system

Abstract

We provide the system. [Solution] A means of receiving project data, storing it in a database, and analyzing that data, A means to update the generated knowledge and search for past cases based on specified conditions, A means of generating and providing useful feedback to users for the project, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including 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, it is required to reduce the anxiety that occurs when taking on a new project and improve the success rate. In particular, regarding projects that have failed in the past, their experiences have not been sufficiently shared, and there is a risk of repeating the same failures. Therefore, it is necessary to centrally manage and share the knowledge of projects including failure cases to improve the quality of project management.

Means for Solving the Problems

[0005] This invention provides a system that includes means for receiving project data and storing it in a database, means for updating generated knowledge and searching for past cases based on specified conditions, and means for generating and providing project-useful feedback to users. By learning from past successes and failures, this system reduces anxiety when first attempting project management and improves the success rate of projects.

[0006] "Project data" refers to a collection of information that includes all information related to a specific project, such as project progress, results, challenges, and risks.

[0007] A "database" is an information management system that stores information efficiently and systematically, and allows information to be searched and accessed as needed.

[0008] "Knowledge" refers to the knowledge and lessons learned from past experiences and case studies, and is information that, when utilized, can lead current and future projects to success more efficiently.

[0009] "Specified conditions" refer to specific criteria or parameters defined by the user when performing searches or filtering.

[0010] A "case study" refers to a specific experience or event that includes details of past projects, success factors, and failure factors.

[0011] "Feedback" refers to information provided to users regarding areas for improvement and lessons learned in a project, thereby supporting the project's further success.

[0012] A "system" is a collection of hardware and software components that work together to achieve a specific purpose. [Brief explanation of the drawing]

[0013] [Figure 1]This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0014] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, the labeled 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.

[0019] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), etc.

[0020] 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."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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".

[0034] This invention provides a knowledge management system for project management, in which servers, terminals, and users interact to improve the success rate of projects. Specific embodiments are shown below.

[0035] Server Functions

[0036] The server has the function of receiving project data and storing it in a database. When a new project is completed, project-related information is entered by the assigned user via their terminal, and the server receives it. The server classifies the received data, identifies success and failure cases, and stores them in the database. The server also utilizes AI models to analyze past project data and extract important lessons and risks. Based on this knowledge, the database is updated, and search results are provided according to the conditions specified by the user.

[0037] Device functions

[0038] The terminal provides an interface for users to input project data. Through this interface, users can input detailed information about project progress, results, and problems, and send it to the server. The terminal also provides an interface to assist users in searching for past project examples. By specifying conditions, the terminal retrieves relevant data from the server in real time and displays it in an easy-to-understand visual format.

[0039] User roles

[0040] Users access this system to support project management. By using a terminal to input new project data, users can contribute to a shared knowledge resource. Furthermore, users can utilize the terminal's search function to investigate similar past cases and apply the lessons learned to improve the success rate of their own projects. For example, during the progress of a new project, they can search for "failures in the market research phase" to identify appropriate approaches and risks to avoid beforehand.

[0041] This allows system users to leverage accumulated knowledge to receive consistent support from project planning to execution, thereby improving the project's success rate.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The user inputs project data for their ongoing project through the terminal interface. They accurately enter information such as project details, progress, results, and any problems encountered, and prepare it for transmission to the server.

[0045] Step 2:

[0046] The terminal organizes the project data entered by the user and sends it to the server in the appropriate format. Once the data transmission is complete, the terminal prepares to proceed to the next operation.

[0047] Step 3:

[0048] The server checks the project data received from the terminal and saves it to the database. It then categorizes the data based on whether the project was successful or unsuccessful, and which phase of the project it represents.

[0049] Step 4:

[0050] The server analyzes the stored project data using an AI model to extract lessons learned and risk factors. The information obtained through this process is integrated into the existing knowledge base, and the search index is updated.

[0051] Step 5:

[0052] The user enters a query on their device to search for past project examples. They specify search criteria and define the details of the project for which they want to retrieve relevant knowledge.

[0053] Step 6:

[0054] The terminal sends the user's search query to the server. The server searches the database according to the specified conditions and finds a suitable past project example.

[0055] Step 7:

[0056] The server sends the terminal with success and failure stories of related projects as search results. The server also generates and provides feedback and suggestions for improvement extracted from the relevant projects.

[0057] Step 8:

[0058] The terminal visually displays search results received from the server to the user. The user then uses this information to review data useful for the progress of their project and to inform their decision-making.

[0059] Step 9:

[0060] Based on feedback from the server, users will improve project planning and execution methods. For example, they might consider and apply workarounds for problems faced by similar projects in the past.

[0061] (Example 1)

[0062] 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."

[0063] A challenge in project management is to streamline the accumulation and utilization of knowledge. Traditional methods have made it difficult to systematically manage project successes and failures and learn from them, and knowledge transfer has often been insufficient. As a result, the same mistakes were repeated, making it difficult to improve project success rates.

[0064] 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.

[0065] In this invention, the server includes means for receiving project information and storing it in a data structure, means for updating the generated knowledge and searching past cases based on specified conditions, and means for extracting important lessons and risks based on the analyzed results and providing them to the user. This makes it possible to effectively accumulate knowledge about the project and utilize it in future projects.

[0066] "Project information" refers to data related to the progress of a project, including the start date, planned end date, progress status, results, and problems.

[0067] A "data structure" refers to a framework for organizing and storing information, and includes databases and other record formats.

[0068] "Analysis" is the process of examining received data in detail to identify trends and patterns.

[0069] "Knowledge" refers to information that can be used in future projects, such as lessons learned, strategies, and risk information gained from the progress of a project.

[0070] A "generative AI model" is a machine learning model that performs pattern recognition and prediction based on a large amount of historical data.

[0071] "User interface" refers to the screen on which a user operates when entering or searching for data on a device, and includes the display of visual information.

[0072] "Success stories and failure stories" refer to information recorded as a result of effective strategies or incorrect decisions made in past projects.

[0073] "Feedback" refers to advice and information generated based on past data analysis results, which serves as a guideline for improving the project.

[0074] "Lessons learned" refer to the knowledge and strategies that should be adopted in new ways, based on the results of past projects.

[0075] This invention provides a knowledge management system for project management. Servers, terminals, and users collaborate to support project success.

[0076] Server Functions

[0077] The server receives project information and stores it in a data structure. The server uses a generative AI model to analyze past project information and classify successes and failures. It extracts important lessons and risks from the analysis results and updates the database. In particular, the generative AI model is used to recognize patterns from large amounts of data and gain new insights. For example, it can analyze "failures in the market research phase" to identify risks that should be avoided.

[0078] Device functions

[0079] The terminal provides a means for users to input project information through a user interface. Users use the terminal interface to input project progress, results, and problems. The terminal sends this information to the server. The terminal also functions as an interface for searching for project examples. When a user specifies criteria, the terminal retrieves relevant data from the server and displays it visually.

[0080] User roles

[0081] Users access the system to provide project data and contribute to shared knowledge resources. For example, when searching for similar past cases during a new project, users can enter the prompt "Tell me about past successful market research cases" and adjust the project's progress based on the information obtained. Based on this prompt, the server searches for relevant information and provides it to the user through the terminal.

[0082] This system allows users to effectively utilize accumulated knowledge and receive consistent support from project planning to execution, thereby improving the success rate of projects.

[0083] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0084] Step 1:

[0085] The user enters project information using the terminal's interface. This information includes the project name, progress, results, and issues. This data is then formatted and sent to the server by the terminal.

[0086] Step 2:

[0087] The server retrieves project information received from the terminal and saves it to the database. Here, the server validates the data to ensure its format is correct. If there are any errors, it generates an error message and sends it back to the terminal.

[0088] Step 3:

[0089] The server analyzes the stored information and compares it to similar project data using a generative AI model. In this analysis process, the server classifies past successes and failures and extracts new lessons and risks. Specifically, the model uses pattern matching to identify key points.

[0090] Step 4:

[0091] Based on the analysis results, the server updates the database. New lessons are added, and existing knowledge is reinforced. The server uses the updated information to prepare to respond to future search requests from users.

[0092] Step 5:

[0093] The user uses the terminal interface to search for past cases based on specific criteria. When the user enters the prompt "Tell me about past successful market research cases," the terminal sends a request to the server.

[0094] Step 6:

[0095] The server searches the database in response to the prompt and identifies the appropriate case. From the identified case, it extracts relevant lessons and success strategies and sends them to the terminal.

[0096] Step 7:

[0097] The terminal visually displays information received from the server to the user. The information is displayed as graphs and charts, processed into a format that is easily interpretable by the user.

[0098] Step 8:

[0099] Users utilize the presented information to consider ways to improve the project. They derive necessary strategies from the obtained data and formulate concrete plans to improve the project's success rate.

[0100] (Application Example 1)

[0101] 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."

[0102] To achieve efficiency improvements and early detection of anomalies in the manufacturing process, it is crucial to collect and analyze large amounts of work data in real time. However, conventional methods involve cumbersome data management and make it difficult to efficiently utilize past case studies. Furthermore, there is a problem in that work optimization and anomaly prevention are not functioning adequately.

[0103] 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.

[0104] In this invention, the server includes means for receiving work data, storing it in an aggregation device, and analyzing the data; means for updating the generated knowledge and searching for past examples based on specified conditions; means for generating and providing useful return routes for the work to the user; and means for providing return routes to the device for optimizing the manufacturing process. This enables increased efficiency of the manufacturing line and early detection of anomalies.

[0105] "Work data" refers to information regarding the implementation status of each process and task in the manufacturing process, and includes numerical values ​​and indicators such as work time, error rate, and material consumption.

[0106] "Data aggregation device" refers to equipment or systems for centrally storing and managing received data, and includes databases and cloud storage.

[0107] "Knowledge" refers to useful information and lessons learned from past data analysis, as well as advice based on success and failure stories.

[0108] A "model" refers to a case where performance or results were good under specific conditions, based on past case data, and serves as a standard for reference.

[0109] "Return route" refers to the path for improvement measures and feedback provided based on information and analysis results obtained during manufacturing and operations.

