system

Abstract

We provide the system. [Solution] A means of analyzing requirements information using natural language processing technology, identifying ambiguous parts, and generating additional information, A means of collecting project progress data in a timely manner, analyzing the progress status, and predicting risks, A means of notifying users based on the analysis results, A means for evaluating resource usage and generating optimization proposals, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In project management, there are problems such as rework and efficiency decline due to ambiguity and omission in requirement definition. Also, since it is difficult to visualize the overall progress, there is a problem that it is difficult for a project manager to make appropriate decisions. Furthermore, since it is difficult to optimize resource allocation, there are problems such as wasted work and duplicate work. It is necessary to solve these problems and improve the accuracy and efficiency of project management.

Means for Solving the Problems

[0005] This invention provides a system that efficiently supports project management by using an information processing device. Specifically, it includes means for identifying ambiguous parts of requirement information using natural language processing technology and generating necessary additional information, and means for collecting and analyzing project progress data to predict risks. Furthermore, it visualizes progress by providing users with notifications based on the analysis results. In addition, it is a system that improves the efficiency of resource allocation by evaluating resource usage and presenting optimization proposals.

[0006] An "information processing device" is a mechanical or electronic device that has the ability to receive data, analyze it, and output results.

[0007] "Natural language processing technology" is a technology that enables computers to understand and process the language that humans use on a daily basis.

[0008] "Requirements information" refers to data that describes specific details regarding the functions and specifications necessary for carrying out a project or task.

[0009] "Ambiguous areas" refer to parts of requirements or specifications that may be interpreted differently or where uncertain elements exist.

[0010] "Progress data" refers to information that shows the progress and status of tasks in a project.

[0011] "Risk forecasting" is the act of anticipating potential problems or obstacles in the future and evaluating their likelihood and impact.

[0012] "Notification" refers to a means of informing users of important information or changes in status.

[0013] "Resource usage" refers to information about the current status of resources such as personnel, time, and funds being used within a project.

[0014] An "optimization plan" is a specific method or procedure proposed to improve the current situation. [Brief explanation of the drawing]

[0015] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine.

Embodiments for Carrying Out the Invention

[0016] An example of an embodiment of the system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0019] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0021] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0026] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0028] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0035] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0036] This invention is a system for enhancing efficiency in project management, centered around an information processing device that enables natural language processing, progress monitoring, and resource optimization. First, the user inputs project requirements information into the server via a terminal. The server receives this data and applies natural language processing technology to identify ambiguous parts within the requirements. Based on this analysis, the server generates any necessary additional information and presents it to the user via the terminal. The user can then clarify the requirements information based on this feedback.

[0037] Next, the terminal continuously collects project progress data and sends it to the server. The server uses this data to monitor the project's progress in real time. Using a risk prediction algorithm, it can detect risks inherent in the project's progress and notify the user in a timely manner. This notification may be displayed as a visual heatmap of the ongoing project, providing a format that the user can intuitively understand.

[0038] Furthermore, the server analyzes current resource usage and develops an optimal resource allocation plan. This involves using optimization algorithms to minimize wasted resource usage and duplicate task assignments. This optimization plan is provided to the user as a suggestion, allowing the user to adjust resource allocation based on it.

[0039] As a concrete example, in a software development project, if a user enters a requirement such as "I want to improve the interface of a new feature," the server will generate questions to confirm "specifically which parts need improvement." Also, if the server detects that a particular task is behind schedule during project progress, it will send a notification to the user stating, "Task A is 50% behind schedule. We recommend reallocating resources or revising the schedule."

[0040] The system of the present invention intelligently supports project requirements definition, progress management, and resource allocation, thereby improving the overall success rate of the project.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The user enters project requirements information into the terminal. The terminal receives this information and sends it to the server.

[0044] Step 2:

[0045] The server analyzes the received requirements information using natural language processing technology to identify ambiguous parts and areas that require further explanation.

[0046] Step 3:

[0047] The server generates additional questions and improvement suggestions based on the analysis results and provides them to the user via the terminal.

[0048] Step 4:

[0049] The user reviews the feedback from the server, updates the requirements information as needed, and resubmits it.

[0050] Step 5:

[0051] The terminal periodically collects project progress data from the project management tool and sends it to the server.

[0052] Step 6:

[0053] The server uses the collected progress data to analyze the project's progress in real time.

[0054] Step 7:

[0055] The server runs a risk prediction algorithm to assess the likelihood of project delays and problems.

[0056] Step 8:

[0057] The server generates notifications based on the evaluation results and provides users with risk alerts and progress updates via their devices.

[0058] Step 9:

[0059] The server analyzes the overall resource usage of the project and creates an optimal allocation plan.

[0060] Step 10:

[0061] The server proposes optimization plans to the user and prompts them to change their resource allocation.

[0062] Step 11:

[0063] The user reviews the server's proposal and adjusts resource allocation as needed.

[0064] (Example 1)

[0065] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0066] In project management, unclear requirements, delays in progress, and inefficiencies due to wasted resources are major challenges. Traditional systems struggle to identify and address these problems in real time, hindering project progress and ultimately lowering the success rate.

[0067] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0068] In this invention, the server includes means for analyzing requirements information using natural language processing technology, identifying ambiguous parts, and generating additional information; means for collecting progress data in a timely manner, analyzing the progress, and predicting risks; and means for evaluating resource usage and generating optimization proposals. This enables users to grasp the progress of projects in real time and address issues quickly and effectively.

[0069] An "information processing device" is a combination of hardware and software designed to receive and analyze data.

[0070] "Natural language processing technology" is a technology that allows computers to analyze human language and understand its meaning.

[0071] "Requirements information" refers to documents or data that specifically outline the project's goals and specifications.

[0072] "Ambiguous parts" refer to sections within the requirements information that are unclear and therefore open to multiple interpretations.

[0073] "Progress data" refers to information including numerical data and status reports that indicate the progress of a project.

[0074] "Risk forecasting" is a method of identifying potential problems that may arise in a project in advance and considering countermeasures.

[0075] "Resource usage" refers to the state of how resources such as personnel, equipment, and time are being used in a project.

[0076] An "optimized plan" is the best possible plan designed to efficiently utilize resources.

[0077] A "visualization map" is a chart or graphic used to visually display progress or analysis results in an easy-to-understand manner.

[0078] A "user" is a person or organization that operates the system and manages the project.

[0079] This invention is an information processing system for efficient project management. The server is primarily responsible for data analysis and resource optimization, and handles data exchange between the terminal and the user. Specifically, the server uses natural language processing technology to analyze project requirements information entered by the user through the terminal and identify ambiguities. This includes technology that utilizes generative AI models to generate additional information and questions to resolve ambiguities in the requirements.

[0080] In terms of hardware configuration, the server is designed as a computer device equipped with a high-performance processor and a large amount of memory, while the terminal is a device that handles the interface with the user. In terms of software, natural language processing is possible by incorporating a generative AI model into the analysis program that runs on the server.

[0081] As a concrete example, suppose a user enters "I want to improve the interface of the new feature" into their terminal. The server applies natural language processing to this requirement and generates specific questions such as "What specific parts need improvement?" and presents them to the user. Based on this feedback, the user clarifies the requirements and facilitates the efficient progress of the project.

[0082] Furthermore, the server aggregates progress data sent from terminals and predicts risks inherent in ongoing projects. This allows users to intuitively understand project progress and risks through visualized heatmaps. For example, the server can send a notification to the user stating, "Task A is 50% behind schedule," prompting appropriate resource reallocation.

[0083] An example of a prompt statement is: "Describe the means to identify ambiguous requirements in a project management system and generate additional information." This prompt is given to the generative AI model and forms the basis for prompting appropriate information generation.

[0084] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0085] Step 1:

[0086] The user enters project requirements information into a terminal and sends it to the server. The entered data represents the specific goals and specifications of the project. The server receives this data and uses natural language processing techniques to identify ambiguous parts in the text. This allows it to generate additional information necessary for interpreting the requirements information. The output at this point consists of the identified ambiguous parts and the generated questions and supplementary information.

[0087] Step 2:

[0088] The server sends the generated questions and additional information to the terminal and presents it to the user. The user then modifies the requirements information based on the presented information and sends the more precise requirements back to the server via the terminal. This allows the user to create a more precise project plan. The output of this step is the modified requirements information.

[0089] Step 3:

[0090] The terminal continuously collects project progress data and sends it to the server. This data includes the completion rate and progress status of each task. The server receives this data and analyzes the progress data in real time. Based on the input data, a risk prediction algorithm is applied to detect potential risks during the project. The output of this process is the risk analysis results and the progress evaluation results.

[0091] Step 4:

[0092] The server sends a risk notification to the user based on the analysis results. This notification includes the specific nature of the risk and recommended countermeasures. It also generates a visual heatmap of project progress and displays it on the terminal. The user can review this visual information and take countermeasures as needed, such as adjusting resource allocation. The output of this step is the notification sent to the user and the heatmap.

[0093] Step 5:

[0094] The server analyzes the overall resource usage of the project and generates an optimization plan. This analysis includes resources in use, personnel allocation, and the priority of each task. The server uses an optimization algorithm to propose an efficient resource allocation while eliminating waste. This new plan is presented to the user via a terminal and adjusted and applied as needed. The output of this step is the proposed optimization plan.

[0095] (Application Example 1)

[0096] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0097] In production project management within factory automation processes, project requirements are often unclear, progress is difficult to track visually, and resource allocation is not optimized. Furthermore, it is necessary to effectively utilize data from production equipment and continuously optimize the process.

[0098] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0099] In this invention, the server includes means for an information processing device to analyze requirements information using natural language processing technology, identify ambiguous parts, and generate additional information; means for an information processing device to collect project progress data in a timely manner, analyze the progress status, and predict risks; and means for an information processing device to aggregate and analyze data from production equipment in the factory automation process. This enables real-time visualization of the progress status of production projects, highly accurate risk prediction, and optimal resource allocation.

[0100] An "information processing device" is a computing device that analyzes input data to clarify requirements, monitor progress, and optimize resources.

[0101] "Natural language processing technology" refers to a set of technologies that enable computers to understand, analyze, and process human language.

[0102] "Ambiguous areas" refer to parts of the requirements information that are not clearly defined and are open to interpretation.

[0103] "Means for generating additional information" refers to methods and processes for providing users with the information necessary to resolve ambiguities.

[0104] "Project progress data" refers to information that shows the completion status and progress of each task in a project.

[0105] "Risk prediction" is the process of estimating and diagnosing potential difficulties and obstacles that may arise in a project.

[0106] An "optimization plan" refers to a plan or proposal designed to maximize the efficiency of resource utilization.

[0107] "Data from production equipment" refers to information such as operating status, utilization rate, and error reports obtained from various machines and sensors within the factory.

[0108] "Real-time visualization" means instantly representing the ongoing state visually so that users can understand it intuitively.

[0109] The system used to implement this application is primarily a program that operates via a server and a terminal. The server consists of a high-performance information processing unit and utilizes natural language processing libraries (e.g., spaCy and NLTK), database management systems (e.g., MySQL®), and data visualization tools (e.g., D3.js).

[0110] First, the user inputs project requirements information into the server using a terminal. The server uses natural language processing technology to analyze this requirements information, identifying particularly ambiguous parts and generating additional information to clarify them. This generated information is presented to the user via the terminal, allowing the user to clarify the project requirements based on it.

[0111] Next, the terminal continuously collects project progress data and sends it to the server. The server uses this data to monitor real-time progress and generate a visual heatmap. This data is very comprehensive because it is collected from various sensors within the factory, such as those on production equipment.

[0112] Furthermore, the server analyzes resource usage and provides the user with the most efficient resource allocation plan. By utilizing machine learning models based on historical data and performing risk predictions, more accurate predictions and suggestions become possible.

[0113] As a concrete example, when starting a "new product prototype project" in a factory, the user inputs the requirement "complete 10 prototypes by the weekend" via a terminal. During this time, the program collects progress data in real time and detects when a particular process is behind schedule. This allows the user to review resource allocation or adjust the schedule.

[0114] When using a generative AI model, users can enter prompts like the following to receive further analysis and suggestions: "Please tell me the predicted delay factors for the new product prototyping project, and suggest the optimal resource allocation to prevent those delays."

[0115] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0116] Step 1:

[0117] The user enters project requirements information via a terminal. This entered requirements information is sent to the server. Because this requirements information is in natural language format, it may contain ambiguous expressions.

[0118] Step 2:

[0119] The server analyzes the received requirements information using natural language processing techniques. The input for the analysis is the requirements information from the user, and the output is a report that identifies ambiguous parts and suggestions for additional information to clarify them. This process uses a natural language processing library to analyze unclear points within the requirements.

[0120] Step 3:

[0121] The server identifies ambiguous parts and sends additional information to the user via the terminal. The user then uses this information to refine and re-enter the requirements, or approves the additional information. The output of this process is the newly confirmed and revised requirements information.

[0122] Step 4:

[0123] The terminal resends these clarified requirements to the server and begins collecting project progress data. Progress data is automatically transmitted from various sensors and production equipment within the factory. This data is aggregated on the server in real time.

