system for storing evaluation results and project information.

[1062] A "digital twin" is a system that acts as a virtual avatar of a manager, tracking project progress and intervening as needed.

[1063] A system for implementing the present invention incorporates a series of means for improving work efficiency, and the system uses a user interface, a generative artificial intelligence model, an emotion engine, an individualized decision model, a database, a project management system, and a digital twin.

[1064] First, the server provides a user interface for subordinates to input project information. This interface allows workers to enter detailed information about the status of the production line and maintenance work. For example, a worker might enter, "The production line is running slowly and is likely to exceed its budget."

[1065] The entered project information is then sent from the user's device to a server. The server uses a generative AI model to extract relevant concerns from the project information. This generative AI model uses OpenAI's API and ChatGPT. During this process, the server identifies project risks and potential issues from the submitted text.

[1066] Furthermore, the server uses an emotion engine to recognize the worker's emotional state. The emotion engine analyzes the worker's input, voice, facial expressions, etc. to identify emotional states such as stress or satisfaction. For example, the server may recognize the worker's input as indicating stress.

[1067] The server then invokes the supervisor's personalized decision-making model based on the extracted concerns and emotional information. This decision-making model, which has learned the specific supervisor's decision-making process and values, evaluates the case information. The evaluation results are sent to the user's device and displayed to the worker. Specific feedback is provided, such as, "Consider how to reallocate your team and resources to optimize the speed of the production line. We also recommend that you consider new tools for budget management."

[1068] Based on this feedback, the worker re-edits the project information and sends it back to the server from the user's device. This re-edited information is then uploaded to the project management system.

[1069] The evaluation results and re-edited case information are stored in a database, and the server tracks the project's progress. When necessary, the server notifies the supervisor's digital twin, which acts as the supervisor's virtual avatar and allows for quick intervention in the project.

[1070] The following sentences are used as examples of prompt sentences:

[1071] Text format:

[1072] "Please extract the concerns or problems from the following text: Our production line is slow and we are on the verge of going over budget."

[1073] "Please provide feedback based on your feelings of stress based on the following concerns: The production line is slow and we're on the verge of going over budget."

[1074] This allows for efficient communication between workers and managers, enabling problems to be detected and resolved quickly, improving production efficiency.

[1075] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1076] Step 1:

[1077] A user interface is used by subordinates to input job information. Workers enter detailed information about the production line and maintenance tasks through user terminals. This input text is saved for later processing. Inputs include specific job information such as, "The production line is running slowly and is on the verge of exceeding budget."

[1078] Step 2:

[1079] The input project information is sent from the user's device to a server. The server temporarily stores the received information and uses a generative artificial intelligence model to extract relevant concerns from the project information. Specifically, it uses OpenAI's API and ChatGPT to analyze the input text and identify potential problems and risks. The output generates concerns such as "slowdown in production line speed" and "risk of budget overruns."

[1080] Step 3:

[1081] After extracting concerns, the server uses an emotion engine to recognize the worker's emotional state. It analyzes the user's input, voice, facial expressions, etc. to identify the emotional state. For example, it identifies emotions such as "stress" or "anxiety" from the input text. The input is the text sent in the previous step, and emotional information is generated as the output.

[1082] Step 4:

[1083] The server calls up the supervisor's personalized judgment model based on the extracted concerns and emotional information. This judgment model has learned the decision-making process and values ​​of the specific supervisor and evaluates the case information. The generative AI model is then used again for evaluation. For example, the supervisor's judgment model is used to generate "specific proposals for optimizing the speed of the production line." The input is the concerns and emotional information, and the output is the evaluation result.

[1084] Step 5:

[1085] The evaluation results are sent to the user's terminal. Specific feedback displayed on the subordinate's terminal may include, "Consider how to reallocate resources to optimize the speed of the production line." This allows the worker to identify areas for improvement or risks. The input is the evaluation results, and the output is feedback.

[1086] Step 6:

[1087] The subordinate re-edits the project information based on the feedback they receive. This re-edited information is then sent back to the server from the user's device. The server then uploads the re-edited information to the project management system and stores it in the database. The input is the re-edited project information, and the output is the updated project information stored in the database.

[1088] Step 7:

[1089] The server tracks the progress of the project. Based on the information stored in the project management system, it monitors progress in real time and sends notifications to the manager's digital twin as needed. This digital twin acts as the manager's virtual avatar, enabling quick intervention in the project. The input is project progress data, and the output is notifications.

[1090] 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.

[1091] 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.

[1092] 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.

[1093] 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.

[1094] 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.

[1095] 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.

[1096] 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).

[1097] 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.

[1098] 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."

[1099] 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.

[1100] 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).

[1101] 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.

[1102] 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.

[1103] 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.

[1104] 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.

[1105] 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.

[1106] 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.

[1107] 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.

[1108] 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.

[1109] 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.

[1110] 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.

[1111] The following is further disclosed regarding the above embodiment.