[0110] "Equipment" refers to all hardware used for manufacturing, including the machines and equipment themselves that are placed on the production line.

[0111] The server collects data from the manufacturing process and stores it in an aggregation device. This makes it possible to unify the management of important indicators such as work time, error rate, and material consumption at each stage of the process. Databases and cloud storage function as the aggregation device. The server further analyzes the aggregated data using AI models and extracts information for anomaly detection and optimization. Specifically, it uses programming languages ​​such as Python and data analysis libraries such as Pandas and Scikit-learn.

[0112] Users can access the server via a terminal and receive feedback for optimizing the manufacturing process and detecting anomalies. The terminal provides an easy-to-use interface, allowing users to search for past examples and develop improvement measures based on the knowledge gained.

[0113] For example, in a parts assembly process on a manufacturing line, the system searches for past instances of quality problems and uses that information to improve specific processes. Examples of prompts include, "Use past manufacturing project data to suggest improvements for the next manufacturing process," and "Utilize the AI ​​model to optimize the anomaly detection algorithm during manufacturing." This enables real-time improvement of the manufacturing line through the collaboration of the server and terminals.

[0114] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0115] Step 1:

[0116] The server collects real-time work data from the manufacturing line and stores it in a data aggregation device. Inputs include process information obtained from sensors and machine operation data. This data is not only stored as is, but is also appropriately classified and structured in a database to improve accessibility.

[0117] Step 2:

[0118] The server uses data stored in the integrated device to perform analysis that utilizes AI models to detect anomalies and inefficiencies. It uses stored historical manufacturing data and current real-time data as input. Using the predictive algorithms provided by the AI ​​model, it identifies unique patterns, extracts risk factors and inefficient parts of the process, and generates data alerts and optimization suggestions as output.

[0119] Step 3:

[0120] Users access the server via their terminal and receive feedback and analysis results from the server. Through the user interface, they can search for past examples based on specific conditions and utilize that knowledge. The database is searched based on user-specified conditions and keywords, and the output includes past examples and visualized information representing the acquired knowledge.

[0121] Step 4:

[0122] Based on feedback from the server, users implement specific improvements to the manufacturing process. Specifically, they adjust on-site operations and modify equipment settings based on the causes of anomalies and points for efficiency improvements indicated in the feedback. This results in the optimization of the manufacturing process, leading to improved product quality and increased productivity.

[0123] 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.

[0124] This invention is a system that incorporates an emotion engine to recognize user emotions, with the aim of providing effective feedback and improvement measures in project management. Specific embodiments are shown below.

[0125] Server Functions

[0126] The server has the basic function of receiving project data and storing it in a database. Furthermore, this system uses an emotion engine to acquire user emotion data. This emotion data includes emotional states extracted through facial recognition and text analysis when the user takes certain actions. The server analyzes this emotion data and uses it for project feedback. An AI model generates feedback by combining this emotion data, providing users with more personalized support.

[0127] Device functions

[0128] The terminal provides an interface for users to input project progress information. It also features an emotion engine and is equipped with sensors and cameras to recognize the user's emotions in real time. While the user inputs project information, the terminal detects changes in the user's emotions via the emotion engine and transmits that data to the server.

[0129] User roles

[0130] Users input project progress and challenges using their terminals as usual. During this process, the emotion engine works to analyze the user's emotional state. For example, if signs of stress are detected when the user is facing a particular problem, the system can provide appropriate feedback. The feedback provided will include content tailored to the user's motivation and stress level, and will suggest specific steps and support to help solve the problem.

[0131] In this way, a system equipped with an emotional engine can provide support that takes into account the psychological state of project participants, thereby facilitating smoother project progress and improving outcomes.

[0132] The following describes the processing flow.

[0133] Step 1:

[0134] The user inputs project details, progress, and problems through the terminal's interface. During the input process, the terminal uses its built-in sensors and camera to acquire emotional data from the user's facial expressions and voice.

[0135] Step 2:

[0136] The device analyzes the acquired emotional data using an emotion engine to recognize the user's emotional state in real time. The recognized emotional state is organized along with project data and transferred to the server.

[0137] Step 3:

[0138] The server receives project data and sentiment data sent from the terminal. This data is recorded in a database, and lessons learned and risks are extracted based on the project's success and failure history.

[0139] Step 4:

[0140] The server's AI model takes received emotional data into account to generate feedback about the project. Based on the user's emotional state, including their motivation and stress levels, it customizes the feedback to suggest appropriate improvements and support.

[0141] Step 5:

[0142] The server sends the generated feedback to the device. The feedback includes specific action plans and recommendations tailored to the user's emotional state.

[0143] Step 6:

[0144] The device displays the received feedback to the user. Based on the feedback provided, the user can concretize ways to improve the project and gain guidance for moving on to the next step.

[0145] Step 7:

[0146] Users carefully consider the feedback and incorporate it into the project's progress. By incorporating feedback based on emotional data, users can maintain their motivation and efficiently advance the project.

[0147] (Example 2)

[0148] 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".

[0149] In project management, providing feedback without considering the user's psychological state can hinder the smooth progress and improved results of a project. Furthermore, a challenge with traditional systems is that feedback is uniform and lacks personalization tailored to the individual user's emotions and needs.

[0150] 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.

[0151] In this invention, the server includes means for receiving and analyzing project information, means for extracting user emotional data using emotion recognition technology, and means for analyzing the emotional data using a generative AI model and creating personalized feedback for the user. This makes it possible to provide flexible and effective feedback that takes into account the user's emotional state.

[0152] "Project information" refers to the project's progress, tasks, goals, and all related data.

[0153] A "storage device" refers to a hardware or software medium for storing digital data.

[0154] "Means of analysis" refers to the process of processing received data using algorithms and programs to extract useful information.

[0155] "Emotion recognition technology" refers to technology that identifies and classifies a user's emotional state from their facial expressions, voice, text, etc.

[0156] "User emotional data" refers to information related to the user's psychological state, extracted using emotion recognition technology.

[0157] A "generative AI model" refers to an artificial intelligence system that uses machine learning algorithms to learn patterns from data and perform predictions and inferences.

[0158] "Feedback" refers to the process of providing users with evaluations and advice regarding the progress of a project.

[0159] "Personalized feedback" refers to specific advice and support tailored to the emotional state and needs of each individual user.

[0160] This system combines advanced emotion recognition technology and AI models to improve the feedback process in project management. An embodiment of this system is shown below.

[0161] The server is responsible for receiving project information and storing it in storage. The server also receives user emotion data extracted by emotion recognition technology. This technology includes algorithms that analyze the user's facial expressions and voice tone to identify emotional states such as joy, anger, and stress. The server analyzes this data using a generative AI model to create personalized feedback tailored to the user. This feedback is designed to support the efficient progress of the project.

[0162] The terminal provides an interface that allows users to easily input information about project progress. In particular, the terminal is equipped with sensors such as a camera and microphone, which are used to recognize the user's emotions in real time. This emotion data is continuously transmitted to the server during project input and used to generate feedback.

[0163] As a concrete example, when a user inputs the progress of a project, the terminal records the user's facial expressions corresponding to this information. For instance, if a user is struggling with a particular issue, the emotion recognition technology can detect subtle signs of stress. Based on this, the server generates feedback suggesting stress management techniques and appropriate ways to take breaks.

[0164] An example of a prompt message is as follows: "Please enter the recent project progress. Please describe the current issue in detail, including its content and progress. Also, please tell us how you felt when facing this issue." Through this specific example, the system generates feedback that takes the user's emotional state into account, enabling more effective project management.

[0165] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0166] Step 1:

[0167] The terminal provides an interface for users to input project-related information. Users enter task progress and related issues into the terminal. The entered information is recorded as text data. This data accurately reflects the user's intent and serves as preparation for sending detailed project management information to the server.

[0168] Step 2:

[0169] When input begins, the device activates its camera and microphone and runs an emotion engine to sense the user's emotions in real time. It detects and analyzes the user's facial expressions and voice tone to generate emotion data. This emotion data includes information about the user's psychological state. This data is temporarily stored on the device and sent to a server for subsequent analysis steps.

[0170] Step 3:

[0171] The server receives project information and sentiment data sent from the terminal. The received data is stored in a database. Next, the server analyzes this data using a generative AI model. The AI ​​model processes the text data of the project information and the sentiment data to generate personalized feedback based on the user's emotional state. The AI ​​model learns the relationships between the data and extracts information that is useful to the user.

[0172] Step 4:

[0173] The server sends the generated feedback to the terminal. This feedback includes solutions to specific challenges the user is facing, as well as support that takes into account their emotional state. The terminal displays this feedback to the user, allowing them to improve or adjust the project's progress based on it. The feedback helps the user understand the project and facilitates smoother project management.

[0174] (Application Example 2)

[0175] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0176] In project management, the emotions and psychological state of workers can significantly impact project progress and productivity. However, existing systems often fail to consider user emotions, resulting in an inability to provide appropriate feedback and hindering efficient project progress. Furthermore, achieving smooth collaboration between workers and robots is difficult in physical work environments such as factories.

[0177] 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.

[0178] In this invention, the server includes means for acquiring project information, storing it in a memory device, and analyzing the information; means for updating the generated knowledge and searching past cases based on specified conditions; means for generating and providing feedback that brings convenience to the project to the user; and means for providing personalized feedback using an emotion engine that recognizes the user's emotions. This makes it possible to provide feedback that takes into account the user's emotional state in real time, streamlining the progress of the project and facilitating smooth collaboration between workers and robots.

[0179] "Project information" refers to data such as the project's progress, work content, schedule, and issues.

[0180] A "storage device" is a physical or virtual medium used to store and save data, and includes, for example, hard disks and cloud storage.

[0181] "Means of analysis" refer to the processes and methods used to analyze acquired information and extract useful insights.

[0182] "Knowledge" refers to information and guidelines obtained through analysis, which are useful for making appropriate decisions regarding specific situations.

[0183] "Means for exploring past cases" refers to a function for searching for and referencing past examples of similar projects or situations.