[0124] Step 5:

[0125] The server analyzes the received progress data to determine the project's progress. This analysis involves generating a heatmap as part of the data processing, visually representing the project's intuitive progress as output. It also performs risk prediction based on progress and identifies potential delay factors.

[0126] Step 6:

[0127] The server presents the user with a progress heatmap and risk prediction information via the terminal. The user can review the specific progress and make adjustments as needed. The output of this step is the project progress report and risk suggestions that the user receives.

[0128] Step 7:

[0129] The server analyzes resource usage and proposes the optimal allocation method. The input is current resource data, and the output is an optimized resource allocation proposal. This process uses a predictive model trained on historical project data to support efficient resource management.

[0130] Step 8:

[0131] The user approves or adjusts the provided resource allocation proposal. As a result, a new resource allocation plan is formulated, and the project proceeds based on it. The output is the updated project plan.

[0132] Throughout this entire process, the production process at the factory is efficiently managed. By utilizing superior data processing and analysis techniques, users can achieve effective project management.

[0133] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0134] This invention is a system aimed at improving efficiency and user experience in project management. The information processing device incorporates means for analyzing requirements information, collecting and analyzing progress data, and optimizing resource allocation, as well as an emotion engine for recognizing user emotions. When a user inputs requirements information using a terminal, the terminal sends it to a server, which analyzes the information using natural language processing technology. This identifies ambiguous areas and areas requiring improvement, and generates necessary additional information.

[0135] Project progress is analyzed in real time based on data sent from the terminal to the server. The server uses a risk prediction algorithm to assess potential risks related to progress and notifies the user. It can also generate a heatmap based on the analyzed data and present it visually to the user through the terminal. For resource optimization, the server evaluates resource usage and generates an efficient allocation plan. This information is proposed to the user, and if approved, resource allocation adjustments are made.

[0136] In particular, the present invention includes an emotion engine that can analyze the user's emotions in real time during interaction. Through this emotion analysis, the server detects the user's stress and anxiety and provides appropriate alerts and advice accordingly. Furthermore, based on user feedback, it is possible to adjust the interface's responses and support methods to provide a more personalized experience.

[0137] For example, if the emotion engine detects signs of stress while a user is using a project management tool, the server will then send an alert stating, "Your workload may be increasing. Consider reviewing your task priorities." In this way, the system is operated in a manner that considers both efficient project management and the psychological burden on the user.

[0138] The following describes the processing flow.

[0139] Step 1:

[0140] The user enters project requirements information into the terminal. The terminal collects the entered information and sends it to the server.

[0141] Step 2:

[0142] The server analyzes the received requirements information using natural language processing technology to identify ambiguous parts. The server then generates necessary additional information and improvement suggestions for the identified parts.

[0143] Step 3:

[0144] The server provides the user with additional questions and suggestions based on the analysis results via the terminal. This allows the user to clarify the requirements information, enter it into the terminal, and send it back to the server.

[0145] Step 4:

[0146] The terminal periodically collects project progress data and sends it to the server. This data includes task completion status, time logs, and progress targets.

[0147] Step 5:

[0148] The server analyzes progress data in real time and evaluates the progress status. Risk prediction algorithms are used to detect the possibility of delays and potential problems.

[0149] Step 6:

[0150] The server generates a heatmap based on progress analysis, providing users with a visual representation of the project's progress. Users can view this heatmap on their devices to understand the overall project status.

[0151] Step 7:

[0152] The server uses an emotion engine to recognize user emotions and detect user stress and anxiety. This enables flexible responses tailored to the user's psychological state.

[0153] Step 8:

[0154] Based on the results of sentiment analysis, the server generates appropriate alerts and advice for the user and notifies them through their device. For example, if the user is experiencing high levels of stress, it may suggest re-evaluating or interrupting tasks.

[0155] Step 9:

[0156] The server evaluates resource usage and creates optimization suggestions to prevent wasteful use and redundant tasks.

[0157] Step 10:

[0158] The server proposes an optimized resource allocation plan to the user and asks for their approval. If the user approves, the resource allocation is adjusted.

[0159] Step 11:

[0160] The user follows the new resource allocation and continues with project tasks. The device continuously updates data throughout this process and provides feedback to adapt to the new situation.

[0161] (Example 2)

[0162] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0163] Traditional project management systems suffer from problems such as ambiguity in requirements information and uncertainty in progress, which negatively impact project efficiency. Furthermore, they often lack management that considers user emotions and psychological burden, which can lead to a decline in overall project performance.

[0164] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0165] In this invention, the server includes means for analyzing requirements information using natural language processing technology, identifying ambiguous parts, and generating additional information; means for collecting progress data, analyzing the progress, and predicting risks; and means for evaluating the user's emotional state using an emotion analysis engine and providing appropriate alerts and advice. This makes it possible to improve the efficiency of project management and reduce the psychological burden on the user.

[0166] An "information processing device" refers to a system for processing and managing data, such as analyzing requirements information and tracking progress.

[0167] "Natural language processing technology" refers to the technology used to analyze and understand human language using computers.

[0168] "Requirements information" refers to data that specifies the specifications and conditions necessary for project execution.

[0169] "Ambiguous parts" refer to sections of information that lack clarity and may lead to misunderstandings.

[0170] "Additional information" refers to further information related to the project that is generated to fill in any ambiguities.

[0171] "Progress data" refers to information that shows the progress of a project.

[0172] "Risk prediction" refers to the act of anticipating potential problems that may arise in a project.

[0173] "Analysis results" refer to the insights and conclusions obtained after analyzing data.

[0174] "Notification" refers to the act of informing a user of important information or warnings from an information processing device.

[0175] "Resources" refer to the personnel, equipment, time, and other elements necessary to carry out a project.

[0176] An "optimization plan" refers to methods or strategies proposed to maximize the efficiency of resource use.

[0177] An "emotion analysis engine" refers to technology used to analyze human emotions and identify their state.

[0178] An "alert" refers to a notification designed to draw the user's attention to urgent information.

[0179] "Advice" refers to suggestions or proposals provided to users to encourage improvement or countermeasures.

[0180] This invention is an information processing system for efficient project management. The entire system consists of a server acting as an information processing device and a terminal operated by the user. The server has advanced data analysis capabilities, and the terminal receives input from the user and receives feedback from the server as needed.

[0181] The server analyzes the requirements information entered by the user on the terminal, based on natural language processing technology. This analysis utilizes generative AI models (e.g., BERT or GPT-3®). The server identifies ambiguous descriptions and generates additional information to supplement them. This process clarifies the project requirements.

[0182] Furthermore, the server monitors project progress in real time and analyzes progress data. Terminals periodically send progress data to the server, which uses data analysis techniques (e.g., machine learning algorithms) to predict risks. Once the server identifies risks, it sends alerts to the user and generates optimized resource allocation suggestions as needed.

[0183] This system can also have the server evaluate the user's emotional state via an emotion analysis engine and provide corresponding advice and alerts. For example, if stress is detected on the user's device, the server will send an alert such as, "Your workload may be increasing. Consider reviewing your task priorities."

[0184] For example, by inputting a prompt such as, "Assess the project's progress risks and suggest what alerts should be issued to users if necessary," the AI ​​model will generate the steps needed to achieve the objective.

[0185] In this way, the invention aims to simultaneously improve the efficiency of project management and reduce the psychological burden on users.

[0186] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0187] Step 1:

[0188] The user enters project requirements information into the terminal. The terminal sends this information to the server. The input consists of text information such as a project overview and required functions. The terminal sends the information using an HTTP POST request. The server receives this information.

[0189] Step 2:

[0190] The server analyzes the received requirements information. This analysis utilizes generative AI models (e.g., BERT or GPT-3). The server leverages natural language processing algorithms to identify ambiguous descriptions and missing information, and generates the necessary additional information. The input is text information, and the output is clearly completed requirements information.

[0191] Step 3:

[0192] The terminal periodically sends data about the project's progress to the server. This data includes the completion status of tasks and resource usage. The server receives the progress data and stores it in a database.

[0193] Step 4:

[0194] The server analyzes progress based on historical and progress data. It uses machine learning algorithms to predict risks. The input is progress data, and the analysis results in risk prediction information.

[0195] Step 5:

[0196] Based on the progress analysis results, the server generates a heatmap to visually represent the progress for each area. The server uses a data visualization tool to create this heatmap and sends it to the terminal. The input is the analysis results, and the output is the heatmap.

[0197] Step 6:

[0198] The server generates an optimal resource allocation plan based on risk predictions and resource usage. This allocation plan is then presented to the user. The server notifies the user of the proposal to confirm whether the user approves it or requests modifications. The input is risk information and resource information, and the output is the resource allocation plan.

[0199] Step 7:

[0200] The server uses an emotion analysis engine to analyze user interactions from the device in real time and evaluate their emotional state. If stress or anxiety is detected, the server sends specific advice or alerts to the user. The input is user interaction data, and the output is advice or warning messages.

[0201] (Application Example 2)

[0202] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0203] Current project management systems and citizen service applications lack the ability to analyze user emotions in real time and provide appropriate support based on their psychological state. Furthermore, there is a lack of effective ways to utilize citizen feedback when optimizing resource allocation, which can easily lead to accumulated user dissatisfaction and a decline in service quality. Moreover, accurately understanding the usage of public services and allocating resources appropriately is crucial.

[0204] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0205] In this invention, the server includes means for analyzing requirements information using natural language processing technology, means for analyzing the user's psychological state using emotion recognition functionality, and means for optimizing resource allocation based on citizen feedback. This makes it possible to provide appropriate support in accordance with the user's emotions and improve the quality of citizen services.

[0206] An "information processing device" is an electronic device that has the function of managing, analyzing, and processing data.

[0207] "Natural language processing technology" is a technology that enables computers to understand and analyze human language.

[0208] "Requirements information" refers to information that indicates the conditions and specifications required for a project or system.

[0209] "Analysis results" are conclusions drawn by analyzing data and understanding its content and meaning.

[0210] "Resources" refer to elements necessary to carry out a project or task, such as personnel, equipment, and time.

[0211] "Emotion recognition function" is a technology that analyzes the user's psychological state from their facial expressions, behavior, and feedback.

[0212] "Feedback" refers to the opinions, impressions, evaluations, and other reactions provided by users.

[0213] "Optimization" is the pursuit of the most effective and efficient state for a specific purpose.

[0214] To implement this invention, it is necessary to build a system in which a server and a terminal exchange data with each other. The server acts as an information processing device, using natural language processing technology to analyze requirements information received from the user, identify ambiguous parts, and generate necessary additional information. The server also receives progress data of ongoing projects from the terminal, analyzes the progress in real time, and predicts risks. A risk prediction algorithm is used for this purpose.

[0215] Based on the analysis results, the server provides appropriate notifications to the user. These notifications include generating and displaying a heatmap that visually shows the project's progress. This heatmap is designed to intuitively communicate the progress to the user. Furthermore, the server analyzes the user's psychological state obtained through user feedback and emotion recognition functions, provides appropriate warnings and advice, and generates optimization suggestions based on resource usage.

[0216] The terminal is a device for users to input requirements information and feedback, and it sends the data to the server. Through the terminal, users can check the progress of the project and receive alerts and advice based on sentiment analysis. Cameras and sensors are used for sentiment recognition, and feedback is entered in text format.

[0217] As a concrete example, there is a process where citizens input feedback on public services into a terminal, and if the comments indicate negative sentiment, the server analyzes them to improve the service. An example of a prompt in this case would be, "As a citizen of a smart city, please provide feedback to improve public transportation services. Please perform sentiment analysis on the feedback, detect negative opinions, and suggest necessary improvements."

[0218] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0219] Step 1:

[0220] The terminal receives requirements information and feedback from the user. The input is in text format, and the user describes the project or public service. The terminal sends this to the server, which uses natural language processing technology to analyze the data and identify ambiguities. The output generates a list of ambiguous requirements and any necessary additional information.

[0221] Step 2:

[0222] The server receives progress data sent from the terminal. This data includes project progress and public opinion regarding public services. The server uses a risk prediction algorithm to analyze the ongoing situation and assess potential risks. The output consists of the risk assessment results and a list of recommended actions based on those results.

[0223] Step 3:

[0224] Users can receive these risk assessment results through their devices. The server also generates a heatmap visually representing the progress and sends it to the device. Users can intuitively understand the progress through this heatmap. Visualized progress data is provided as output.

[0225] Step 4:

[0226] The server uses data from terminals and sensors to analyze the user's psychological state in real time. Through emotion recognition, it identifies stress and frustration and generates appropriate warnings and advice. The output is psychological state-based alerts and advice provided to the user.

[0227] Step 5:

[0228] The server evaluates resource usage based on the analyzed data and generates optimization suggestions. These suggestions improve resource allocation and enhance the efficiency of projects and public services. The resource optimization suggestions are then generated and presented to the user as output.

[0229] Step 6:

[0230] The user reviews and approves the optimization proposal. Once the approval is sent to the server via the terminal, the server adjusts the resource allocation. This ensures that project and public service resources are used efficiently. The output shows the execution status of the approved resource allocation.