[1112] (Claim 1)

[1113] means for providing a user interface for subordinates to input case information;

[1114] means for receiving input case information and extracting relevant concerns using a generative artificial intelligence model;

[1115] a means for evaluating case information based on extracted concerns using a supervisor's individualized judgment model;

[1116] a means for transmitting and displaying the evaluation results on the subordinate's user terminal;

[1117] A means for subordinates to re-edit project information based on the evaluation results and upload it to the project management system;

[1118] A system including:

[1119] (Claim 2)

[1120] 10. The system of claim 1, further comprising means for storing the evaluation results in a database and storing the recompiled project information in a project management system.

[1121] (Claim 3)

[1122] 10. The system of claim 1, further comprising means for tracking project progress and notifying the supervisor's digital twin when necessary.

[1123] "Example 1"

[1124] (Claim 1)

[1125] means for providing a user interface for subordinates to input case information;

[1126] means for receiving input case information and extracting relevant concerns using a generative artificial intelligence model;

[1127] a means for evaluating case information based on extracted concerns using a supervisor's individualized judgment model;

[1128] a means for transmitting and displaying the evaluation results on the subordinate's user terminal;

[1129] A means for subordinates to re-edit project information based on the evaluation results and upload it to the project management system;

[1130] A way to track project progress and notify the manager's digital twin when necessary;

[1131] A system including:

[1132] (Claim 2)

[1133] 10. The system of claim 1, further comprising means for storing the evaluation results in a database and storing the recompiled project information in a project management system.

[1134] (Claim 3)

[1135] 2. The system according to claim 1, further comprising means for transmitting case information from a subordinate's terminal to the server in real time.

[1136] "Application Example 1"

[1137] (Claim 1)

[1138] means for providing a user interface for subordinates to input case information;

[1139] means for receiving input case information and extracting relevant concerns using a generative artificial intelligence model;

[1140] a means for evaluating case information based on extracted concerns using a supervisor's individualized judgment model;

[1141] a means for transmitting and displaying the evaluation results on the subordinate's user terminal;

[1142] A means for subordinates to re-edit project information based on the evaluation results and upload it to the project management system;

[1143] A means for providing an interface for inputting content planning information, analyzing the input content planning information, and extracting concerns using a generative artificial intelligence model;

[1144] A method for evaluating priorities based on the extracted concerns, sending the evaluation results to the content planning team, and displaying them as feedback;

[1145] means for reorganizing the content planning information based on the priority of concerns and uploading the information to a content distribution system;

[1146] A system including:

[1147] (Claim 2)

[1148] 10. The system of claim 1, further comprising means for storing the evaluation results in a database and for storing the recompiled case information and content planning information in a project management system and a content distribution system.

[1149] (Claim 3)

[1150] 10. The system of claim 1, further comprising means for tracking project progress and content delivery progress and notifying the supervisor's digital twin and management's digital twin when necessary.

[1151] "Example 2: Combining Emotion Engines"

[1152] (Claim 1)

[1153] means for providing a user interface for subordinates to input case information;

[1154] means for receiving input case information and extracting relevant concerns using a generative artificial intelligence model;

[1155] means for analyzing user input, voice data, or video to drive an emotion engine that identifies the user's emotional state;

[1156] a means for evaluating case information based on extracted concerns using a supervisor's individualized judgment model;

[1157] a means for transmitting and displaying the evaluation results on the subordinate's user terminal;

[1158] A means for subordinates to re-edit project information based on the evaluation results and upload it to the project management system;

[1159] A system including:

[1160] (Claim 2)

[1161] 10. The system of claim 1, further comprising means for storing the evaluation results in a database and storing the recompiled project information in a project management system.

[1162] (Claim 3)

[1163] 10. The system of claim 1, further comprising means for tracking project progress and notifying the supervisor's digital twin when significant changes or risks occur.

[1164] "Application example 2 when combining emotion engines"

[1165] (Claim 1)

[1166] means for providing a user interface for subordinates to input case information;

[1167] means for receiving input case information and extracting relevant concerns using a generative artificial intelligence model;

[1168] a means for using an emotion engine to recognize an emotional state of the worker;

[1169] a means for evaluating the case information based on the extracted concerns and sentiment information using an individualized judgment model of the supervisor;

[1170] a means for transmitting and displaying the evaluation results on the subordinate's user terminal;

[1171] A means for subordinates to re-edit project information based on the evaluation results and upload it to the project management system;

[1172] A system including:

[1173] (Claim 2)

[1174] 10. The system of claim 1, further comprising means for storing the evaluation results in a database and storing the recompiled project information in a project management system.

[1175] (Claim 3)

[1176] 10. The system of claim 1, further comprising means for tracking project progress and notifying the supervisor's digital twin when necessary. [Explanation of symbols]

[1177] 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. means for providing a user interface for subordinates to input case information; means for receiving input case information and extracting relevant concerns using a generative artificial intelligence model; a means for evaluating case information based on extracted concerns using a supervisor's individualized judgment model; a means for transmitting and displaying the evaluation results on the subordinate's user terminal; A means for subordinates to re-edit project information based on the evaluation results and upload it to the project management system; A system including:

2. 2. The system of claim 1, further comprising means for storing the evaluation results in a database and for storing the recompiled case information in a project management system.

3. 10. The system of claim 1, further comprising means for tracking project progress and notifying the supervisor's digital twin when necessary.

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