[0184] "Convenience-enhancing feedback" refers to constructive and helpful information and recommendations that support users' work and project management.

[0185] "Users" refers to people who use the system, including project managers and workers.

[0186] An "emotion engine" is a technology or algorithm used to recognize and analyze a user's emotional state.

[0187] "Personalized feedback" refers to feedback that is customized to the user's emotional state and circumstances, addressing specific needs and challenges.

[0188] The system that implements this application example includes three main components: a server, a terminal, and a user.

[0189] The server receives project information and stores it in storage. Storage devices such as hard disks or cloud storage are used. Next, to analyze the information, the server uses analysis software to analyze the project data and extract useful insights. Based on this analysis, a function to explore past cases is activated, searching for and referencing similar project examples. Furthermore, an emotion engine is incorporated to recognize and analyze the user's emotional state and generate personalized feedback. This enables personalized support tailored to the user's situation.

[0190] The terminal is an interface for users to input project information and is equipped with sensors and a camera. This hardware is used to detect the user's emotions in real time and transmit that data to the server. The user interface is designed to minimize the effort required to input project progress details.

[0191] Users utilize the system using smart glasses or devices to manage the status of their projects. For example, if a user experiences stress when facing a specific challenge during work, the device's emotion engine analyzes their state, and appropriate feedback is provided from the server. This allows users to solve problems more efficiently and ensure smooth project progress.

[0192] As a concrete example, consider using smart glasses to improve work efficiency among factory workers. When a worker feels anxious, they can receive specific instructions through the smart glasses, such as, "Changing the placement of parts will increase efficiency." This support reduces the psychological burden on workers and improves productivity.

[0193] The following are examples of prompts for a generative AI model:

[0194] "What unique feedback can factory robots provide when workers are feeling stressed?"

[0195] "Please explain the specific methods for using smart glasses to assist with tasks."

[0196] In this way, this system enables efficient project management and optimization of the work environment.

[0197] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0198] Step 1:

[0199] The server receives project information from the terminal. The input includes data regarding the project's progress and issues. The server stores this information in its memory, preparing it for later analysis.

[0200] Step 2:

[0201] The terminal collects project information entered through the user interface. As the user enters data, sensors and cameras on the terminal also activate, and the emotion engine recognizes the user's emotional state in real time, sending it to the server as digital data.

[0202] Step 3:

[0203] The server analyzes the received project information and sentiment data. Analysis software is used to extract characteristics of the project information, and the sentiment data is analyzed by a generating AI model. This analysis searches the database for similar past cases and identifies similar patterns.

[0204] Step 4:

[0205] The server generates feedback best suited to the user's situation based on analysis results and past cases. It utilizes an emotion engine to personalize the feedback based on emotions and build personalized support messages.

[0206] Step 5:

[0207] Users receive feedback through smart glasses or devices. This feedback includes suggestions for improving specific work procedures and increasing efficiency. For example, it may suggest improvements to the layout or changes to procedures at the work site.

[0208] Step 6:

[0209] The terminal or server monitors the effectiveness of the feedback and verifies that users are appropriately utilizing the feedback to improve the project. If no improvement is seen, it adjusts and provides further feedback.

[0210] Inputs and outputs at each step are automatically processed and calculated within the program. Examples of prompts include specific action instructions such as, "What unique feedback can a factory robot provide when a worker is stressed?" or "Please explain specific ways to use smart glasses for work assistance."

[0211] 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.

[0212] 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.

[0213] 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.

[0214] [Second Embodiment]

[0215] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0216] 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.

[0217] 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).

[0218] 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.

[0219] 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.

[0220] 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).

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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".

[0227] This invention provides a knowledge management system for project management, in which servers, terminals, and users interact to improve the success rate of projects. Specific embodiments are shown below.

[0228] Server Functions

[0229] The server has the function of receiving project data and storing it in a database. When a new project is completed, project-related information is entered by the assigned user via their terminal, and the server receives it. The server classifies the received data, identifies success and failure cases, and stores them in the database. The server also utilizes AI models to analyze past project data and extract important lessons and risks. Based on this knowledge, the database is updated, and search results are provided according to the conditions specified by the user.

[0230] Device functions

[0231] The terminal provides an interface for users to input project data. Through this interface, users can input detailed information about project progress, results, and problems, and send it to the server. The terminal also provides an interface to assist users in searching for past project examples. By specifying conditions, the terminal retrieves relevant data from the server in real time and displays it in an easy-to-understand visual format.

[0232] User roles

[0233] Users access this system to support project management. By using a terminal to input new project data, users can contribute to a shared knowledge resource. Furthermore, users can utilize the terminal's search function to investigate similar past cases and apply the lessons learned to improve the success rate of their own projects. For example, during the progress of a new project, they can search for "failures in the market research phase" to identify appropriate approaches and risks to avoid beforehand.

[0234] This allows system users to leverage accumulated knowledge to receive consistent support from project planning to execution, thereby improving the project's success rate.

[0235] The following describes the processing flow.

[0236] Step 1:

[0237] The user inputs project data for their ongoing project through the terminal interface. They accurately enter information such as project details, progress, results, and any problems encountered, and prepare it for transmission to the server.

[0238] Step 2:

[0239] The terminal organizes the project data entered by the user and sends it to the server in the appropriate format. Once the data transmission is complete, the terminal prepares to proceed to the next operation.

[0240] Step 3:

[0241] The server checks the project data received from the terminal and saves it to the database. It then categorizes the data based on whether the project was successful or unsuccessful, and which phase of the project it represents.

[0242] Step 4:

[0243] The server analyzes the stored project data using an AI model to extract lessons learned and risk factors. The information obtained through this process is integrated into the existing knowledge base, and the search index is updated.

[0244] Step 5:

[0245] The user enters a query on their device to search for past project examples. They specify search criteria and define the details of the project for which they want to retrieve relevant knowledge.

[0246] Step 6:

[0247] The terminal sends the user's search query to the server. The server searches the database according to the specified conditions and finds a suitable past project example.

[0248] Step 7:

[0249] The server sends the terminal with success and failure stories of related projects as search results. The server also generates and provides feedback and suggestions for improvement extracted from the relevant projects.

[0250] Step 8:

[0251] The terminal visually displays search results received from the server to the user. The user then uses this information to review data useful for the progress of their project and to inform their decision-making.

[0252] Step 9:

[0253] Based on feedback from the server, users will improve project planning and execution methods. For example, they might consider and apply workarounds for problems faced by similar projects in the past.

[0254] (Example 1)

[0255] 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."

[0256] A challenge in project management is to streamline the accumulation and utilization of knowledge. Traditional methods have made it difficult to systematically manage project successes and failures and learn from them, and knowledge transfer has often been insufficient. As a result, the same mistakes were repeated, making it difficult to improve project success rates.

[0257] 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.

[0258] In this invention, the server includes means for receiving project information and storing it in a data structure, means for updating the generated knowledge and searching past cases based on specified conditions, and means for extracting important lessons and risks based on the analyzed results and providing them to the user. This makes it possible to effectively accumulate knowledge about the project and utilize it in future projects.

[0259] "Project information" refers to data related to the progress of a project, including the start date, planned end date, progress status, results, and problems.

[0260] A "data structure" refers to a framework for organizing and storing information, and includes databases and other record formats.

[0261] "Analysis" is the process of examining received data in detail to identify trends and patterns.

[0262] "Knowledge" refers to information that can be used in future projects, such as lessons learned, strategies, and risk information gained from the progress of a project.

[0263] A "generative AI model" is a machine learning model that performs pattern recognition and prediction based on a large amount of historical data.

[0264] "User interface" refers to the screen on which a user operates when entering or searching for data on a device, and includes the display of visual information.

[0265] "Success stories and failure stories" refer to information recorded as a result of effective strategies or incorrect decisions made in past projects.

[0266] "Feedback" refers to advice and information generated based on past data analysis results, which serves as a guideline for improving the project.

[0267] "Lessons learned" refer to the knowledge and strategies that should be adopted in new ways, based on the results of past projects.

[0268] This invention provides a knowledge management system for project management. Servers, terminals, and users collaborate to support project success.

[0269] Server Functions

[0270] The server receives project information and stores it in a data structure. The server uses a generative AI model to analyze past project information and classify successes and failures. It extracts important lessons and risks from the analysis results and updates the database. In particular, the generative AI model is used to recognize patterns from large amounts of data and gain new insights. For example, it can analyze "failures in the market research phase" to identify risks that should be avoided.

[0271] Device functions

[0272] The terminal provides a means for users to input project information through a user interface. Users use the terminal interface to input project progress, results, and problems. The terminal sends this information to the server. The terminal also functions as an interface for searching for project examples. When a user specifies criteria, the terminal retrieves relevant data from the server and displays it visually.

[0273] User roles

[0274] Users access the system to provide project data and contribute to shared knowledge resources. For example, when searching for similar past cases during a new project, users can enter the prompt "Tell me about past successful market research cases" and adjust the project's progress based on the information obtained. Based on this prompt, the server searches for relevant information and provides it to the user through the terminal.

[0275] With this system, users can effectively utilize the accumulated knowledge and receive consistent support from project planning to execution, thereby improving the success rate of projects.

[0276] The flow of the specific process in Example 1 will be described using FIG. 11.

[0277] Step 1:

[0278] The user inputs project information using the interface of the terminal. The information to be input includes the project name, progress, achievements, problems, etc. After this data is formatted, it is sent by the terminal to the server.

[0279] Step 2:

[0280] The server obtains the project information received from the terminal and stores it in the database. Here, the server verifies the data to confirm the consistency of the data format. If there are any deficiencies, an error message is generated and sent back to the terminal.

[0281] Step 3:

[0282] The server analyzes the stored information and compares it with similar project data using the generated AI model. In this analysis process, the server classifies past success cases and failure cases and extracts new lessons and risks. Specifically, the model performs pattern matching on the information to identify important points.

[0283] Step 4:

[0284] Based on the analysis results, the server updates the database. New lessons are added and the existing knowledge is strengthened. The server prepares to be able to respond to future search requests from the user by utilizing the updated information.