[0231] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0232] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0233] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0234] [Second Embodiment]

[0235] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0236] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0237] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0238] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0239] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0240] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0241] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0242] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0243] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0244] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0245] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0246] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0247] This invention is a system for enhancing efficiency in project management, centered around an information processing device that enables natural language processing, progress monitoring, and resource optimization. First, the user inputs project requirements information into the server via a terminal. The server receives this data and applies natural language processing technology to identify ambiguous parts within the requirements. Based on this analysis, the server generates any necessary additional information and presents it to the user via the terminal. The user can then clarify the requirements information based on this feedback.

[0248] Next, the terminal continuously collects project progress data and sends it to the server. The server uses this data to monitor the project's progress in real time. Using a risk prediction algorithm, it can detect risks inherent in the project's progress and notify the user in a timely manner. This notification may be displayed as a visual heatmap of the ongoing project, providing a format that the user can intuitively understand.

[0249] Furthermore, the server analyzes current resource usage and develops an optimal resource allocation plan. This involves using optimization algorithms to minimize wasted resource usage and duplicate task assignments. This optimization plan is provided to the user as a suggestion, allowing the user to adjust resource allocation based on it.

[0250] As a concrete example, in a software development project, if a user enters a requirement such as "I want to improve the interface of a new feature," the server will generate questions to confirm "specifically which parts need improvement." Also, if the server detects that a particular task is behind schedule during project progress, it will send a notification to the user stating, "Task A is 50% behind schedule. We recommend reallocating resources or revising the schedule."

[0251] The system of the present invention intelligently supports project requirements definition, progress management, and resource allocation, thereby improving the overall success rate of the project.

[0252] The following describes the processing flow.

[0253] Step 1:

[0254] The user enters project requirements information into the terminal. The terminal receives this information and sends it to the server.

[0255] Step 2:

[0256] The server analyzes the received requirements information using natural language processing technology to identify ambiguous parts and areas that require further explanation.

[0257] Step 3:

[0258] The server generates additional questions and improvement suggestions based on the analysis results and provides them to the user via the terminal.

[0259] Step 4:

[0260] The user reviews the feedback from the server, updates the requirements information as needed, and resubmits it.

[0261] Step 5:

[0262] The terminal periodically collects project progress data from the project management tool and sends it to the server.

[0263] Step 6:

[0264] The server uses the collected progress data to analyze the project's progress in real time.

[0265] Step 7:

[0266] The server runs a risk prediction algorithm to assess the likelihood of project delays and problems.

[0267] Step 8:

[0268] The server generates notifications based on the evaluation results and provides users with risk alerts and progress updates via their devices.

[0269] Step 9:

[0270] The server analyzes the overall resource usage of the project and creates an optimal allocation plan.

[0271] Step 10:

[0272] The server proposes optimization plans to the user and prompts them to change their resource allocation.

[0273] Step 11:

[0274] The user reviews the server's proposal and adjusts resource allocation as needed.

[0275] (Example 1)

[0276] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0277] In project management, unclear requirements, delays in progress, and inefficiencies due to wasted resources are major challenges. Traditional systems struggle to identify and address these problems in real time, hindering project progress and ultimately lowering the success rate.

[0278] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0279] In this invention, the server includes means for analyzing requirements information using natural language processing technology, identifying ambiguous parts, and generating additional information; means for collecting progress data in a timely manner, analyzing the progress, and predicting risks; and means for evaluating resource usage and generating optimization proposals. This enables users to grasp the progress of projects in real time and address issues quickly and effectively.

[0280] An "information processing device" is a combination of hardware and software designed to receive and analyze data.

[0281] "Natural language processing technology" refers to the technology that enables a computer to analyze and understand the language used by humans.

[0282] "Requirement information" refers to documents or data that specifically indicate the goals and specifications of a project.

[0283] "Ambiguous parts" refer to parts in the requirement information that are unclear and thus have multiple possible interpretations.

[0284] "Progress data" refers to information that includes numerical values indicating the progress of a project and status reports.

[0285] "Risk prediction" refers to a method of identifying potential problems that may occur in a project in advance and considering countermeasures.

[0286] "Resource usage status" refers to the state indicating how resources such as human resources, equipment, and time in a project are utilized.

[0287] "Optimization plan" refers to the best plan constructed aiming at the efficient utilization of resources.

[0288] "Visualization map" refers to charts and graphics for visually and clearly displaying the progress and analysis results.

[0289] "User" refers to a person or organization that operates the system and conducts project management.

[0290] This invention is an information processing system for efficiently conducting project management. The server is mainly responsible for data analysis and resource optimization, and exchanges data between the terminal and the user. Specifically, the server uses natural language processing technology to analyze the project requirement information input by the user through the terminal and identify ambiguous parts. This includes technologies that utilize generative AI models to generate additional information and questions for resolving the ambiguity of requirements.

[0291] In terms of hardware configuration, the server is designed as a computer device equipped with a high-performance processor and a large amount of memory, while the terminal is a device that handles the interface with the user. In terms of software, natural language processing is possible by incorporating a generative AI model into the analysis program that runs on the server.

[0292] As a concrete example, suppose a user enters "I want to improve the interface of the new feature" into their terminal. The server applies natural language processing to this requirement and generates specific questions such as "What specific parts need improvement?" and presents them to the user. Based on this feedback, the user clarifies the requirements and facilitates the efficient progress of the project.

[0293] Furthermore, the server aggregates progress data sent from terminals and predicts risks inherent in ongoing projects. This allows users to intuitively understand project progress and risks through visualized heatmaps. For example, the server can send a notification to the user stating, "Task A is 50% behind schedule," prompting appropriate resource reallocation.

[0294] An example of a prompt statement is: "Describe the means to identify ambiguous requirements in a project management system and generate additional information." This prompt is given to the generative AI model and forms the basis for prompting appropriate information generation.

[0295] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0296] Step 1:

[0297] The user enters project requirements information into a terminal and sends it to the server. The entered data represents the specific goals and specifications of the project. The server receives this data and uses natural language processing techniques to identify ambiguous parts in the text. This allows it to generate additional information necessary for interpreting the requirements information. The output at this point consists of the identified ambiguous parts and the generated questions and supplementary information.

[0298] Step 2:

[0299] The server sends the generated questions and additional information to the terminal and presents it to the user. The user then modifies the requirements information based on the presented information and sends the more precise requirements back to the server via the terminal. This allows the user to create a more precise project plan. The output of this step is the modified requirements information.

[0300] Step 3:

[0301] The terminal continuously collects project progress data and sends it to the server. This data includes the completion rate and progress status of each task. The server receives this data and analyzes the progress data in real time. Based on the input data, a risk prediction algorithm is applied to detect potential risks during the project. The output of this process is the risk analysis results and the progress evaluation results.

[0302] Step 4:

[0303] The server sends a risk notification to the user based on the analysis results. This notification includes the specific nature of the risk and recommended countermeasures. It also generates a visual heatmap of project progress and displays it on the terminal. The user can review this visual information and take countermeasures as needed, such as adjusting resource allocation. The output of this step is the notification sent to the user and the heatmap.

[0304] Step 5:

[0305] The server analyzes the resource usage status of the entire project and generates an optimization plan. This analysis includes the resources in use, personnel allocation, and the priority of each task. The server uses an optimization algorithm to propose an efficient resource allocation while eliminating waste. This new plan is presented to the user through the terminal and adjusted / applied as needed. The output of this step is the proposed optimization plan.

[0306] (Application Example 1)

[0307] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0308] In the production project management in the factory automation process, the project requirements are often unclear, visual management of progress is difficult, and resource allocation is not optimized. Furthermore, it is necessary to effectively utilize the data from production facilities and continuously optimize.

[0309] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0310] In this invention, the server includes means for the information processing device to analyze requirement information using natural language processing technology, identify ambiguous parts and generate additional information, means for the information processing device to collect project progress data in a timely manner, analyze the progress status and perform risk prediction, and means for the information processing device to aggregate and analyze data from production facilities in the factory automation process. Thereby, the progress status of the production project can be visualized in real time, enabling highly accurate risk prediction and optimal resource allocation.

[0311] The "information processing device" is a computer device for analyzing the input data, clarifying requirements, monitoring progress status, and optimizing resources.

[0312] "Natural language processing technology" refers to a set of technologies that enable computers to understand, analyze, and process human language.

[0313] "Ambiguous areas" refer to parts of the requirements information that are not clearly defined and are open to interpretation.

[0314] "Means for generating additional information" refers to methods and processes for providing users with the information necessary to resolve ambiguities.

[0315] "Project progress data" refers to information that shows the completion status and progress of each task in a project.

[0316] "Risk prediction" is the process of estimating and diagnosing potential difficulties and obstacles that may arise in a project.

[0317] An "optimization plan" refers to a plan or proposal designed to maximize the efficiency of resource utilization.

[0318] "Data from production equipment" refers to information such as operating status, utilization rate, and error reports obtained from various machines and sensors within the factory.

[0319] "Real-time visualization" means instantly representing the ongoing state visually so that users can understand it intuitively.

[0320] The system used to implement this application is primarily a program that operates via a server and a terminal. The server consists of a high-performance information processing unit and utilizes natural language processing libraries (e.g., spaCy and NLTK), database management systems (e.g., MySQL), and data visualization tools (e.g., D3.js).

[0321] First, the user inputs project requirements information into the server using a terminal. The server uses natural language processing technology to analyze this requirements information, identifying particularly ambiguous parts and generating additional information to clarify them. This generated information is presented to the user via the terminal, allowing the user to clarify the project requirements based on it.

[0322] Next, the terminal continuously collects project progress data and sends it to the server. The server uses this data to monitor real-time progress and generate a visual heatmap. This data is very comprehensive because it is collected from various sensors within the factory, such as those on production equipment.

[0323] Furthermore, the server analyzes resource usage and provides the user with the most efficient resource allocation plan. By utilizing machine learning models based on historical data and performing risk predictions, more accurate predictions and suggestions become possible.

[0324] As a concrete example, when starting a "new product prototype project" in a factory, the user inputs the requirement "complete 10 prototypes by the weekend" via a terminal. During this time, the program collects progress data in real time and detects when a particular process is behind schedule. This allows the user to review resource allocation or adjust the schedule.

[0325] When using a generative AI model, users can enter prompts like the following to receive further analysis and suggestions: "Please tell me the predicted delay factors for the new product prototyping project, and suggest the optimal resource allocation to prevent those delays."

[0326] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0327] Step 1:

[0328] The user enters project requirements information via a terminal. This entered requirements information is sent to the server. Because this requirements information is in natural language format, it may contain ambiguous expressions.

[0329] Step 2:

[0330] The server analyzes the received requirements information using natural language processing techniques. The input for the analysis is the requirements information from the user, and the output is a report that identifies ambiguous parts and suggestions for additional information to clarify them. This process uses a natural language processing library to analyze unclear points within the requirements.

[0331] Step 3:

[0332] The server identifies ambiguous parts and sends additional information to the user via the terminal. The user then uses this information to refine and re-enter the requirements, or approves the additional information. The output of this process is the newly confirmed and revised requirements information.

[0333] Step 4:

[0334] The terminal resends these clarified requirements to the server and begins collecting project progress data. Progress data is automatically transmitted from various sensors and production equipment within the factory. This data is aggregated on the server in real time.

[0335] Step 5:

[0336] The server analyzes the received progress data to determine the project's progress. This analysis involves generating a heatmap as part of the data processing, visually representing the project's intuitive progress as output. It also performs risk prediction based on progress and identifies potential delay factors.

[0337] Step 6:

[0338] The server presents the user with a progress heatmap and risk prediction information via the terminal. The user can review the specific progress and make adjustments as needed. The output of this step is the project progress report and risk suggestions that the user receives.

[0339] Step 7:

[0340] The server analyzes resource usage and proposes the optimal allocation method. The input is current resource data, and the output is an optimized resource allocation proposal. This process uses a predictive model trained on historical project data to support efficient resource management.

[0341] Step 8:

[0342] The user approves or adjusts the provided resource allocation proposal. As a result, a new resource allocation plan is formulated, and the project proceeds based on it. The output is the updated project plan.

[0343] Throughout this entire process, the production process at the factory is efficiently managed. By utilizing superior data processing and analysis techniques, users can achieve effective project management.

[0344] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0345] This invention is a system aimed at improving efficiency and user experience in project management. The information processing device incorporates means for analyzing requirements information, collecting and analyzing progress data, and optimizing resource allocation, as well as an emotion engine for recognizing user emotions. When a user inputs requirements information using a terminal, the terminal sends it to a server, which analyzes the information using natural language processing technology. This identifies ambiguous areas and areas requiring improvement, and generates necessary additional information.

[0346] Project progress is analyzed in real time based on data sent from the terminal to the server. The server uses a risk prediction algorithm to assess potential risks related to progress and notifies the user. It can also generate a heatmap based on the analyzed data and present it visually to the user through the terminal. For resource optimization, the server evaluates resource usage and generates an efficient allocation plan. This information is proposed to the user, and if approved, resource allocation adjustments are made.