[0285] Step 5:

[0286] The user uses the interface of the terminal to search for past cases under specific conditions. When the prompt sentence "Tell me about past successful market research cases" is input, the terminal sends a request to the server.

[0287] Step 6:

[0288] The server searches the database according to the prompt sentence and identifies appropriate cases. From the identified cases, relevant lessons and successful strategies are extracted and sent to the terminal.

[0289] Step 7:

[0290] The terminal visually displays the information received from the server to the user. The information is displayed as graphs or charts and processed into a form that can be easily interpreted by the user.

[0291] Step 8:

[0292] The user utilizes the presented information to consider improvement measures for the project. Necessary strategies are derived from the obtained data, and a specific plan for improving the success rate of the project is formulated.

[0293] (Application Example 1)

[0294] 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".

[0295] In order to achieve efficiency improvement and early detection of abnormalities in the manufacturing process, it is important to collect and analyze a large amount of work data in real time. However, in the conventional method, data management is complicated, and it is difficult to efficiently utilize past cases. Furthermore, there is a problem that work optimization and abnormality prevention do not function sufficiently.

[0296] 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.

[0297] In this invention, the server includes means for receiving work data, storing it in an aggregation device, and analyzing the data; means for updating the generated knowledge and searching for past examples based on specified conditions; means for generating and providing useful return routes for the work to the user; and means for providing return routes to the device for optimizing the manufacturing process. This enables increased efficiency of the manufacturing line and early detection of anomalies.

[0298] "Work data" refers to information regarding the implementation status of each process and task in the manufacturing process, and includes numerical values ​​and indicators such as work time, error rate, and material consumption.

[0299] "Data aggregation device" refers to equipment or systems for centrally storing and managing received data, and includes databases and cloud storage.

[0300] "Knowledge" refers to useful information and lessons learned from past data analysis, as well as advice based on success and failure stories.

[0301] A "model" refers to a case where performance or results were good under specific conditions, based on past case data, and serves as a standard for reference.

[0302] "Return route" refers to the path for improvement measures and feedback provided based on information and analysis results obtained during manufacturing and operations.

[0303] "Equipment" refers to all hardware used for manufacturing, including the machines and equipment themselves that are placed on the production line.

[0304] The server collects the data of the operations in the manufacturing process and stores it in the integration device. This enables the unified management of important indicators such as the operation time, error rate, and material consumption in each process. A database or cloud storage functions as the integration device. The server further analyzes the collected data using an AI model and extracts information for anomaly detection and optimization. Specifically, programming languages such as Python and data analysis libraries such as Pandas and Scikit-learn are used.

[0305] Users can access the server via a terminal and receive feedback for optimizing the manufacturing process and detecting anomalies. The terminal is provided with an easy-to-operate interface, allowing users to search for past exemplars and formulate improvement measures based on the knowledge obtained.

[0306] As an example, in the component assembly process on a manufacturing line, search for cases where quality problems have occurred in the past and improve specific processes based on them. Examples of prompt texts include "Please present the improvement points for the next manufacturing process using the past manufacturing project data." and "Please optimize the anomaly detection algorithm during manufacturing by leveraging the AI model." This enables real-time improvement of the manufacturing line through the cooperation between the server and the terminal.

[0307] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0308] Step 1:

[0309] <Object: The server collects the data of the operations in real time from the manufacturing line and stores it in the integration device. The inputs include the process information obtained from sensors and the operation data of the machines. These data are not only stored as they are, but also appropriately classified and structured in the database to improve accessibility.

[0310] Step 2:

[0311] The server uses data stored in the integrated device to perform analysis that utilizes AI models to detect anomalies and inefficiencies. It uses stored historical manufacturing data and current real-time data as input. Using the predictive algorithms provided by the AI ​​model, it identifies unique patterns, extracts risk factors and inefficient parts of the process, and generates data alerts and optimization suggestions as output.

[0312] Step 3:

[0313] Users access the server via their terminal and receive feedback and analysis results from the server. Through the user interface, they can search for past examples based on specific conditions and utilize that knowledge. The database is searched based on user-specified conditions and keywords, and the output includes past examples and visualized information representing the acquired knowledge.

[0314] Step 4:

[0315] Based on feedback from the server, users implement specific improvements to the manufacturing process. Specifically, they adjust on-site operations and modify equipment settings based on the causes of anomalies and points for efficiency improvements indicated in the feedback. This results in the optimization of the manufacturing process, leading to improved product quality and increased productivity.

[0316] 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.

[0317] This invention is a system that incorporates an emotion engine to recognize user emotions, with the aim of providing effective feedback and improvement measures in project management. Specific embodiments are shown below.

[0318] Server Functions

[0319] The server has the basic function of receiving project data and storing it in a database. Furthermore, this system uses an emotion engine to acquire user emotion data. This emotion data includes emotional states extracted through facial recognition and text analysis when the user takes certain actions. The server analyzes this emotion data and uses it for project feedback. An AI model generates feedback by combining this emotion data, providing users with more personalized support.

[0320] Device functions

[0321] The terminal provides an interface for users to input project progress information. It also features an emotion engine and is equipped with sensors and cameras to recognize the user's emotions in real time. While the user inputs project information, the terminal detects changes in the user's emotions via the emotion engine and transmits that data to the server.

[0322] User roles

[0323] Users input project progress and challenges using their terminals as usual. During this process, the emotion engine works to analyze the user's emotional state. For example, if signs of stress are detected when the user is facing a particular problem, the system can provide appropriate feedback. The feedback provided will include content tailored to the user's motivation and stress level, and will suggest specific steps and support to help solve the problem.

[0324] In this way, a system equipped with an emotional engine can provide support that takes into account the psychological state of project participants, thereby facilitating smoother project progress and improving outcomes.

[0325] The following describes the processing flow.

[0326] Step 1:

[0327] The user inputs project details, progress, and problems through the terminal's interface. During the input process, the terminal uses its built-in sensors and camera to acquire emotional data from the user's facial expressions and voice.

[0328] Step 2:

[0329] The device analyzes the acquired emotional data using an emotion engine to recognize the user's emotional state in real time. The recognized emotional state is organized along with project data and transferred to the server.

[0330] Step 3:

[0331] The server receives project data and sentiment data sent from the terminal. This data is recorded in a database, and lessons learned and risks are extracted based on the project's success and failure history.

[0332] Step 4:

[0333] The server's AI model takes received emotional data into account to generate feedback about the project. Based on the user's emotional state, including their motivation and stress levels, it customizes the feedback to suggest appropriate improvements and support.

[0334] Step 5:

[0335] The server sends the generated feedback to the device. The feedback includes specific action plans and recommendations tailored to the user's emotional state.

[0336] Step 6:

[0337] The device displays the received feedback to the user. Based on the feedback provided, the user can concretize ways to improve the project and gain guidance for moving on to the next step.

[0338] Step 7:

[0339] Users carefully consider the feedback and incorporate it into the project's progress. By incorporating feedback based on emotional data, users can maintain their motivation and efficiently advance the project.

[0340] (Example 2)

[0341] 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".

[0342] In project management, providing feedback without considering the user's psychological state can hinder the smooth progress and improved results of a project. Furthermore, a challenge with traditional systems is that feedback is uniform and lacks personalization tailored to the individual user's emotions and needs.

[0343] 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.

[0344] In this invention, the server includes means for receiving and analyzing project information, means for extracting user emotional data using emotion recognition technology, and means for analyzing the emotional data using a generative AI model and creating personalized feedback for the user. This makes it possible to provide flexible and effective feedback that takes into account the user's emotional state.

[0345] "Project information" refers to the project's progress, tasks, goals, and all related data.

[0346] A "storage device" refers to a hardware or software medium for storing digital data.

[0347] "Means of analysis" refers to the process of processing received data using algorithms and programs to extract useful information.

[0348] "Emotion recognition technology" refers to technology that identifies and classifies a user's emotional state from their facial expressions, voice, text, etc.

[0349] "User emotional data" refers to information related to the user's psychological state, extracted using emotion recognition technology.

[0350] A "generative AI model" refers to an artificial intelligence system that uses machine learning algorithms to learn patterns from data and perform predictions and inferences.

[0351] "Feedback" refers to the process of providing users with evaluations and advice regarding the progress of a project.

[0352] "Personalized feedback" refers to specific advice and support tailored to the emotional state and needs of each individual user.

[0353] This system combines advanced emotion recognition technology and AI models to improve the feedback process in project management. An embodiment of this system is shown below.

[0354] The server is responsible for receiving project information and storing it in storage. The server also receives user emotion data extracted by emotion recognition technology. This technology includes algorithms that analyze the user's facial expressions and voice tone to identify emotional states such as joy, anger, and stress. The server analyzes this data using a generative AI model to create personalized feedback tailored to the user. This feedback is designed to support the efficient progress of the project.

[0355] The terminal provides an interface that allows users to easily input information about project progress. In particular, the terminal is equipped with sensors such as a camera and microphone, which are used to recognize the user's emotions in real time. This emotion data is continuously transmitted to the server during project input and used to generate feedback.

[0356] As a concrete example, when a user inputs the progress of a project, the terminal records the user's facial expressions corresponding to this information. For instance, if a user is struggling with a particular issue, the emotion recognition technology can detect subtle signs of stress. Based on this, the server generates feedback suggesting stress management techniques and appropriate ways to take breaks.

[0357] An example of a prompt message is as follows: "Please enter the recent project progress. Please describe the current issue in detail, including its content and progress. Also, please tell us how you felt when facing this issue." Through this specific example, the system generates feedback that takes the user's emotional state into account, enabling more effective project management.

[0358] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0359] Step 1:

[0360] The terminal provides an interface for users to input project-related information. Users enter task progress and related issues into the terminal. The entered information is recorded as text data. This data accurately reflects the user's intent and serves as preparation for sending detailed project management information to the server.

[0361] Step 2:

[0362] When input begins, the device activates its camera and microphone and runs an emotion engine to sense the user's emotions in real time. It detects and analyzes the user's facial expressions and voice tone to generate emotion data. This emotion data includes information about the user's psychological state. This data is temporarily stored on the device and sent to a server for subsequent analysis steps.

[0363] Step 3:

[0364] The server receives project information and sentiment data sent from the terminal. The received data is stored in a database. Next, the server analyzes this data using a generative AI model. The AI ​​model processes the text data of the project information and the sentiment data to generate personalized feedback based on the user's emotional state. The AI ​​model learns the relationships between the data and extracts information that is useful to the user.

[0365] Step 4:

[0366] The server sends the generated feedback to the terminal. This feedback includes solutions to specific challenges the user is facing, as well as support that takes into account their emotional state. The terminal displays this feedback to the user, allowing them to improve or adjust the project's progress based on it. The feedback helps the user understand the project and facilitates smoother project management.

[0367] (Application Example 2)

[0368] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0369] In project management, the emotions and psychological state of workers can significantly impact project progress and productivity. However, existing systems often fail to consider user emotions, resulting in an inability to provide appropriate feedback and hindering efficient project progress. Furthermore, achieving smooth collaboration between workers and robots is difficult in physical work environments such as factories.

[0370] 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.

[0371] In this invention, the server includes means for acquiring project information, storing it in a memory device, and analyzing the information; means for updating the generated knowledge and searching past cases based on specified conditions; means for generating and providing feedback that brings convenience to the project to the user; and means for providing personalized feedback using an emotion engine that recognizes the user's emotions. This makes it possible to provide feedback that takes into account the user's emotional state in real time, streamlining the progress of the project and facilitating smooth collaboration between workers and robots.

[0372] "Project information" refers to data such as the project's progress, work content, schedule, and issues.

[0373] A "storage device" is a physical or virtual medium used to store and save data, and includes, for example, hard disks and cloud storage.

[0374] "Means of analysis" refer to the processes and methods used to analyze acquired information and extract useful insights.

[0375] "Knowledge" refers to information and guidelines obtained through analysis, which are useful for making appropriate decisions regarding specific situations.

[0376] "Means for exploring past cases" refers to a function for searching for and referencing past examples of similar projects or situations.

[0377] "Convenience-enhancing feedback" refers to constructive and helpful information and recommendations that support users' work and project management.

[0378] "Users" refers to people who use the system, including project managers and workers.

[0379] An "emotion engine" is a technology or algorithm used to recognize and analyze a user's emotional state.

[0380] "Personalized feedback" refers to feedback that is customized to the user's emotional state and circumstances, addressing specific needs and challenges.

[0381] The system that implements this application example includes three main components: a server, a terminal, and a user.

[0382] The server receives project information and stores it in storage. Storage devices such as hard disks or cloud storage are used. Next, to analyze the information, the server uses analysis software to analyze the project data and extract useful insights. Based on this analysis, a function to explore past cases is activated, searching for and referencing similar project examples. Furthermore, an emotion engine is incorporated to recognize and analyze the user's emotional state and generate personalized feedback. This enables personalized support tailored to the user's situation.

[0383] The terminal is an interface for users to input project information and is equipped with sensors and a camera. This hardware is used to detect the user's emotions in real time and transmit that data to the server. The user interface is designed to minimize the effort required to input project progress details.

[0384] Users utilize the system using smart glasses or devices to manage the status of their projects. For example, if a user experiences stress when facing a specific challenge during work, the device's emotion engine analyzes their state, and appropriate feedback is provided from the server. This allows users to solve problems more efficiently and ensure smooth project progress.

[0385] As a concrete example, consider using smart glasses to improve work efficiency among factory workers. When a worker feels anxious, they can receive specific instructions through the smart glasses, such as, "Changing the placement of parts will increase efficiency." This support reduces the psychological burden on workers and improves productivity.

[0386] The following are examples of prompts for a generative AI model:

[0387] "What unique feedback can factory robots provide when workers are feeling stressed?"

[0388] "Please explain the specific methods for using smart glasses to assist with tasks."

[0389] In this way, this system enables efficient project management and optimization of the work environment.

[0390] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0391] Step 1:

[0392] The server receives project information from the terminal. The input includes data regarding the project's progress and issues. The server stores this information in its memory, preparing it for later analysis.

[0393] Step 2:

[0394] The terminal collects project information entered through the user interface. As the user enters data, sensors and cameras on the terminal also activate, and the emotion engine recognizes the user's emotional state in real time, sending it to the server as digital data.

[0395] Step 3:

[0396] The server analyzes the received project information and sentiment data. Analysis software is used to extract characteristics of the project information, and the sentiment data is analyzed by a generating AI model. This analysis searches the database for similar past cases and identifies similar patterns.

[0397] Step 4:

[0398] The server generates feedback best suited to the user's situation based on analysis results and past cases. It utilizes an emotion engine to personalize the feedback based on emotions and build personalized support messages.

[0399] Step 5:

[0400] Users receive feedback through smart glasses or devices. This feedback includes suggestions for improving specific work procedures and increasing efficiency. For example, it may suggest improvements to the layout or changes to procedures at the work site.

[0401] Step 6:

[0402] The terminal or server monitors the effectiveness of the feedback and verifies that users are appropriately utilizing the feedback to improve the project. If no improvement is seen, it adjusts and provides further feedback.

[0403] Inputs and outputs at each step are automatically processed and calculated within the program. Examples of prompts include specific action instructions such as, "What unique feedback can a factory robot provide when a worker is stressed?" or "Please explain specific ways to use smart glasses for work assistance."

[0404] 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.

[0405] 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.

[0406] 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.

[0407] [Third Embodiment]

[0408] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0409] 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.

[0410] 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).

[0411] 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.

[0412] 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.

[0413] 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).

[0414] 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.

[0415] 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.

[0416] 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.

[0417] 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.

[0418] 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.

[0419] 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".

[0420] This invention provides a knowledge management system for project management, in which servers, terminals, and users interact to improve the success rate of projects. Specific embodiments are shown below.

[0421] Server Functions

[0422] The server has the function of receiving project data and storing it in a database. When a new project is completed, project-related information is entered by the assigned user via their terminal, and the server receives it. The server classifies the received data, identifies success and failure cases, and stores them in the database. The server also utilizes AI models to analyze past project data and extract important lessons and risks. Based on this knowledge, the database is updated, and search results are provided according to the conditions specified by the user.

[0423] Device functions

[0424] The terminal provides an interface for users to input project data. Through this interface, users can input detailed information about project progress, results, and problems, and send it to the server. The terminal also provides an interface to assist users in searching for past project examples. By specifying conditions, the terminal retrieves relevant data from the server in real time and displays it in an easy-to-understand visual format.

[0425] User roles

[0426] Users access this system to support project management. By using a terminal to input new project data, users can contribute to a shared knowledge resource. Furthermore, users can utilize the terminal's search function to investigate similar past cases and apply the lessons learned to improve the success rate of their own projects. For example, during the progress of a new project, they can search for "failures in the market research phase" to identify appropriate approaches and risks to avoid beforehand.

[0427] This allows system users to leverage accumulated knowledge to receive consistent support from project planning to execution, thereby improving the project's success rate.

[0428] The following describes the processing flow.

[0429] Step 1:

[0430] The user inputs project data for their ongoing project through the terminal interface. They accurately enter information such as project details, progress, results, and any problems encountered, and prepare it for transmission to the server.

[0431] Step 2:

[0432] The terminal organizes the project data entered by the user and sends it to the server in the appropriate format. Once the data transmission is complete, the terminal prepares to proceed to the next operation.

[0433] Step 3:

[0434] The server checks the project data received from the terminal and saves it to the database. It then categorizes the data based on whether the project was successful or unsuccessful, and which phase of the project it represents.

[0435] Step 4:

[0436] The server analyzes the stored project data using an AI model to extract lessons learned and risk factors. The information obtained through this process is integrated into the existing knowledge base, and the search index is updated.

[0437] Step 5:

[0438] The user enters a query on their device to search for past project examples. They specify search criteria and define the details of the project for which they want to retrieve relevant knowledge.

[0439] Step 6:

[0440] The terminal sends the user's search query to the server. The server searches the database according to the specified conditions and finds a suitable past project example.

[0441] Step 7:

[0442] The server sends the terminal with success and failure stories of related projects as search results. The server also generates and provides feedback and suggestions for improvement extracted from the relevant projects.

[0443] Step 8:

[0444] The terminal visually displays search results received from the server to the user. The user then uses this information to review data useful for the progress of their project and to inform their decision-making.

[0445] Step 9:

[0446] Based on feedback from the server, users will improve project planning and execution methods. For example, they might consider and apply workarounds for problems faced by similar projects in the past.

[0447] (Example 1)

[0448] 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."

[0449] A challenge in project management is to streamline the accumulation and utilization of knowledge. Traditional methods have made it difficult to systematically manage project successes and failures and learn from them, and knowledge transfer has often been insufficient. As a result, the same mistakes were repeated, making it difficult to improve project success rates.

[0450] 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.

[0451] In this invention, the server includes means for receiving project information and storing it in a data structure, means for updating the generated knowledge and searching past cases based on specified conditions, and means for extracting important lessons and risks based on the analyzed results and providing them to the user. This makes it possible to effectively accumulate knowledge about the project and utilize it in future projects.

[0452] "Project information" refers to data related to the progress of a project, including the start date, planned end date, progress status, results, and problems.

[0453] A "data structure" refers to a framework for organizing and storing information, and includes databases and other record formats.

[0454] "Analysis" is the process of examining received data in detail to identify trends and patterns.

[0455] "Knowledge" refers to information that can be used in future projects, such as lessons learned, strategies, and risk information gained from the progress of a project.

[0456] A "generative AI model" is a machine learning model that performs pattern recognition and prediction based on a large amount of historical data.

[0457] "User interface" refers to the screen on which a user operates when entering or searching for data on a device, and includes the display of visual information.

[0458] "Success stories and failure stories" refer to information recorded as a result of effective strategies or incorrect decisions made in past projects.

[0459] "Feedback" refers to advice and information generated based on past data analysis results, which serves as a guideline for improving the project.