[0347] In particular, the present invention includes an emotion engine that can analyze the user's emotions in real time during interaction. Through this emotion analysis, the server detects the user's stress and anxiety and provides appropriate alerts and advice accordingly. Furthermore, based on user feedback, it is possible to adjust the interface's responses and support methods to provide a more personalized experience.

[0348] For example, if the emotion engine detects signs of stress while a user is using a project management tool, the server will then send an alert stating, "Your workload may be increasing. Consider reviewing your task priorities." In this way, the system is operated in a manner that considers both efficient project management and the psychological burden on the user.

[0349] The following describes the processing flow.

[0350] Step 1:

[0351] The user enters project requirements information into the terminal. The terminal collects the entered information and sends it to the server.

[0352] Step 2:

[0353] The server analyzes the received requirements information using natural language processing technology to identify ambiguous parts. The server then generates necessary additional information and improvement suggestions for the identified parts.

[0354] Step 3:

[0355] The server provides the user with additional questions and suggestions based on the analysis results via the terminal. This allows the user to clarify the requirements information, enter it into the terminal, and send it back to the server.

[0356] Step 4:

[0357] The terminal periodically collects project progress data and sends it to the server. This data includes task completion status, time logs, and progress targets.

[0358] Step 5:

[0359] The server analyzes progress data in real time and evaluates the progress status. Risk prediction algorithms are used to detect the possibility of delays and potential problems.

[0360] Step 6:

[0361] The server generates a heatmap based on progress analysis, providing users with a visual representation of the project's progress. Users can view this heatmap on their devices to understand the overall project status.

[0362] Step 7:

[0363] The server uses an emotion engine to recognize user emotions and detect user stress and anxiety. This enables flexible responses tailored to the user's psychological state.

[0364] Step 8:

[0365] Based on the results of sentiment analysis, the server generates appropriate alerts and advice for the user and notifies them through their device. For example, if the user is experiencing high levels of stress, it may suggest re-evaluating or interrupting tasks.

[0366] Step 9:

[0367] The server evaluates resource usage and creates optimization suggestions to prevent wasteful use and redundant tasks.

[0368] Step 10:

[0369] The server proposes an optimized resource allocation plan to the user and asks for their approval. If the user approves, the resource allocation is adjusted.

[0370] Step 11:

[0371] The user follows the new resource allocation and continues with project tasks. The device continuously updates data throughout this process and provides feedback to adapt to the new situation.

[0372] (Example 2)

[0373] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0374] Traditional project management systems suffer from problems such as ambiguity in requirements information and uncertainty in progress, which negatively impact project efficiency. Furthermore, they often lack management that considers user emotions and psychological burden, which can lead to a decline in overall project performance.

[0375] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0376] In this invention, the server includes means for analyzing requirements information using natural language processing technology, identifying ambiguous parts, and generating additional information; means for collecting progress data, analyzing the progress, and predicting risks; and means for evaluating the user's emotional state using an emotion analysis engine and providing appropriate alerts and advice. This makes it possible to improve the efficiency of project management and reduce the psychological burden on the user.

[0377] An "information processing device" refers to a system for processing and managing data, such as analyzing requirements information and tracking progress.

[0378] "Natural language processing technology" refers to the technology used to analyze and understand human language using computers.

[0379] "Requirements information" refers to data that specifies the specifications and conditions necessary for project execution.

[0380] "Ambiguous parts" refer to sections of information that lack clarity and may lead to misunderstandings.

[0381] "Additional information" refers to further information related to the project that is generated to fill in any ambiguities.

[0382] "Progress data" refers to information that shows the progress of a project.

[0383] "Risk prediction" refers to the act of anticipating potential problems that may arise in a project.

[0384] "Analysis results" refer to the insights and conclusions obtained after analyzing data.

[0385] "Notification" refers to the act of informing a user of important information or warnings from an information processing device.

[0386] "Resources" refer to the personnel, equipment, time, and other elements necessary to carry out a project.

[0387] An "optimization plan" refers to methods or strategies proposed to maximize the efficiency of resource use.

[0388] An "emotion analysis engine" refers to technology used to analyze human emotions and identify their state.

[0389] An "alert" refers to a notification designed to draw the user's attention to urgent information.

[0390] "Advice" refers to suggestions or proposals provided to users to encourage improvement or countermeasures.

[0391] This invention is an information processing system for efficient project management. The entire system consists of a server acting as an information processing device and a terminal operated by the user. The server has advanced data analysis capabilities, and the terminal receives input from the user and receives feedback from the server as needed.

[0392] The server analyzes the requirements information entered by the user on the terminal, based on natural language processing technology. This analysis utilizes generative AI models (e.g., BERT or GPT-3). The server identifies ambiguous descriptions and generates additional information to fill them in. This process clarifies the project requirements.

[0393] Furthermore, the server monitors project progress in real time and analyzes progress data. Terminals periodically send progress data to the server, which uses data analysis techniques (e.g., machine learning algorithms) to predict risks. Once the server identifies risks, it sends alerts to the user and generates optimized resource allocation suggestions as needed.

[0394] This system can also have the server evaluate the user's emotional state via an emotion analysis engine and provide corresponding advice and alerts. For example, if stress is detected on the user's device, the server will send an alert such as, "Your workload may be increasing. Consider reviewing your task priorities."

[0395] For example, by inputting a prompt such as, "Assess the project's progress risks and suggest what alerts should be issued to users if necessary," the AI ​​model will generate the steps needed to achieve the objective.

[0396] In this way, the invention aims to simultaneously improve the efficiency of project management and reduce the psychological burden on users.

[0397] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0398] Step 1:

[0399] The user enters project requirements information into the terminal. The terminal sends this information to the server. The input consists of text information such as a project overview and required functions. The terminal sends the information using an HTTP POST request. The server receives this information.

[0400] Step 2:

[0401] The server analyzes the received requirements information. This analysis utilizes generative AI models (e.g., BERT or GPT-3). The server leverages natural language processing algorithms to identify ambiguous descriptions and missing information, and generates the necessary additional information. The input is text information, and the output is clearly completed requirements information.

[0402] Step 3:

[0403] The terminal periodically sends data about the project's progress to the server. This data includes the completion status of tasks and resource usage. The server receives the progress data and stores it in a database.

[0404] Step 4:

[0405] The server analyzes progress based on historical and progress data. It uses machine learning algorithms to predict risks. The input is progress data, and the analysis results in risk prediction information.

[0406] Step 5:

[0407] Based on the progress analysis results, the server generates a heatmap to visually represent the progress for each area. The server uses a data visualization tool to create this heatmap and sends it to the terminal. The input is the analysis results, and the output is the heatmap.

[0408] Step 6:

[0409] The server generates an optimal resource allocation plan based on risk predictions and resource usage. This allocation plan is then presented to the user. The server notifies the user of the proposal to confirm whether the user approves it or requests modifications. The input is risk information and resource information, and the output is the resource allocation plan.

[0410] Step 7:

[0411] The server uses an emotion analysis engine to analyze user interactions from the device in real time and evaluate their emotional state. If stress or anxiety is detected, the server sends specific advice or alerts to the user. The input is user interaction data, and the output is advice or warning messages.

[0412] (Application Example 2)

[0413] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0414] Current project management systems and citizen service applications lack the ability to analyze user emotions in real time and provide appropriate support based on their psychological state. Furthermore, there is a lack of effective ways to utilize citizen feedback when optimizing resource allocation, which can easily lead to accumulated user dissatisfaction and a decline in service quality. Moreover, accurately understanding the usage of public services and allocating resources appropriately is crucial.

[0415] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0416] In this invention, the server includes means for analyzing requirements information using natural language processing technology, means for analyzing the user's psychological state using emotion recognition functionality, and means for optimizing resource allocation based on citizen feedback. This makes it possible to provide appropriate support in accordance with the user's emotions and improve the quality of citizen services.

[0417] An "information processing device" is an electronic device that has the function of managing, analyzing, and processing data.

[0418] "Natural language processing technology" is a technology that enables computers to understand and analyze human language.

[0419] "Requirements information" refers to information that indicates the conditions and specifications required for a project or system.

[0420] "Analysis results" are conclusions drawn by analyzing data and understanding its content and meaning.

[0421] "Resources" refer to elements necessary to carry out a project or task, such as personnel, equipment, and time.

[0422] "Emotion recognition function" is a technology that analyzes the user's psychological state from their facial expressions, behavior, and feedback.

[0423] "Feedback" refers to the opinions, impressions, evaluations, and other reactions provided by users.

[0424] "Optimization" is the pursuit of the most effective and efficient state for a specific purpose.

[0425] To implement this invention, it is necessary to build a system in which a server and a terminal exchange data with each other. The server acts as an information processing device, using natural language processing technology to analyze requirements information received from the user, identify ambiguous parts, and generate necessary additional information. The server also receives progress data of ongoing projects from the terminal, analyzes the progress in real time, and predicts risks. A risk prediction algorithm is used for this purpose.

[0426] Based on the analysis results, the server provides appropriate notifications to the user. These notifications include generating and displaying a heatmap that visually shows the project's progress. This heatmap is designed to intuitively communicate the progress to the user. Furthermore, the server analyzes the user's psychological state obtained through user feedback and emotion recognition functions, provides appropriate warnings and advice, and generates optimization suggestions based on resource usage.

[0427] The terminal is a device for users to input requirements information and feedback, and it sends the data to the server. Through the terminal, users can check the progress of the project and receive alerts and advice based on sentiment analysis. Cameras and sensors are used for sentiment recognition, and feedback is entered in text format.

[0428] As a concrete example, there is a process where citizens input feedback on public services into a terminal, and if the comments indicate negative sentiment, the server analyzes them to improve the service. An example of a prompt in this case would be, "As a citizen of a smart city, please provide feedback to improve public transportation services. Please perform sentiment analysis on the feedback, detect negative opinions, and suggest necessary improvements."

[0429] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0430] Step 1:

[0431] The terminal receives requirements information and feedback from the user. The input is in text format, and the user describes the project or public service. The terminal sends this to the server, which uses natural language processing technology to analyze the data and identify ambiguities. The output generates a list of ambiguous requirements and any necessary additional information.

[0432] Step 2:

[0433] The server receives progress data sent from the terminal. This data includes project progress and public opinion regarding public services. The server uses a risk prediction algorithm to analyze the ongoing situation and assess potential risks. The output consists of the risk assessment results and a list of recommended actions based on those results.

[0434] Step 3:

[0435] Users can receive these risk assessment results through their devices. The server also generates a heatmap visually representing the progress and sends it to the device. Users can intuitively understand the progress through this heatmap. Visualized progress data is provided as output.

[0436] Step 4:

[0437] The server uses data from terminals and sensors to analyze the user's psychological state in real time. Through emotion recognition, it identifies stress and frustration and generates appropriate warnings and advice. The output is psychological state-based alerts and advice provided to the user.

[0438] Step 5:

[0439] The server evaluates resource usage based on the analyzed data and generates optimization suggestions. These suggestions improve resource allocation and enhance the efficiency of projects and public services. The resource optimization suggestions are then generated and presented to the user as output.

[0440] Step 6:

[0441] The user reviews and approves the optimization proposal. Once the approval is sent to the server via the terminal, the server adjusts the resource allocation. This ensures that project and public service resources are used efficiently. The output shows the execution status of the approved resource allocation.

[0442] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0443] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0444] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0445] [Third Embodiment]

[0446] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0447] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0448] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0449] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0450] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0451] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0452] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0453] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0454] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0455] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0456] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0457] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0458] This invention is a system for enhancing efficiency in project management, centered around an information processing device that enables natural language processing, progress monitoring, and resource optimization. First, the user inputs project requirements information into the server via a terminal. The server receives this data and applies natural language processing technology to identify ambiguous parts within the requirements. Based on this analysis, the server generates any necessary additional information and presents it to the user via the terminal. The user can then clarify the requirements information based on this feedback.

[0459] Next, the terminal continuously collects project progress data and sends it to the server. The server uses this data to monitor the project's progress in real time. Using a risk prediction algorithm, it can detect risks inherent in the project's progress and notify the user in a timely manner. This notification may be displayed as a visual heatmap of the ongoing project, providing a format that the user can intuitively understand.

[0460] Furthermore, the server analyzes current resource usage and develops an optimal resource allocation plan. This involves using optimization algorithms to minimize wasted resource usage and duplicate task assignments. This optimization plan is provided to the user as a suggestion, allowing the user to adjust resource allocation based on it.

[0461] As a concrete example, in a software development project, if a user enters a requirement such as "I want to improve the interface of a new feature," the server will generate questions to confirm "specifically which parts need improvement." Also, if the server detects that a particular task is behind schedule during project progress, it will send a notification to the user stating, "Task A is 50% behind schedule. We recommend reallocating resources or revising the schedule."

[0462] The system of the present invention intelligently supports project requirements definition, progress management, and resource allocation, thereby improving the overall success rate of the project.

[0463] The following describes the processing flow.

[0464] Step 1:

[0465] The user enters project requirements information into the terminal. The terminal receives this information and sends it to the server.

[0466] Step 2:

[0467] The server analyzes the received requirements information using natural language processing technology to identify ambiguous parts and areas that require further explanation.

[0468] Step 3:

[0469] The server generates additional questions and improvement suggestions based on the analysis results and provides them to the user via the terminal.

[0470] Step 4:

[0471] The user reviews the feedback from the server, updates the requirements information as needed, and resubmits it.

[0472] Step 5:

[0473] The terminal periodically collects project progress data from the project management tool and sends it to the server.

[0474] Step 6:

[0475] The server uses the collected progress data to analyze the project's progress in real time.

[0476] Step 7:

[0477] The server runs a risk prediction algorithm to assess the likelihood of project delays and problems.

[0478] Step 8:

[0479] The server generates notifications based on the evaluation results and provides users with risk alerts and progress updates via their devices.

[0480] Step 9:

[0481] The server analyzes the overall resource usage of the project and creates an optimal allocation plan.

[0482] Step 10:

[0483] The server proposes optimization plans to the user and prompts them to change their resource allocation.

[0484] Step 11:

[0485] The user reviews the server's proposal and adjusts resource allocation as needed.

[0486] (Example 1)

[0487] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0488] In project management, unclear requirements, delays in progress, and inefficiencies due to wasted resources are major challenges. Traditional systems struggle to identify and address these problems in real time, hindering project progress and ultimately lowering the success rate.

[0489] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0490] In this invention, the server includes means for analyzing requirements information using natural language processing technology, identifying ambiguous parts, and generating additional information; means for collecting progress data in a timely manner, analyzing the progress, and predicting risks; and means for evaluating resource usage and generating optimization proposals. This enables users to grasp the progress of projects in real time and address issues quickly and effectively.

[0491] An "information processing device" is a combination of hardware and software designed to receive and analyze data.

[0492] "Natural language processing technology" is a technology that allows computers to analyze human language and understand its meaning.

[0493] "Requirements information" refers to documents or data that specifically outline the project's goals and specifications.

[0494] "Ambiguous parts" refer to sections within the requirements information that are unclear and therefore open to multiple interpretations.

[0495] "Progress data" refers to information including numerical data and status reports that indicate the progress of a project.

[0496] "Risk forecasting" is a method of identifying potential problems that may arise in a project in advance and considering countermeasures.

[0497] "Resource usage" refers to the state of how resources such as personnel, equipment, and time are being used in a project.

[0498] An "optimized plan" is the best possible plan designed to efficiently utilize resources.

[0499] A "visualization map" is a chart or graphic used to visually display progress or analysis results in an easy-to-understand manner.

[0500] A "user" is a person or organization that operates the system and manages the project.

[0501] This invention is an information processing system for efficient project management. The server is primarily responsible for data analysis and resource optimization, and handles data exchange between the terminal and the user. Specifically, the server uses natural language processing technology to analyze project requirements information entered by the user through the terminal and identify ambiguities. This includes technology that utilizes generative AI models to generate additional information and questions to resolve ambiguities in the requirements.

[0502] In terms of hardware configuration, the server is designed as a computer device equipped with a high-performance processor and a large amount of memory, while the terminal is a device that handles the interface with the user. In terms of software, natural language processing is possible by incorporating a generative AI model into the analysis program that runs on the server.

[0503] As a concrete example, suppose a user enters "I want to improve the interface of the new feature" into their terminal. The server applies natural language processing to this requirement and generates specific questions such as "What specific parts need improvement?" and presents them to the user. Based on this feedback, the user clarifies the requirements and facilitates the efficient progress of the project.

[0504] Furthermore, the server aggregates progress data sent from terminals and predicts risks inherent in ongoing projects. This allows users to intuitively understand project progress and risks through visualized heatmaps. For example, the server can send a notification to the user stating, "Task A is 50% behind schedule," prompting appropriate resource reallocation.

[0505] An example of a prompt statement is: "Describe the means to identify ambiguous requirements in a project management system and generate additional information." This prompt is given to the generative AI model and forms the basis for prompting appropriate information generation.

[0506] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0507] Step 1:

[0508] The user enters project requirements information into a terminal and sends it to the server. The entered data represents the specific goals and specifications of the project. The server receives this data and uses natural language processing techniques to identify ambiguous parts in the text. This allows it to generate additional information necessary for interpreting the requirements information. The output at this point consists of the identified ambiguous parts and the generated questions and supplementary information.

[0509] Step 2:

[0510] The server sends the generated questions and additional information to the terminal and presents it to the user. The user then modifies the requirements information based on the presented information and sends the more precise requirements back to the server via the terminal. This allows the user to create a more precise project plan. The output of this step is the modified requirements information.

[0511] Step 3:

[0512] The terminal continuously collects project progress data and sends it to the server. This data includes the completion rate and progress status of each task. The server receives this data and analyzes the progress data in real time. Based on the input data, a risk prediction algorithm is applied to detect potential risks during the project. The output of this process is the risk analysis results and the progress evaluation results.

[0513] Step 4:

[0514] The server sends a risk notification to the user based on the analysis results. This notification includes the specific nature of the risk and recommended countermeasures. It also generates a visual heatmap of project progress and displays it on the terminal. The user can review this visual information and take countermeasures as needed, such as adjusting resource allocation. The output of this step is the notification sent to the user and the heatmap.

[0515] Step 5:

[0516] The server analyzes the overall resource usage of the project and generates an optimization plan. This analysis includes resources in use, personnel allocation, and the priority of each task. The server uses an optimization algorithm to propose an efficient resource allocation while eliminating waste. This new plan is presented to the user via a terminal and adjusted and applied as needed. The output of this step is the proposed optimization plan.

[0517] (Application Example 1)

[0518] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0519] In production project management within factory automation processes, project requirements are often unclear, progress is difficult to track visually, and resource allocation is not optimized. Furthermore, it is necessary to effectively utilize data from production equipment and continuously optimize the process.

[0520] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0521] In this invention, the server includes means for an information processing device to analyze requirements information using natural language processing technology, identify ambiguous parts, and generate additional information; means for an information processing device to collect project progress data in a timely manner, analyze the progress status, and predict risks; and means for an information processing device to aggregate and analyze data from production equipment in the factory automation process. This enables real-time visualization of the progress status of production projects, highly accurate risk prediction, and optimal resource allocation.

[0522] An "information processing device" is a computing device that analyzes input data to clarify requirements, monitor progress, and optimize resources.

[0523] "Natural language processing technology" refers to a set of technologies that enable computers to understand, analyze, and process human language.

[0524] "Ambiguous areas" refer to parts of the requirements information that are not clearly defined and are open to interpretation.

[0525] "Means for generating additional information" refers to methods and processes for providing users with the information necessary to resolve ambiguities.

[0526] "Project progress data" refers to information that shows the completion status and progress of each task in a project.

[0527] "Risk prediction" is the process of estimating and diagnosing potential difficulties and obstacles that may arise in a project.

[0528] An "optimization plan" refers to a plan or proposal designed to maximize the efficiency of resource utilization.

[0529] "Data from production equipment" refers to information such as operating status, utilization rate, and error reports obtained from various machines and sensors within the factory.

[0530] "Real-time visualization" means instantly representing the ongoing state visually so that users can understand it intuitively.

[0531] The system used to implement this application is primarily a program that operates via a server and a terminal. The server consists of a high-performance information processing unit and utilizes natural language processing libraries (e.g., spaCy and NLTK), database management systems (e.g., MySQL), and data visualization tools (e.g., D3.js).

[0532] First, the user inputs project requirements information into the server using a terminal. The server uses natural language processing technology to analyze this requirements information, identifying particularly ambiguous parts and generating additional information to clarify them. This generated information is presented to the user via the terminal, allowing the user to clarify the project requirements based on it.

[0533] Next, the terminal continuously collects project progress data and sends it to the server. The server uses this data to monitor real-time progress and generate a visual heatmap. This data is very comprehensive because it is collected from various sensors within the factory, such as those on production equipment.

[0534] Furthermore, the server analyzes resource usage and provides the user with the most efficient resource allocation plan. By utilizing machine learning models based on historical data and performing risk predictions, more accurate predictions and suggestions become possible.

[0535] As a concrete example, when starting a "new product prototype project" in a factory, the user inputs the requirement "complete 10 prototypes by the weekend" via a terminal. During this time, the program collects progress data in real time and detects when a particular process is behind schedule. This allows the user to review resource allocation or adjust the schedule.

[0536] When using a generative AI model, users can enter prompts like the following to receive further analysis and suggestions: "Please tell me the predicted delay factors for the new product prototyping project, and suggest the optimal resource allocation to prevent those delays."

[0537] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0538] Step 1:

[0539] The user enters project requirements information via a terminal. This entered requirements information is sent to the server. Because this requirements information is in natural language format, it may contain ambiguous expressions.

[0540] Step 2:

[0541] The server analyzes the received requirements information using natural language processing techniques. The input for the analysis is the requirements information from the user, and the output is a report that identifies ambiguous parts and suggestions for additional information to clarify them. This process uses a natural language processing library to analyze unclear points within the requirements.

[0542] Step 3:

[0543] The server identifies ambiguous parts and sends additional information to the user via the terminal. The user then uses this information to refine and re-enter the requirements, or approves the additional information. The output of this process is the newly confirmed and revised requirements information.

[0544] Step 4:

[0545] The terminal resends these clarified requirements to the server and begins collecting project progress data. Progress data is automatically transmitted from various sensors and production equipment within the factory. This data is aggregated on the server in real time.

[0546] Step 5:

[0547] The server analyzes the received progress data to determine the project's progress. This analysis involves generating a heatmap as part of the data processing, visually representing the project's intuitive progress as output. It also performs risk prediction based on progress and identifies potential delay factors.

[0548] Step 6:

[0549] The server presents the user with a progress heatmap and risk prediction information via the terminal. The user can review the specific progress and make adjustments as needed. The output of this step is the project progress report and risk suggestions that the user receives.

[0550] Step 7:

[0551] The server analyzes resource usage and proposes the optimal allocation method. The input is current resource data, and the output is an optimized resource allocation proposal. This process uses a predictive model trained on historical project data to support efficient resource management.

[0552] Step 8:

[0553] The user approves or adjusts the provided resource allocation proposal. As a result, a new resource allocation plan is formulated, and the project proceeds based on it. The output is the updated project plan.

[0554] Throughout this entire process, the production process at the factory is efficiently managed. By utilizing superior data processing and analysis techniques, users can achieve effective project management.

[0555] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0556] This invention is a system aimed at improving efficiency and user experience in project management. The information processing device incorporates means for analyzing requirements information, collecting and analyzing progress data, and optimizing resource allocation, as well as an emotion engine for recognizing user emotions. When a user inputs requirements information using a terminal, the terminal sends it to a server, which analyzes the information using natural language processing technology. This identifies ambiguous areas and areas requiring improvement, and generates necessary additional information.

[0557] Project progress is analyzed in real time based on data sent from the terminal to the server. The server uses a risk prediction algorithm to assess potential risks related to progress and notifies the user. It can also generate a heatmap based on the analyzed data and present it visually to the user through the terminal. For resource optimization, the server evaluates resource usage and generates an efficient allocation plan. This information is proposed to the user, and if approved, resource allocation adjustments are made.

[0558] In particular, the present invention includes an emotion engine that can analyze the user's emotions in real time during interaction. Through this emotion analysis, the server detects the user's stress and anxiety and provides appropriate alerts and advice accordingly. Furthermore, based on user feedback, it is possible to adjust the interface's responses and support methods to provide a more personalized experience.

[0559] For example, if the emotion engine detects signs of stress while a user is using a project management tool, the server will then send an alert stating, "Your workload may be increasing. Consider reviewing your task priorities." In this way, the system is operated in a manner that considers both efficient project management and the psychological burden on the user.

[0560] The following describes the processing flow.

[0561] Step 1:

[0562] The user enters project requirements information into the terminal. The terminal collects the entered information and sends it to the server.

[0563] Step 2:

[0564] The server analyzes the received requirements information using natural language processing technology to identify ambiguous parts. The server then generates necessary additional information and improvement suggestions for the identified parts.

[0565] Step 3:

[0566] The server provides the user with additional questions and suggestions based on the analysis results via the terminal. This allows the user to clarify the requirements information, enter it into the terminal, and send it back to the server.

[0567] Step 4:

[0568] The terminal periodically collects project progress data and sends it to the server. This data includes task completion status, time logs, and progress targets.

[0569] Step 5:

[0570] The server analyzes progress data in real time and evaluates the progress status. Risk prediction algorithms are used to detect the possibility of delays and potential problems.

[0571] Step 6:

[0572] The server generates a heatmap based on progress analysis, providing users with a visual representation of the project's progress. Users can view this heatmap on their devices to understand the overall project status.

[0573] Step 7:

[0574] The server uses an emotion engine to recognize user emotions and detect user stress and anxiety. This enables flexible responses tailored to the user's psychological state.