[0460] "Lessons learned" refer to the knowledge and strategies that should be adopted in new ways, based on the results of past projects.

[0461] This invention provides a knowledge management system for project management. Servers, terminals, and users collaborate to support project success.

[0462] Server Functions

[0463] The server receives project information and stores it in a data structure. The server uses a generative AI model to analyze past project information and classify successes and failures. It extracts important lessons and risks from the analysis results and updates the database. In particular, the generative AI model is used to recognize patterns from large amounts of data and gain new insights. For example, it can analyze "failures in the market research phase" to identify risks that should be avoided.

[0464] Device functions

[0465] The terminal provides a means for users to input project information through a user interface. Users use the terminal interface to input project progress, results, and problems. The terminal sends this information to the server. The terminal also functions as an interface for searching for project examples. When a user specifies criteria, the terminal retrieves relevant data from the server and displays it visually.

[0466] User roles

[0467] Users access the system to provide project data and contribute to shared knowledge resources. For example, when searching for similar past cases during a new project, users can enter the prompt "Tell me about past successful market research cases" and adjust the project's progress based on the information obtained. Based on this prompt, the server searches for relevant information and provides it to the user through the terminal.

[0468] This system allows users to effectively utilize accumulated knowledge and receive consistent support from project planning to execution, thereby improving the success rate of projects.

[0469] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0470] Step 1:

[0471] The user enters project information using the terminal's interface. This information includes the project name, progress, results, and issues. This data is then formatted and sent to the server by the terminal.

[0472] Step 2:

[0473] The server retrieves project information received from the terminal and saves it to the database. Here, the server validates the data to ensure its format is correct. If there are any errors, it generates an error message and sends it back to the terminal.

[0474] Step 3:

[0475] The server analyzes the stored information and compares it to similar project data using a generative AI model. In this analysis process, the server classifies past successes and failures and extracts new lessons and risks. Specifically, the model uses pattern matching to identify key points.

[0476] Step 4:

[0477] Based on the analysis results, the server updates the database. New lessons are added, and existing knowledge is reinforced. The server uses the updated information to prepare to respond to future search requests from users.

[0478] Step 5:

[0479] The user uses the terminal interface to search for past cases based on specific criteria. When the user enters the prompt "Tell me about past successful market research cases," the terminal sends a request to the server.

[0480] Step 6:

[0481] The server searches the database in response to the prompt and identifies the appropriate case. From the identified case, it extracts relevant lessons and success strategies and sends them to the terminal.

[0482] Step 7:

[0483] The terminal visually displays information received from the server to the user. The information is displayed as graphs and charts, processed into a format that is easily interpretable by the user.

[0484] Step 8:

[0485] Users utilize the presented information to consider ways to improve the project. They derive necessary strategies from the obtained data and formulate concrete plans to improve the project's success rate.

[0486] (Application Example 1)

[0487] 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."

[0488] To achieve efficiency improvements and early detection of anomalies in the manufacturing process, it is crucial to collect and analyze large amounts of work data in real time. However, conventional methods involve cumbersome data management and make it difficult to efficiently utilize past case studies. Furthermore, there is a problem in that work optimization and anomaly prevention are not functioning adequately.

[0489] 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.

[0490] In this invention, the server includes means for receiving work data, storing it in an aggregation device, and analyzing the data; means for updating the generated knowledge and searching for past examples based on specified conditions; means for generating and providing useful return routes for the work to the user; and means for providing return routes to the device for optimizing the manufacturing process. This enables increased efficiency of the manufacturing line and early detection of anomalies.

[0491] "Work data" refers to information regarding the implementation status of each process and task in the manufacturing process, and includes numerical values ​​and indicators such as work time, error rate, and material consumption.

[0492] "Data aggregation device" refers to equipment or systems for centrally storing and managing received data, and includes databases and cloud storage.

[0493] "Knowledge" refers to useful information and lessons learned from past data analysis, as well as advice based on success and failure stories.

[0494] A "model" refers to a case where performance or results were good under specific conditions, based on past case data, and serves as a standard for reference.

[0495] "Return route" refers to the path for improvement measures and feedback provided based on information and analysis results obtained during manufacturing and operations.

[0496] "Equipment" refers to all hardware used for manufacturing, including the machines and equipment themselves that are placed on the production line.

[0497] The server collects data from the manufacturing process and stores it in an aggregation device. This makes it possible to unify the management of important indicators such as work time, error rate, and material consumption at each stage of the process. Databases and cloud storage function as the aggregation device. The server further analyzes the aggregated data using AI models and extracts information for anomaly detection and optimization. Specifically, it uses programming languages ​​such as Python and data analysis libraries such as Pandas and Scikit-learn.

[0498] Users can access the server via a terminal and receive feedback for optimizing the manufacturing process and detecting anomalies. The terminal provides an easy-to-use interface, allowing users to search for past examples and develop improvement measures based on the knowledge gained.

[0499] For example, in a parts assembly process on a manufacturing line, the system searches for past instances of quality problems and uses that information to improve specific processes. Examples of prompts include, "Use past manufacturing project data to suggest improvements for the next manufacturing process," and "Utilize the AI ​​model to optimize the anomaly detection algorithm during manufacturing." This enables real-time improvement of the manufacturing line through the collaboration of the server and terminals.

[0500] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0501] Step 1:

[0502] The server collects real-time work data from the manufacturing line and stores it in a data aggregation device. Inputs include process information obtained from sensors and machine operation data. This data is not only stored as is, but is also appropriately classified and structured in a database to improve accessibility.

[0503] Step 2:

[0504] The server uses data stored in the integrated device to perform analysis that utilizes AI models to detect anomalies and inefficiencies. It uses stored historical manufacturing data and current real-time data as input. Using the predictive algorithms provided by the AI ​​model, it identifies unique patterns, extracts risk factors and inefficient parts of the process, and generates data alerts and optimization suggestions as output.

[0505] Step 3:

[0506] Users access the server via their terminal and receive feedback and analysis results from the server. Through the user interface, they can search for past examples based on specific conditions and utilize that knowledge. The database is searched based on user-specified conditions and keywords, and the output includes past examples and visualized information representing the acquired knowledge.

[0507] Step 4:

[0508] Based on feedback from the server, users implement specific improvements to the manufacturing process. Specifically, they adjust on-site operations and modify equipment settings based on the causes of anomalies and points for efficiency improvements indicated in the feedback. This results in the optimization of the manufacturing process, leading to improved product quality and increased productivity.

[0509] 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.

[0510] This invention is a system that incorporates an emotion engine to recognize user emotions, with the aim of providing effective feedback and improvement measures in project management. Specific embodiments are shown below.

[0511] Server Functions

[0512] The server has the basic function of receiving project data and storing it in a database. Furthermore, this system uses an emotion engine to acquire user emotion data. This emotion data includes emotional states extracted through facial recognition and text analysis when the user takes certain actions. The server analyzes this emotion data and uses it for project feedback. An AI model generates feedback by combining this emotion data, providing users with more personalized support.

[0513] Device functions

[0514] The terminal provides an interface for users to input project progress information. It also features an emotion engine and is equipped with sensors and cameras to recognize the user's emotions in real time. While the user inputs project information, the terminal detects changes in the user's emotions via the emotion engine and transmits that data to the server.

[0515] User roles

[0516] Users input project progress and challenges using their terminals as usual. During this process, the emotion engine works to analyze the user's emotional state. For example, if signs of stress are detected when the user is facing a particular problem, the system can provide appropriate feedback. The feedback provided will include content tailored to the user's motivation and stress level, and will suggest specific steps and support to help solve the problem.

[0517] In this way, a system equipped with an emotional engine can provide support that takes into account the psychological state of project participants, thereby facilitating smoother project progress and improving outcomes.

[0518] The following describes the processing flow.

[0519] Step 1:

[0520] The user inputs project details, progress, and problems through the terminal's interface. During the input process, the terminal uses its built-in sensors and camera to acquire emotional data from the user's facial expressions and voice.

[0521] Step 2:

[0522] The device analyzes the acquired emotional data using an emotion engine to recognize the user's emotional state in real time. The recognized emotional state is organized along with project data and transferred to the server.

[0523] Step 3:

[0524] The server receives project data and sentiment data sent from the terminal. This data is recorded in a database, and lessons learned and risks are extracted based on the project's success and failure history.

[0525] Step 4:

[0526] The server's AI model takes received emotional data into account to generate feedback about the project. Based on the user's emotional state, including their motivation and stress levels, it customizes the feedback to suggest appropriate improvements and support.

[0527] Step 5:

[0528] The server sends the generated feedback to the device. The feedback includes specific action plans and recommendations tailored to the user's emotional state.

[0529] Step 6:

[0530] The device displays the received feedback to the user. Based on the feedback provided, the user can concretize ways to improve the project and gain guidance for moving on to the next step.

[0531] Step 7:

[0532] Users carefully consider the feedback and incorporate it into the project's progress. By incorporating feedback based on emotional data, users can maintain their motivation and efficiently advance the project.

[0533] (Example 2)

[0534] 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."

[0535] In project management, providing feedback without considering the user's psychological state can hinder the smooth progress and improved results of a project. Furthermore, a challenge with traditional systems is that feedback is uniform and lacks personalization tailored to the individual user's emotions and needs.

[0536] 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.

[0537] In this invention, the server includes means for receiving and analyzing project information, means for extracting user emotional data using emotion recognition technology, and means for analyzing the emotional data using a generative AI model and creating personalized feedback for the user. This makes it possible to provide flexible and effective feedback that takes into account the user's emotional state.

[0538] "Project information" refers to the project's progress, tasks, goals, and all related data.

[0539] A "storage device" refers to a hardware or software medium for storing digital data.

[0540] "Means of analysis" refers to the process of processing received data using algorithms and programs to extract useful information.

[0541] "Emotion recognition technology" refers to technology that identifies and classifies a user's emotional state from their facial expressions, voice, text, etc.

[0542] "User emotional data" refers to information related to the user's psychological state, extracted using emotion recognition technology.

[0543] A "generative AI model" refers to an artificial intelligence system that uses machine learning algorithms to learn patterns from data and perform predictions and inferences.