[0575] Step 8:

[0576] Based on the results of sentiment analysis, the server generates appropriate alerts and advice for the user and notifies them through their device. For example, if the user is experiencing high levels of stress, it may suggest re-evaluating or interrupting tasks.

[0577] Step 9:

[0578] The server evaluates resource usage and creates optimization suggestions to prevent wasteful use and redundant tasks.

[0579] Step 10:

[0580] The server proposes an optimized resource allocation plan to the user and asks for their approval. If the user approves, the resource allocation is adjusted.

[0581] Step 11:

[0582] The user follows the new resource allocation and continues with project tasks. The device continuously updates data throughout this process and provides feedback to adapt to the new situation.

[0583] (Example 2)

[0584] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0585] Traditional project management systems suffer from problems such as ambiguity in requirements information and uncertainty in progress, which negatively impact project efficiency. Furthermore, they often lack management that considers user emotions and psychological burden, which can lead to a decline in overall project performance.

[0586] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0587] In this invention, the server includes means for analyzing requirements information using natural language processing technology, identifying ambiguous parts, and generating additional information; means for collecting progress data, analyzing the progress, and predicting risks; and means for evaluating the user's emotional state using an emotion analysis engine and providing appropriate alerts and advice. This makes it possible to improve the efficiency of project management and reduce the psychological burden on the user.

[0588] An "information processing device" refers to a system for processing and managing data, such as analyzing requirements information and tracking progress.

[0589] "Natural language processing technology" refers to the technology used to analyze and understand human language using computers.

[0590] "Requirements information" refers to data that specifies the specifications and conditions necessary for project execution.

[0591] "Ambiguous parts" refer to sections of information that lack clarity and may lead to misunderstandings.

[0592] "Additional information" refers to further information related to the project that is generated to fill in any ambiguities.

[0593] "Progress data" refers to information that shows the progress of a project.

[0594] "Risk prediction" refers to the act of anticipating potential problems that may arise in a project.

[0595] "Analysis results" refer to the insights and conclusions obtained after analyzing data.

[0596] "Notification" refers to the act of informing a user of important information or warnings from an information processing device.

[0597] "Resources" refer to the personnel, equipment, time, and other elements necessary to carry out a project.

[0598] An "optimization plan" refers to methods or strategies proposed to maximize the efficiency of resource use.

[0599] An "emotion analysis engine" refers to technology used to analyze human emotions and identify their state.

[0600] An "alert" refers to a notification designed to draw the user's attention to urgent information.

[0601] "Advice" refers to suggestions or proposals provided to users to encourage improvement or countermeasures.

[0602] This invention is an information processing system for efficient project management. The entire system consists of a server acting as an information processing device and a terminal operated by the user. The server has advanced data analysis capabilities, and the terminal receives input from the user and receives feedback from the server as needed.

[0603] The server analyzes the requirements information entered by the user on the terminal, based on natural language processing technology. This analysis utilizes generative AI models (e.g., BERT or GPT-3). The server identifies ambiguous descriptions and generates additional information to fill them in. This process clarifies the project requirements.

[0604] Furthermore, the server monitors project progress in real time and analyzes progress data. Terminals periodically send progress data to the server, which uses data analysis techniques (e.g., machine learning algorithms) to predict risks. Once the server identifies risks, it sends alerts to the user and generates optimized resource allocation suggestions as needed.

[0605] This system can also have the server evaluate the user's emotional state via an emotion analysis engine and provide corresponding advice and alerts. For example, if stress is detected on the user's device, the server will send an alert such as, "Your workload may be increasing. Consider reviewing your task priorities."

[0606] For example, by inputting a prompt such as, "Assess the project's progress risks and suggest what alerts should be issued to users if necessary," the AI ​​model will generate the steps needed to achieve the objective.

[0607] In this way, the invention aims to simultaneously improve the efficiency of project management and reduce the psychological burden on users.

[0608] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0609] Step 1:

[0610] The user enters project requirements information into the terminal. The terminal sends this information to the server. The input consists of text information such as a project overview and required functions. The terminal sends the information using an HTTP POST request. The server receives this information.

[0611] Step 2:

[0612] The server analyzes the received requirements information. This analysis utilizes generative AI models (e.g., BERT or GPT-3). The server leverages natural language processing algorithms to identify ambiguous descriptions and missing information, and generates the necessary additional information. The input is text information, and the output is clearly completed requirements information.

[0613] Step 3:

[0614] The terminal periodically sends data about the project's progress to the server. This data includes the completion status of tasks and resource usage. The server receives the progress data and stores it in a database.

[0615] Step 4:

[0616] The server analyzes progress based on historical and progress data. It uses machine learning algorithms to predict risks. The input is progress data, and the analysis results in risk prediction information.

[0617] Step 5:

[0618] Based on the progress analysis results, the server generates a heatmap to visually represent the progress for each area. The server uses a data visualization tool to create this heatmap and sends it to the terminal. The input is the analysis results, and the output is the heatmap.

[0619] Step 6:

[0620] The server generates an optimal resource allocation plan based on risk predictions and resource usage. This allocation plan is then presented to the user. The server notifies the user of the proposal to confirm whether the user approves it or requests modifications. The input is risk information and resource information, and the output is the resource allocation plan.

[0621] Step 7:

[0622] The server uses an emotion analysis engine to analyze user interactions from the device in real time and evaluate their emotional state. If stress or anxiety is detected, the server sends specific advice or alerts to the user. The input is user interaction data, and the output is advice or warning messages.

[0623] (Application Example 2)

[0624] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0625] Current project management systems and citizen service applications lack the ability to analyze user emotions in real time and provide appropriate support based on their psychological state. Furthermore, there is a lack of effective ways to utilize citizen feedback when optimizing resource allocation, which can easily lead to accumulated user dissatisfaction and a decline in service quality. Moreover, accurately understanding the usage of public services and allocating resources appropriately is crucial.

[0626] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0627] In this invention, the server includes means for analyzing requirements information using natural language processing technology, means for analyzing the user's psychological state using emotion recognition functionality, and means for optimizing resource allocation based on citizen feedback. This makes it possible to provide appropriate support in accordance with the user's emotions and improve the quality of citizen services.

[0628] An "information processing device" is an electronic device that has the function of managing, analyzing, and processing data.

[0629] "Natural language processing technology" is a technology that enables computers to understand and analyze human language.

[0630] "Requirements information" refers to information that indicates the conditions and specifications required for a project or system.

[0631] "Analysis results" are conclusions drawn by analyzing data and understanding its content and meaning.

[0632] "Resources" refer to elements necessary to carry out a project or task, such as personnel, equipment, and time.

[0633] "Emotion recognition function" is a technology that analyzes the user's psychological state from their facial expressions, behavior, and feedback.

[0634] "Feedback" refers to the opinions, impressions, evaluations, and other reactions provided by users.

[0635] "Optimization" is the pursuit of the most effective and efficient state for a specific purpose.

[0636] To implement this invention, it is necessary to build a system in which a server and a terminal exchange data with each other. The server acts as an information processing device, using natural language processing technology to analyze requirements information received from the user, identify ambiguous parts, and generate necessary additional information. The server also receives progress data of ongoing projects from the terminal, analyzes the progress in real time, and predicts risks. A risk prediction algorithm is used for this purpose.

[0637] Based on the analysis results, the server provides appropriate notifications to the user. These notifications include generating and displaying a heatmap that visually shows the project's progress. This heatmap is designed to intuitively communicate the progress to the user. Furthermore, the server analyzes the user's psychological state obtained through user feedback and emotion recognition functions, provides appropriate warnings and advice, and generates optimization suggestions based on resource usage.

[0638] The terminal is a device for users to input requirements information and feedback, and it sends the data to the server. Through the terminal, users can check the progress of the project and receive alerts and advice based on sentiment analysis. Cameras and sensors are used for sentiment recognition, and feedback is entered in text format.

[0639] As a concrete example, there is a process where citizens input feedback on public services into a terminal, and if the comments indicate negative sentiment, the server analyzes them to improve the service. An example of a prompt in this case would be, "As a citizen of a smart city, please provide feedback to improve public transportation services. Please perform sentiment analysis on the feedback, detect negative opinions, and suggest necessary improvements."

[0640] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0641] Step 1:

[0642] The terminal receives requirements information and feedback from the user. The input is in text format, and the user describes the project or public service. The terminal sends this to the server, which uses natural language processing technology to analyze the data and identify ambiguities. The output generates a list of ambiguous requirements and any necessary additional information.

[0643] Step 2:

[0644] The server receives progress data sent from the terminal. This data includes project progress and public opinion regarding public services. The server uses a risk prediction algorithm to analyze the ongoing situation and assess potential risks. The output consists of the risk assessment results and a list of recommended actions based on those results.

[0645] Step 3:

[0646] Users can receive these risk assessment results through their devices. The server also generates a heatmap visually representing the progress and sends it to the device. Users can intuitively understand the progress through this heatmap. Visualized progress data is provided as output.

[0647] Step 4:

[0648] The server uses data from terminals and sensors to analyze the user's psychological state in real time. Through emotion recognition, it identifies stress and frustration and generates appropriate warnings and advice. The output is psychological state-based alerts and advice provided to the user.

[0649] Step 5:

[0650] The server evaluates resource usage based on the analyzed data and generates optimization suggestions. These suggestions improve resource allocation and enhance the efficiency of projects and public services. The resource optimization suggestions are then generated and presented to the user as output.

[0651] Step 6:

[0652] The user reviews and approves the optimization proposal. Once the approval is sent to the server via the terminal, the server adjusts the resource allocation. This ensures that project and public service resources are used efficiently. The output shows the execution status of the approved resource allocation.

[0653] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0654] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0655] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0656] [Fourth Embodiment]

[0657] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0658] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0659] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0660] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0661] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0662] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0663] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0664] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0665] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0666] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0667] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0668] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0669] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0670] This invention is a system for enhancing efficiency in project management, centered around an information processing device that enables natural language processing, progress monitoring, and resource optimization. First, the user inputs project requirements information into the server via a terminal. The server receives this data and applies natural language processing technology to identify ambiguous parts within the requirements. Based on this analysis, the server generates any necessary additional information and presents it to the user via the terminal. The user can then clarify the requirements information based on this feedback.

[0671] Next, the terminal continuously collects project progress data and sends it to the server. The server uses this data to monitor the project's progress in real time. Using a risk prediction algorithm, it can detect risks inherent in the project's progress and notify the user in a timely manner. This notification may be displayed as a visual heatmap of the ongoing project, providing a format that the user can intuitively understand.

[0672] Furthermore, the server analyzes current resource usage and develops an optimal resource allocation plan. This involves using optimization algorithms to minimize wasted resource usage and duplicate task assignments. This optimization plan is provided to the user as a suggestion, allowing the user to adjust resource allocation based on it.

[0673] As a concrete example, in a software development project, if a user enters a requirement such as "I want to improve the interface of a new feature," the server will generate questions to confirm "specifically which parts need improvement." Also, if the server detects that a particular task is behind schedule during project progress, it will send a notification to the user stating, "Task A is 50% behind schedule. We recommend reallocating resources or revising the schedule."

[0674] The system of the present invention intelligently supports project requirements definition, progress management, and resource allocation, thereby improving the overall success rate of the project.

[0675] The following describes the processing flow.

[0676] Step 1:

[0677] The user enters project requirements information into the terminal. The terminal receives this information and sends it to the server.

[0678] Step 2:

[0679] The server analyzes the received requirements information using natural language processing technology to identify ambiguous parts and areas that require further explanation.

[0680] Step 3:

[0681] The server generates additional questions and improvement suggestions based on the analysis results and provides them to the user via the terminal.

[0682] Step 4:

[0683] The user reviews the feedback from the server, updates the requirements information as needed, and resubmits it.

[0684] Step 5:

[0685] The terminal periodically collects project progress data from the project management tool and sends it to the server.

[0686] Step 6:

[0687] The server uses the collected progress data to analyze the project's progress in real time.

[0688] Step 7:

[0689] The server runs a risk prediction algorithm to assess the likelihood of project delays and problems.

[0690] Step 8:

[0691] The server generates notifications based on the evaluation results and provides users with risk alerts and progress updates via their devices.

[0692] Step 9:

[0693] The server analyzes the overall resource usage of the project and creates an optimal allocation plan.

[0694] Step 10:

[0695] The server proposes optimization plans to the user and prompts them to change their resource allocation.

[0696] Step 11:

[0697] The user reviews the server's proposal and adjusts resource allocation as needed.

[0698] (Example 1)

[0699] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0700] In project management, unclear requirements, delays in progress, and inefficiencies due to wasted resources are major challenges. Traditional systems struggle to identify and address these problems in real time, hindering project progress and ultimately lowering the success rate.

[0701] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0702] In this invention, the server includes means for analyzing requirements information using natural language processing technology, identifying ambiguous parts, and generating additional information; means for collecting progress data in a timely manner, analyzing the progress, and predicting risks; and means for evaluating resource usage and generating optimization proposals. This enables users to grasp the progress of projects in real time and address issues quickly and effectively.