[0544] "Feedback" refers to the process of providing users with evaluations and advice regarding the progress of a project.

[0545] "Personalized feedback" refers to specific advice and support tailored to the emotional state and needs of each individual user.

[0546] This system combines advanced emotion recognition technology and AI models to improve the feedback process in project management. An embodiment of this system is shown below.

[0547] The server is responsible for receiving project information and storing it in storage. The server also receives user emotion data extracted by emotion recognition technology. This technology includes algorithms that analyze the user's facial expressions and voice tone to identify emotional states such as joy, anger, and stress. The server analyzes this data using a generative AI model to create personalized feedback tailored to the user. This feedback is designed to support the efficient progress of the project.

[0548] The terminal provides an interface that allows users to easily input information about project progress. In particular, the terminal is equipped with sensors such as a camera and microphone, which are used to recognize the user's emotions in real time. This emotion data is continuously transmitted to the server during project input and used to generate feedback.

[0549] As a concrete example, when a user inputs the progress of a project, the terminal records the user's facial expressions corresponding to this information. For instance, if a user is struggling with a particular issue, the emotion recognition technology can detect subtle signs of stress. Based on this, the server generates feedback suggesting stress management techniques and appropriate ways to take breaks.

[0550] An example of a prompt message is as follows: "Please enter the recent project progress. Please describe the current issue in detail, including its content and progress. Also, please tell us how you felt when facing this issue." Through this specific example, the system generates feedback that takes the user's emotional state into account, enabling more effective project management.

[0551] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0552] Step 1:

[0553] The terminal provides an interface for users to input project-related information. Users enter task progress and related issues into the terminal. The entered information is recorded as text data. This data accurately reflects the user's intent and serves as preparation for sending detailed project management information to the server.

[0554] Step 2:

[0555] When input begins, the device activates its camera and microphone and runs an emotion engine to sense the user's emotions in real time. It detects and analyzes the user's facial expressions and voice tone to generate emotion data. This emotion data includes information about the user's psychological state. This data is temporarily stored on the device and sent to a server for subsequent analysis steps.

[0556] Step 3:

[0557] The server receives project information and sentiment data sent from the terminal. The received data is stored in a database. Next, the server analyzes this data using a generative AI model. The AI ​​model processes the text data of the project information and the sentiment data to generate personalized feedback based on the user's emotional state. The AI ​​model learns the relationships between the data and extracts information that is useful to the user.

[0558] Step 4:

[0559] The server sends the generated feedback to the terminal. This feedback includes solutions to specific challenges the user is facing, as well as support that takes into account their emotional state. The terminal displays this feedback to the user, allowing them to improve or adjust the project's progress based on it. The feedback helps the user understand the project and facilitates smoother project management.

[0560] (Application Example 2)

[0561] 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."

[0562] In project management, the emotions and psychological state of workers can significantly impact project progress and productivity. However, existing systems often fail to consider user emotions, resulting in an inability to provide appropriate feedback and hindering efficient project progress. Furthermore, achieving smooth collaboration between workers and robots is difficult in physical work environments such as factories.

[0563] 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.

[0564] In this invention, the server includes means for acquiring project information, storing it in a memory device, and analyzing the information; means for updating the generated knowledge and searching past cases based on specified conditions; means for generating and providing feedback that brings convenience to the project to the user; and means for providing personalized feedback using an emotion engine that recognizes the user's emotions. This makes it possible to provide feedback that takes into account the user's emotional state in real time, streamlining the progress of the project and facilitating smooth collaboration between workers and robots.

[0565] "Project information" refers to data such as the project's progress, work content, schedule, and issues.

[0566] A "storage device" is a physical or virtual medium used to store and save data, and includes, for example, hard disks and cloud storage.

[0567] "Means of analysis" refer to the processes and methods used to analyze acquired information and extract useful insights.

[0568] "Knowledge" refers to information and guidelines obtained through analysis, which are useful for making appropriate decisions regarding specific situations.

[0569] "Means for exploring past cases" refers to a function for searching for and referencing past examples of similar projects or situations.

[0570] "Convenience-enhancing feedback" refers to constructive and helpful information and recommendations that support users' work and project management.

[0571] "Users" refers to people who use the system, including project managers and workers.

[0572] An "emotion engine" is a technology or algorithm used to recognize and analyze a user's emotional state.

[0573] "Personalized feedback" refers to feedback that is customized to the user's emotional state and circumstances, addressing specific needs and challenges.

[0574] The system that implements this application example includes three main components: a server, a terminal, and a user.

[0575] The server receives project information and stores it in storage. Storage devices such as hard disks or cloud storage are used. Next, to analyze the information, the server uses analysis software to analyze the project data and extract useful insights. Based on this analysis, a function to explore past cases is activated, searching for and referencing similar project examples. Furthermore, an emotion engine is incorporated to recognize and analyze the user's emotional state and generate personalized feedback. This enables personalized support tailored to the user's situation.

[0576] The terminal is an interface for users to input project information and is equipped with sensors and a camera. This hardware is used to detect the user's emotions in real time and transmit that data to the server. The user interface is designed to minimize the effort required to input project progress details.

[0577] Users utilize the system using smart glasses or devices to manage the status of their projects. For example, if a user experiences stress when facing a specific challenge during work, the device's emotion engine analyzes their state, and appropriate feedback is provided from the server. This allows users to solve problems more efficiently and ensure smooth project progress.

[0578] As a concrete example, consider using smart glasses to improve work efficiency among factory workers. When a worker feels anxious, they can receive specific instructions through the smart glasses, such as, "Changing the placement of parts will increase efficiency." This support reduces the psychological burden on workers and improves productivity.

[0579] The following are examples of prompts for a generative AI model:

[0580] "What unique feedback can factory robots provide when workers are feeling stressed?"

[0581] "Please explain the specific methods for using smart glasses to assist with tasks."

[0582] In this way, this system enables efficient project management and optimization of the work environment.

[0583] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0584] Step 1:

[0585] The server receives project information from the terminal. The input includes data regarding the project's progress and issues. The server stores this information in its memory, preparing it for later analysis.

[0586] Step 2:

[0587] The terminal collects project information entered through the user interface. As the user enters data, sensors and cameras on the terminal also activate, and the emotion engine recognizes the user's emotional state in real time, sending it to the server as digital data.

[0588] Step 3:

[0589] The server analyzes the received project information and sentiment data. Analysis software is used to extract characteristics of the project information, and the sentiment data is analyzed by a generating AI model. This analysis searches the database for similar past cases and identifies similar patterns.

[0590] Step 4:

[0591] The server generates feedback best suited to the user's situation based on analysis results and past cases. It utilizes an emotion engine to personalize the feedback based on emotions and build personalized support messages.

[0592] Step 5:

[0593] Users receive feedback through smart glasses or devices. This feedback includes suggestions for improving specific work procedures and increasing efficiency. For example, it may suggest improvements to the layout or changes to procedures at the work site.

[0594] Step 6:

[0595] The terminal or server monitors the effectiveness of the feedback and verifies that users are appropriately utilizing the feedback to improve the project. If no improvement is seen, it adjusts and provides further feedback.

[0596] Inputs and outputs at each step are automatically processed and calculated within the program. Examples of prompts include specific action instructions such as, "What unique feedback can a factory robot provide when a worker is stressed?" or "Please explain specific ways to use smart glasses for work assistance."

[0597] 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.

[0598] 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.

[0599] 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.

[0600] [Fourth Embodiment]

[0601] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0602] 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.

[0603] 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).

[0604] 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.

[0605] 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.

[0606] 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).

[0607] 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.

[0608] 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.

[0609] 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.

[0610] 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.

[0611] 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.

[0612] 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.

[0613] 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".

[0614] This invention provides a knowledge management system for project management, in which servers, terminals, and users interact to improve the success rate of projects. Specific embodiments are shown below.

[0615] Server Functions

[0616] The server has the function of receiving project data and storing it in a database. When a new project is completed, project-related information is entered by the assigned user via their terminal, and the server receives it. The server classifies the received data, identifies success and failure cases, and stores them in the database. The server also utilizes AI models to analyze past project data and extract important lessons and risks. Based on this knowledge, the database is updated, and search results are provided according to the conditions specified by the user.

[0617] Device functions

[0618] The terminal provides an interface for users to input project data. Through this interface, users can input detailed information about project progress, results, and problems, and send it to the server. The terminal also provides an interface to assist users in searching for past project examples. By specifying conditions, the terminal retrieves relevant data from the server in real time and displays it in an easy-to-understand visual format.

[0619] User roles

[0620] Users access this system to support project management. By using a terminal to input new project data, users can contribute to a shared knowledge resource. Furthermore, users can utilize the terminal's search function to investigate similar past cases and apply the lessons learned to improve the success rate of their own projects. For example, during the progress of a new project, they can search for "failures in the market research phase" to identify appropriate approaches and risks to avoid beforehand.

[0621] This allows system users to leverage accumulated knowledge to receive consistent support from project planning to execution, thereby improving the project's success rate.

[0622] The following describes the processing flow.

[0623] Step 1:

[0624] The user inputs project data for their ongoing project through the terminal interface. They accurately enter information such as project details, progress, results, and any problems encountered, and prepare it for transmission to the server.

[0625] Step 2:

[0626] The terminal organizes the project data entered by the user and sends it to the server in the appropriate format. Once the data transmission is complete, the terminal prepares to proceed to the next operation.

[0627] Step 3:

[0628] The server checks the project data received from the terminal and saves it to the database. It then categorizes the data based on whether the project was successful or unsuccessful, and which phase of the project it represents.

[0629] Step 4:

[0630] The server analyzes the stored project data using an AI model to extract lessons learned and risk factors. The information obtained through this process is integrated into the existing knowledge base, and the search index is updated.

[0631] Step 5:

[0632] The user enters a query on their device to search for past project examples. They specify search criteria and define the details of the project for which they want to retrieve relevant knowledge.

[0633] Step 6:

[0634] The terminal sends the user's search query to the server. The server searches the database according to the specified conditions and finds a suitable past project example.