[0703] An "information processing device" is a combination of hardware and software designed to receive and analyze data.

[0704] "Natural language processing technology" is a technology that allows computers to analyze human language and understand its meaning.

[0705] "Requirements information" refers to documents or data that specifically outline the project's goals and specifications.

[0706] "Ambiguous parts" refer to sections within the requirements information that are unclear and therefore open to multiple interpretations.

[0707] "Progress data" refers to information including numerical data and status reports that indicate the progress of a project.

[0708] "Risk forecasting" is a method of identifying potential problems that may arise in a project in advance and considering countermeasures.

[0709] "Resource usage" refers to the state of how resources such as personnel, equipment, and time are being used in a project.

[0710] An "optimized plan" is the best possible plan designed to efficiently utilize resources.

[0711] A "visualization map" is a chart or graphic used to visually display progress or analysis results in an easy-to-understand manner.

[0712] A "user" is a person or organization that operates the system and manages the project.

[0713] This invention is an information processing system for efficient project management. The server is primarily responsible for data analysis and resource optimization, and handles data exchange between the terminal and the user. Specifically, the server uses natural language processing technology to analyze project requirements information entered by the user through the terminal and identify ambiguities. This includes technology that utilizes generative AI models to generate additional information and questions to resolve ambiguities in the requirements.

[0714] In terms of hardware configuration, the server is designed as a computer device equipped with a high-performance processor and a large amount of memory, while the terminal is a device that handles the interface with the user. In terms of software, natural language processing is possible by incorporating a generative AI model into the analysis program that runs on the server.

[0715] As a concrete example, suppose a user enters "I want to improve the interface of the new feature" into their terminal. The server applies natural language processing to this requirement and generates specific questions such as "What specific parts need improvement?" and presents them to the user. Based on this feedback, the user clarifies the requirements and facilitates the efficient progress of the project.

[0716] Furthermore, the server aggregates progress data sent from terminals and predicts risks inherent in ongoing projects. This allows users to intuitively understand project progress and risks through visualized heatmaps. For example, the server can send a notification to the user stating, "Task A is 50% behind schedule," prompting appropriate resource reallocation.

[0717] An example of a prompt statement is: "Describe the means to identify ambiguous requirements in a project management system and generate additional information." This prompt is given to the generative AI model and forms the basis for prompting appropriate information generation.

[0718] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0719] Step 1:

[0720] The user enters project requirements information into a terminal and sends it to the server. The entered data represents the specific goals and specifications of the project. The server receives this data and uses natural language processing techniques to identify ambiguous parts in the text. This allows it to generate additional information necessary for interpreting the requirements information. The output at this point consists of the identified ambiguous parts and the generated questions and supplementary information.

[0721] Step 2:

[0722] The server sends the generated questions and additional information to the terminal and presents it to the user. The user then modifies the requirements information based on the presented information and sends the more precise requirements back to the server via the terminal. This allows the user to create a more precise project plan. The output of this step is the modified requirements information.

[0723] Step 3:

[0724] The terminal continuously collects project progress data and sends it to the server. This data includes the completion rate and progress status of each task. The server receives this data and analyzes the progress data in real time. Based on the input data, a risk prediction algorithm is applied to detect potential risks during the project. The output of this process is the risk analysis results and the progress evaluation results.

[0725] Step 4:

[0726] The server sends a risk notification to the user based on the analysis results. This notification includes the specific nature of the risk and recommended countermeasures. It also generates a visual heatmap of project progress and displays it on the terminal. The user can review this visual information and take countermeasures as needed, such as adjusting resource allocation. The output of this step is the notification sent to the user and the heatmap.

[0727] Step 5:

[0728] The server analyzes the overall resource usage of the project and generates an optimization plan. This analysis includes resources in use, personnel allocation, and the priority of each task. The server uses an optimization algorithm to propose an efficient resource allocation while eliminating waste. This new plan is presented to the user via a terminal and adjusted and applied as needed. The output of this step is the proposed optimization plan.

[0729] (Application Example 1)

[0730] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0731] In production project management within factory automation processes, project requirements are often unclear, progress is difficult to track visually, and resource allocation is not optimized. Furthermore, it is necessary to effectively utilize data from production equipment and continuously optimize the process.

[0732] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0733] In this invention, the server includes means for an information processing device to analyze requirements information using natural language processing technology, identify ambiguous parts, and generate additional information; means for an information processing device to collect project progress data in a timely manner, analyze the progress status, and predict risks; and means for an information processing device to aggregate and analyze data from production equipment in the factory automation process. This enables real-time visualization of the progress status of production projects, highly accurate risk prediction, and optimal resource allocation.

[0734] An "information processing device" is a computing device that analyzes input data to clarify requirements, monitor progress, and optimize resources.

[0735] "Natural language processing technology" refers to a set of technologies that enable computers to understand, analyze, and process human language.

[0736] "Ambiguous areas" refer to parts of the requirements information that are not clearly defined and are open to interpretation.

[0737] "Means for generating additional information" refers to methods and processes for providing users with the information necessary to resolve ambiguities.

[0738] "Project progress data" refers to information that shows the completion status and progress of each task in a project.

[0739] "Risk prediction" is the process of estimating and diagnosing potential difficulties and obstacles that may arise in a project.

[0740] An "optimization plan" refers to a plan or proposal designed to maximize the efficiency of resource utilization.

[0741] "Data from production equipment" refers to information such as operating status, utilization rate, and error reports obtained from various machines and sensors within the factory.

[0742] "Real-time visualization" means instantly representing the ongoing state visually so that users can understand it intuitively.

[0743] The system used to implement this application is primarily a program that operates via a server and a terminal. The server consists of a high-performance information processing unit and utilizes natural language processing libraries (e.g., spaCy and NLTK), database management systems (e.g., MySQL), and data visualization tools (e.g., D3.js).

[0744] First, the user inputs project requirements information into the server using a terminal. The server uses natural language processing technology to analyze this requirements information, identifying particularly ambiguous parts and generating additional information to clarify them. This generated information is presented to the user via the terminal, allowing the user to clarify the project requirements based on it.

[0745] Next, the terminal continuously collects project progress data and sends it to the server. The server uses this data to monitor real-time progress and generate a visual heatmap. This data is very comprehensive because it is collected from various sensors within the factory, such as those on production equipment.

[0746] Furthermore, the server analyzes resource usage and provides the user with the most efficient resource allocation plan. By utilizing machine learning models based on historical data and performing risk predictions, more accurate predictions and suggestions become possible.

[0747] As a concrete example, when starting a "new product prototype project" in a factory, the user inputs the requirement "complete 10 prototypes by the weekend" via a terminal. During this time, the program collects progress data in real time and detects when a particular process is behind schedule. This allows the user to review resource allocation or adjust the schedule.

[0748] When using a generative AI model, users can enter prompts like the following to receive further analysis and suggestions: "Please tell me the predicted delay factors for the new product prototyping project, and suggest the optimal resource allocation to prevent those delays."

[0749] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0750] Step 1:

[0751] The user enters project requirements information via a terminal. This entered requirements information is sent to the server. Because this requirements information is in natural language format, it may contain ambiguous expressions.

[0752] Step 2:

[0753] The server analyzes the received requirements information using natural language processing techniques. The input for the analysis is the requirements information from the user, and the output is a report that identifies ambiguous parts and suggestions for additional information to clarify them. This process uses a natural language processing library to analyze unclear points within the requirements.

[0754] Step 3:

[0755] The server identifies ambiguous parts and sends additional information to the user via the terminal. The user then uses this information to refine and re-enter the requirements, or approves the additional information. The output of this process is the newly confirmed and revised requirements information.

[0756] Step 4:

[0757] The terminal resends these clarified requirements to the server and begins collecting project progress data. Progress data is automatically transmitted from various sensors and production equipment within the factory. This data is aggregated on the server in real time.

[0758] Step 5:

[0759] The server analyzes the received progress data to determine the project's progress. This analysis involves generating a heatmap as part of the data processing, visually representing the project's intuitive progress as output. It also performs risk prediction based on progress and identifies potential delay factors.

[0760] Step 6:

[0761] The server presents the user with a progress heatmap and risk prediction information via the terminal. The user can review the specific progress and make adjustments as needed. The output of this step is the project progress report and risk suggestions that the user receives.

[0762] Step 7:

[0763] The server analyzes resource usage and proposes the optimal allocation method. The input is current resource data, and the output is an optimized resource allocation proposal. This process uses a predictive model trained on historical project data to support efficient resource management.

[0764] Step 8:

[0765] The user approves or adjusts the provided resource allocation proposal. As a result, a new resource allocation plan is formulated, and the project proceeds based on it. The output is the updated project plan.

[0766] Throughout this entire process, the production process at the factory is efficiently managed. By utilizing superior data processing and analysis techniques, users can achieve effective project management.

[0767] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0768] This invention is a system aimed at improving efficiency and user experience in project management. The information processing device incorporates means for analyzing requirements information, collecting and analyzing progress data, and optimizing resource allocation, as well as an emotion engine for recognizing user emotions. When a user inputs requirements information using a terminal, the terminal sends it to a server, which analyzes the information using natural language processing technology. This identifies ambiguous areas and areas requiring improvement, and generates necessary additional information.

[0769] Project progress is analyzed in real time based on data sent from the terminal to the server. The server uses a risk prediction algorithm to assess potential risks related to progress and notifies the user. It can also generate a heatmap based on the analyzed data and present it visually to the user through the terminal. For resource optimization, the server evaluates resource usage and generates an efficient allocation plan. This information is proposed to the user, and if approved, resource allocation adjustments are made.

[0770] In particular, the present invention includes an emotion engine that can analyze the user's emotions in real time during interaction. Through this emotion analysis, the server detects the user's stress and anxiety and provides appropriate alerts and advice accordingly. Furthermore, based on user feedback, it is possible to adjust the interface's responses and support methods to provide a more personalized experience.

[0771] For example, if the emotion engine detects signs of stress while a user is using a project management tool, the server will then send an alert stating, "Your workload may be increasing. Consider reviewing your task priorities." In this way, the system is operated in a manner that considers both efficient project management and the psychological burden on the user.

[0772] The following describes the processing flow.

[0773] Step 1:

[0774] The user enters project requirements information into the terminal. The terminal collects the entered information and sends it to the server.

[0775] Step 2:

[0776] The server analyzes the received requirements information using natural language processing technology to identify ambiguous parts. The server then generates necessary additional information and improvement suggestions for the identified parts.

[0777] Step 3:

[0778] The server provides the user with additional questions and suggestions based on the analysis results via the terminal. This allows the user to clarify the requirements information, enter it into the terminal, and send it back to the server.

[0779] Step 4:

[0780] The terminal periodically collects project progress data and sends it to the server. This data includes task completion status, time logs, and progress targets.

[0781] Step 5:

[0782] The server analyzes progress data in real time and evaluates the progress status. Risk prediction algorithms are used to detect the possibility of delays and potential problems.

[0783] Step 6:

[0784] The server generates a heatmap based on progress analysis, providing users with a visual representation of the project's progress. Users can view this heatmap on their devices to understand the overall project status.

[0785] Step 7:

[0786] The server uses an emotion engine to recognize user emotions and detect user stress and anxiety. This enables flexible responses tailored to the user's psychological state.

[0787] Step 8:

[0788] Based on the results of sentiment analysis, the server generates appropriate alerts and advice for the user and notifies them through their device. For example, if the user is experiencing high levels of stress, it may suggest re-evaluating or interrupting tasks.

[0789] Step 9:

[0790] The server evaluates resource usage and creates optimization suggestions to prevent wasteful use and redundant tasks.

[0791] Step 10:

[0792] The server proposes an optimized resource allocation plan to the user and asks for their approval. If the user approves, the resource allocation is adjusted.

[0793] Step 11:

[0794] The user follows the new resource allocation and continues with project tasks. The device continuously updates data throughout this process and provides feedback to adapt to the new situation.

[0795] (Example 2)

[0796] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0797] Traditional project management systems suffer from problems such as ambiguity in requirements information and uncertainty in progress, which negatively impact project efficiency. Furthermore, they often lack management that considers user emotions and psychological burden, which can lead to a decline in overall project performance.

[0798] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0799] In this invention, the server includes means for analyzing requirements information using natural language processing technology, identifying ambiguous parts, and generating additional information; means for collecting progress data, analyzing the progress, and predicting risks; and means for evaluating the user's emotional state using an emotion analysis engine and providing appropriate alerts and advice. This makes it possible to improve the efficiency of project management and reduce the psychological burden on the user.

[0800] An "information processing device" refers to a system for processing and managing data, such as analyzing requirements information and tracking progress.

[0801] "Natural language processing technology" refers to the technology used to analyze and understand human language using computers.

[0802] "Requirements information" refers to data that specifies the specifications and conditions necessary for project execution.

[0803] "Ambiguous parts" refer to sections of information that lack clarity and may lead to misunderstandings.

[0804] "Additional information" refers to further information related to the project that is generated to fill in any ambiguities.

[0805] "Progress data" refers to information that shows the progress of a project.

[0806] "Risk prediction" refers to the act of anticipating potential problems that may arise in a project.

[0807] "Analysis results" refer to the insights and conclusions obtained after analyzing data.

[0808] "Notification" refers to the act of informing a user of important information or warnings from an information processing device.

[0809] "Resources" refer to the personnel, equipment, time, and other elements necessary to carry out a project.

[0810] An "optimization plan" refers to methods or strategies proposed to maximize the efficiency of resource use.

[0811] An "emotion analysis engine" refers to technology used to analyze human emotions and identify their state.

[0812] An "alert" refers to a notification designed to draw the user's attention to urgent information.

[0813] "Advice" refers to suggestions or proposals provided to users to encourage improvement or countermeasures.

[0814] This invention is an information processing system for efficient project management. The entire system consists of a server acting as an information processing device and a terminal operated by the user. The server has advanced data analysis capabilities, and the terminal receives input from the user and receives feedback from the server as needed.

[0815] The server analyzes the requirements information entered by the user on the terminal, based on natural language processing technology. This analysis utilizes generative AI models (e.g., BERT or GPT-3). The server identifies ambiguous descriptions and generates additional information to fill them in. This process clarifies the project requirements.

[0816] Furthermore, the server monitors project progress in real time and analyzes progress data. Terminals periodically send progress data to the server, which uses data analysis techniques (e.g., machine learning algorithms) to predict risks. Once the server identifies risks, it sends alerts to the user and generates optimized resource allocation suggestions as needed.

[0817] This system can also have the server evaluate the user's emotional state via an emotion analysis engine and provide corresponding advice and alerts. For example, if stress is detected on the user's device, the server will send an alert such as, "Your workload may be increasing. Consider reviewing your task priorities."

[0818] For example, by inputting a prompt such as, "Assess the project's progress risks and suggest what alerts should be issued to users if necessary," the AI ​​model will generate the steps needed to achieve the objective.

[0819] In this way, the invention aims to simultaneously improve the efficiency of project management and reduce the psychological burden on users.

[0820] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0821] Step 1:

[0822] The user enters project requirements information into the terminal. The terminal sends this information to the server. The input consists of text information such as a project overview and required functions. The terminal sends the information using an HTTP POST request. The server receives this information.

[0823] Step 2:

[0824] The server analyzes the received requirements information. This analysis utilizes generative AI models (e.g., BERT or GPT-3). The server leverages natural language processing algorithms to identify ambiguous descriptions and missing information, and generates the necessary additional information. The input is text information, and the output is clearly completed requirements information.

[0825] Step 3:

[0826] The terminal periodically sends data about the project's progress to the server. This data includes the completion status of tasks and resource usage. The server receives the progress data and stores it in a database.

[0827] Step 4:

[0828] The server analyzes progress based on historical and progress data. It uses machine learning algorithms to predict risks. The input is progress data, and the analysis results in risk prediction information.

[0829] Step 5:

[0830] Based on the progress analysis results, the server generates a heatmap to visually represent the progress for each area. The server uses a data visualization tool to create this heatmap and sends it to the terminal. The input is the analysis results, and the output is the heatmap.

[0831] Step 6:

[0832] The server generates an optimal resource allocation plan based on risk predictions and resource usage. This allocation plan is then presented to the user. The server notifies the user of the proposal to confirm whether the user approves it or requests modifications. The input is risk information and resource information, and the output is the resource allocation plan.

[0833] Step 7:

[0834] The server uses an emotion analysis engine to analyze user interactions from the device in real time and evaluate their emotional state. If stress or anxiety is detected, the server sends specific advice or alerts to the user. The input is user interaction data, and the output is advice or warning messages.

[0835] (Application Example 2)

[0836] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0837] Current project management systems and citizen service applications lack the ability to analyze user emotions in real time and provide appropriate support based on their psychological state. Furthermore, there is a lack of effective ways to utilize citizen feedback when optimizing resource allocation, which can easily lead to accumulated user dissatisfaction and a decline in service quality. Moreover, accurately understanding the usage of public services and allocating resources appropriately is crucial.

[0838] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0839] In this invention, the server includes means for analyzing requirements information using natural language processing technology, means for analyzing the user's psychological state using emotion recognition functionality, and means for optimizing resource allocation based on citizen feedback. This makes it possible to provide appropriate support in accordance with the user's emotions and improve the quality of citizen services.

[0840] An "information processing device" is an electronic device that has the function of managing, analyzing, and processing data.

[0841] "Natural language processing technology" is a technology that enables computers to understand and analyze human language.

[0842] "Requirements information" refers to information that indicates the conditions and specifications required for a project or system.

[0843] "Analysis results" are conclusions drawn by analyzing data and understanding its content and meaning.

[0844] "Resources" refer to elements necessary to carry out a project or task, such as personnel, equipment, and time.

[0845] "Emotion recognition function" is a technology that analyzes the user's psychological state from their facial expressions, behavior, and feedback.

[0846] "Feedback" refers to the opinions, impressions, evaluations, and other reactions provided by users.

[0847] "Optimization" is the pursuit of the most effective and efficient state for a specific purpose.

[0848] To implement this invention, it is necessary to build a system in which a server and a terminal exchange data with each other. The server acts as an information processing device, using natural language processing technology to analyze requirements information received from the user, identify ambiguous parts, and generate necessary additional information. The server also receives progress data of ongoing projects from the terminal, analyzes the progress in real time, and predicts risks. A risk prediction algorithm is used for this purpose.

[0849] Based on the analysis results, the server provides appropriate notifications to the user. These notifications include generating and displaying a heatmap that visually shows the project's progress. This heatmap is designed to intuitively communicate the progress to the user. Furthermore, the server analyzes the user's psychological state obtained through user feedback and emotion recognition functions, provides appropriate warnings and advice, and generates optimization suggestions based on resource usage.

[0850] The terminal is a device for users to input requirements information and feedback, and it sends the data to the server. Through the terminal, users can check the progress of the project and receive alerts and advice based on sentiment analysis. Cameras and sensors are used for sentiment recognition, and feedback is entered in text format.

[0851] As a concrete example, there is a process where citizens input feedback on public services into a terminal, and if the comments indicate negative sentiment, the server analyzes them to improve the service. An example of a prompt in this case would be, "As a citizen of a smart city, please provide feedback to improve public transportation services. Please perform sentiment analysis on the feedback, detect negative opinions, and suggest necessary improvements."

[0852] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0853] Step 1:

[0854] The terminal receives requirements information and feedback from the user. The input is in text format, and the user describes the project or public service. The terminal sends this to the server, which uses natural language processing technology to analyze the data and identify ambiguities. The output generates a list of ambiguous requirements and any necessary additional information.

[0855] Step 2:

[0856] The server receives progress data sent from the terminal. This data includes project progress and public opinion regarding public services. The server uses a risk prediction algorithm to analyze the ongoing situation and assess potential risks. The output consists of the risk assessment results and a list of recommended actions based on those results.

[0857] Step 3:

[0858] Users can receive these risk assessment results through their devices. The server also generates a heatmap visually representing the progress and sends it to the device. Users can intuitively understand the progress through this heatmap. Visualized progress data is provided as output.

[0859] Step 4:

[0860] The server uses data from terminals and sensors to analyze the user's psychological state in real time. Through emotion recognition, it identifies stress and frustration and generates appropriate warnings and advice. The output is psychological state-based alerts and advice provided to the user.

[0861] Step 5:

[0862] The server evaluates resource usage based on the analyzed data and generates optimization suggestions. These suggestions improve resource allocation and enhance the efficiency of projects and public services. The resource optimization suggestions are then generated and presented to the user as output.

[0863] Step 6:

[0864] The user reviews and approves the optimization proposal. Once the approval is sent to the server via the terminal, the server adjusts the resource allocation. This ensures that project and public service resources are used efficiently. The output shows the execution status of the approved resource allocation.

[0865] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0866] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0867] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0868] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0869] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0870] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0871] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0872] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0873] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0874] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0875] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0876] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0877] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0878] 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.

[0879] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0880] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0881] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0882] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0883] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0884] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0885] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0886] The following is further disclosed regarding the embodiments described above.

[0887] (Claim 1)

[0888] The information processing device includes means for analyzing requirement information using natural language processing technology, identifying ambiguous parts, and generating additional information.

[0889] The information processing device collects project progress data in a timely manner, analyzes the progress status, and provides means for predicting risks.

[0890] The information processing device provides means for notifying the user based on the analysis results,

[0891] The information processing device includes means for evaluating resource usage and generating optimization proposals,

[0892] A system that includes this.

[0893] (Claim 2)

[0894] The system according to claim 1, further comprising means for an information processing device to automatically generate a heat map according to the progress of a project and to visually present the progress to the user.

[0895] (Claim 3)

[0896] The system according to claim 1, further characterized in that the information processing device includes means for optimizing a risk prediction model by learning past project data and continuously improving its accuracy.

[0897] "Example 1"

[0898] (Claim 1)

[0899] The information processing device includes means for analyzing requirement information using natural language processing technology, identifying ambiguous parts, and generating additional information.

[0900] The information processing device collects progress data in a timely manner, analyzes the progress, and provides means for predicting risks.

[0901] The information processing device provides means for notifying the user based on the analysis results,

[0902] The information processing device includes means for evaluating resource usage and generating an optimization plan,

[0903] Means for the user to modify the requirements information based on the generated or presented information,

[0904] A system that includes this.

[0905] (Claim 2)

[0906] The system according to claim 1, further characterized in that the information processing device includes means for automatically generating a visualization map according to the progress and visually presenting the progress to the user.

[0907] (Claim 3)

[0908] The system according to claim 1, further comprising means for the information processing device to learn past business data, optimize a risk prediction model, and continuously improve its accuracy.

[0909] "Application Example 1"

[0910] (Claim 1)

[0911] The information processing device includes means for analyzing requirement information using natural language processing technology, identifying ambiguous parts, and generating additional information.

[0912] The information processing device collects project progress data in a timely manner, analyzes the progress status, and provides means for predicting risks.

[0913] The information processing device provides means for notifying the user based on the analysis results,

[0914] The information processing device includes means for evaluating resource usage and generating optimization proposals,

[0915] An information processing device provides a means to visualize and present project progress to the user in real time.

[0916] To support production project management in factory automation processes, an information processing device provides a means for aggregating and analyzing data from production equipment,

[0917] A system that includes this.

[0918] (Claim 2)

[0919] The system according to claim 1, further comprising means for an information processing device to automatically generate a heat map according to the progress of a project and to visually present the progress to the user.

[0920] (Claim 3)

[0921] The system according to claim 1, further comprising means for the information processing device to learn data from past production processes, optimize a risk prediction model, and continuously improve its accuracy.

[0922] "Example 2 of combining an emotion engine"

[0923] (Claim 1)

[0924] The information processing device includes means for analyzing requirement information using natural language processing technology, identifying ambiguous parts, and generating additional information.

[0925] The information processing device collects progress data in a timely manner, analyzes the progress, and provides means for predicting risks.

[0926] The information processing device provides a means for notifying the user based on the analysis results,

[0927] The information processing device includes means for evaluating resource usage and generating an optimization plan,

[0928] An information processing device that uses an emotion analysis engine to evaluate the user's emotional state and provides appropriate alerts and advice,

[0929] A system that includes this.

[0930] (Claim 2)

[0931] The system according to claim 1, further comprising means for an information processing device to automatically generate visual information according to progress and to visually present the progress to the user.

[0932] (Claim 3)

[0933] The system according to claim 1, further comprising means for the information processing device to learn past data, optimize a risk prediction model, and continuously improve its accuracy.

[0934] "Application example 2 when combining with an emotional engine"

[0935] (Claim 1)

[0936] The information processing device includes means for analyzing requirement information using natural language processing technology, identifying ambiguous parts, and generating additional information.

[0937] The information processing device collects progress data in a timely manner, analyzes the ongoing situation, and provides means for predicting risks.

[0938] The information processing device provides a means for notifying the user based on the analysis results,

[0939] The information processing device includes means for evaluating resource usage and generating an optimization plan,

[0940] An information processing device analyzes the user's psychological state using emotion recognition functionality and provides appropriate warnings and advice.

[0941] A system that includes this.

[0942] (Claim 2)

[0943] The system according to claim 1, characterized in that the information processing device analyzes citizen feedback, detects citizen stress and dissatisfaction through sentiment analysis, and takes appropriate action.

[0944] (Claim 3)

[0945] The system according to claim 1, characterized in that the information processing device optimizes resource allocation based on citizens' opinions and improves citizen services in a smart city. [Explanation of symbols]

[0946] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of analyzing requirements information using natural language processing technology, identifying ambiguous parts, and generating additional information, A means of collecting project progress data in a timely manner, analyzing the progress status, and predicting risks, A means of notifying users based on the analysis results, A means for evaluating resource usage and generating optimization proposals, A system that includes this.

2. The system according to claim 1, further comprising means for automatically generating a heat map according to the progress of a project and visually presenting the progress to the user.

3. The system according to claim 1, further comprising means for optimizing a risk prediction model by learning from past project data and continuously improving its accuracy.

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