[0635] Step 7:

[0636] The server sends the terminal with success and failure stories of related projects as search results. The server also generates and provides feedback and suggestions for improvement extracted from the relevant projects.

[0637] Step 8:

[0638] The terminal visually displays search results received from the server to the user. The user then uses this information to review data useful for the progress of their project and to inform their decision-making.

[0639] Step 9:

[0640] Based on feedback from the server, users will improve project planning and execution methods. For example, they might consider and apply workarounds for problems faced by similar projects in the past.

[0641] (Example 1)

[0642] 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".

[0643] A challenge in project management is to streamline the accumulation and utilization of knowledge. Traditional methods have made it difficult to systematically manage project successes and failures and learn from them, and knowledge transfer has often been insufficient. As a result, the same mistakes were repeated, making it difficult to improve project success rates.

[0644] 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.

[0645] In this invention, the server includes means for receiving project information and storing it in a data structure, means for updating the generated knowledge and searching past cases based on specified conditions, and means for extracting important lessons and risks based on the analyzed results and providing them to the user. This makes it possible to effectively accumulate knowledge about the project and utilize it in future projects.

[0646] "Project information" refers to data related to the progress of a project, including the start date, planned end date, progress status, results, and problems.

[0647] A "data structure" refers to a framework for organizing and storing information, and includes databases and other record formats.

[0648] "Analysis" is the process of examining received data in detail to identify trends and patterns.

[0649] "Knowledge" refers to information that can be used in future projects, such as lessons learned, strategies, and risk information gained from the progress of a project.

[0650] A "generative AI model" is a machine learning model that performs pattern recognition and prediction based on a large amount of historical data.

[0651] "User interface" refers to the screen on which a user operates when entering or searching for data on a device, and includes the display of visual information.

[0652] "Success stories and failure stories" refer to information recorded as a result of effective strategies or incorrect decisions made in past projects.

[0653] "Feedback" refers to advice and information generated based on past data analysis results, which serves as a guideline for improving the project.

[0654] "Lessons learned" refer to the knowledge and strategies that should be adopted in new ways, based on the results of past projects.

[0655] This invention provides a knowledge management system for project management. Servers, terminals, and users collaborate to support project success.

[0656] Server Functions

[0657] The server receives project information and stores it in a data structure. The server uses a generative AI model to analyze past project information and classify successes and failures. It extracts important lessons and risks from the analysis results and updates the database. In particular, the generative AI model is used to recognize patterns from large amounts of data and gain new insights. For example, it can analyze "failures in the market research phase" to identify risks that should be avoided.

[0658] Device functions

[0659] The terminal provides a means for users to input project information through a user interface. Users use the terminal interface to input project progress, results, and problems. The terminal sends this information to the server. The terminal also functions as an interface for searching for project examples. When a user specifies criteria, the terminal retrieves relevant data from the server and displays it visually.

[0660] User roles

[0661] Users access the system to provide project data and contribute to shared knowledge resources. For example, when searching for similar past cases during a new project, users can enter the prompt "Tell me about past successful market research cases" and adjust the project's progress based on the information obtained. Based on this prompt, the server searches for relevant information and provides it to the user through the terminal.

[0662] This system allows users to effectively utilize accumulated knowledge and receive consistent support from project planning to execution, thereby improving the success rate of projects.

[0663] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0664] Step 1:

[0665] The user enters project information using the terminal's interface. This information includes the project name, progress, results, and issues. This data is then formatted and sent to the server by the terminal.

[0666] Step 2:

[0667] The server retrieves project information received from the terminal and saves it to the database. Here, the server validates the data to ensure its format is correct. If there are any errors, it generates an error message and sends it back to the terminal.

[0668] Step 3:

[0669] The server analyzes the stored information and compares it to similar project data using a generative AI model. In this analysis process, the server classifies past successes and failures and extracts new lessons and risks. Specifically, the model uses pattern matching to identify key points.

[0670] Step 4:

[0671] Based on the analysis results, the server updates the database. New lessons are added, and existing knowledge is reinforced. The server uses the updated information to prepare to respond to future search requests from users.

[0672] Step 5:

[0673] The user uses the terminal interface to search for past cases based on specific criteria. When the user enters the prompt "Tell me about past successful market research cases," the terminal sends a request to the server.

[0674] Step 6:

[0675] The server searches the database in response to the prompt and identifies the appropriate case. From the identified case, it extracts relevant lessons and success strategies and sends them to the terminal.

[0676] Step 7:

[0677] The terminal visually displays information received from the server to the user. The information is displayed as graphs and charts, processed into a format that is easily interpretable by the user.

[0678] Step 8:

[0679] Users utilize the presented information to consider ways to improve the project. They derive necessary strategies from the obtained data and formulate concrete plans to improve the project's success rate.

[0680] (Application Example 1)

[0681] 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".

[0682] To achieve efficiency improvements and early detection of anomalies in the manufacturing process, it is crucial to collect and analyze large amounts of work data in real time. However, conventional methods involve cumbersome data management and make it difficult to efficiently utilize past case studies. Furthermore, there is a problem in that work optimization and anomaly prevention are not functioning adequately.

[0683] 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.

[0684] In this invention, the server includes means for receiving work data, storing it in an aggregation device, and analyzing the data; means for updating the generated knowledge and searching for past examples based on specified conditions; means for generating and providing useful return routes for the work to the user; and means for providing return routes to the device for optimizing the manufacturing process. This enables increased efficiency of the manufacturing line and early detection of anomalies.

[0685] "Work data" refers to information regarding the implementation status of each process and task in the manufacturing process, and includes numerical values ​​and indicators such as work time, error rate, and material consumption.

[0686] "Data aggregation device" refers to equipment or systems for centrally storing and managing received data, and includes databases and cloud storage.

[0687] "Knowledge" refers to useful information and lessons learned from past data analysis, as well as advice based on success and failure stories.

[0688] A "model" refers to a case where performance or results were good under specific conditions, based on past case data, and serves as a standard for reference.

[0689] "Return route" refers to the path for improvement measures and feedback provided based on information and analysis results obtained during manufacturing and operations.

[0690] "Equipment" refers to all hardware used for manufacturing, including the machines and equipment themselves that are placed on the production line.

[0691] The server collects data from the manufacturing process and stores it in an aggregation device. This makes it possible to unify the management of important indicators such as work time, error rate, and material consumption at each stage of the process. Databases and cloud storage function as the aggregation device. The server further analyzes the aggregated data using AI models and extracts information for anomaly detection and optimization. Specifically, it uses programming languages ​​such as Python and data analysis libraries such as Pandas and Scikit-learn.

[0692] Users can access the server via a terminal and receive feedback for optimizing the manufacturing process and detecting anomalies. The terminal provides an easy-to-use interface, allowing users to search for past examples and develop improvement measures based on the knowledge gained.

[0693] For example, in a parts assembly process on a manufacturing line, the system searches for past instances of quality problems and uses that information to improve specific processes. Examples of prompts include, "Use past manufacturing project data to suggest improvements for the next manufacturing process," and "Utilize the AI ​​model to optimize the anomaly detection algorithm during manufacturing." This enables real-time improvement of the manufacturing line through the collaboration of the server and terminals.

[0694] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0695] Step 1:

[0696] The server collects real-time work data from the manufacturing line and stores it in a data aggregation device. Inputs include process information obtained from sensors and machine operation data. This data is not only stored as is, but is also appropriately classified and structured in a database to improve accessibility.

[0697] Step 2:

[0698] The server uses data stored in the integrated device to perform analysis that utilizes AI models to detect anomalies and inefficiencies. It uses stored historical manufacturing data and current real-time data as input. Using the predictive algorithms provided by the AI ​​model, it identifies unique patterns, extracts risk factors and inefficient parts of the process, and generates data alerts and optimization suggestions as output.

[0699] Step 3:

[0700] Users access the server via their terminal and receive feedback and analysis results from the server. Through the user interface, they can search for past examples based on specific conditions and utilize that knowledge. The database is searched based on user-specified conditions and keywords, and the output includes past examples and visualized information representing the acquired knowledge.

[0701] Step 4:

[0702] Based on feedback from the server, users implement specific improvements to the manufacturing process. Specifically, they adjust on-site operations and modify equipment settings based on the causes of anomalies and points for efficiency improvements indicated in the feedback. This results in the optimization of the manufacturing process, leading to improved product quality and increased productivity.

[0703] 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.

[0704] This invention is a system that incorporates an emotion engine to recognize user emotions, with the aim of providing effective feedback and improvement measures in project management. Specific embodiments are shown below.

[0705] Server Functions

[0706] The server has the basic function of receiving project data and storing it in a database. Furthermore, this system uses an emotion engine to acquire user emotion data. This emotion data includes emotional states extracted through facial recognition and text analysis when the user takes certain actions. The server analyzes this emotion data and uses it for project feedback. An AI model generates feedback by combining this emotion data, providing users with more personalized support.

[0707] Device functions

[0708] The terminal provides an interface for users to input project progress information. It also features an emotion engine and is equipped with sensors and cameras to recognize the user's emotions in real time. While the user inputs project information, the terminal detects changes in the user's emotions via the emotion engine and transmits that data to the server.

[0709] User roles

[0710] Users input project progress and challenges using their terminals as usual. During this process, the emotion engine works to analyze the user's emotional state. For example, if signs of stress are detected when the user is facing a particular problem, the system can provide appropriate feedback. The feedback provided will include content tailored to the user's motivation and stress level, and will suggest specific steps and support to help solve the problem.

[0711] In this way, a system equipped with an emotional engine can provide support that takes into account the psychological state of project participants, thereby facilitating smoother project progress and improving outcomes.

[0712] The following describes the processing flow.

[0713] Step 1:

[0714] The user inputs project details, progress, and problems through the terminal's interface. During the input process, the terminal uses its built-in sensors and camera to acquire emotional data from the user's facial expressions and voice.

[0715] Step 2:

[0716] The device analyzes the acquired emotional data using an emotion engine to recognize the user's emotional state in real time. The recognized emotional state is organized along with project data and transferred to the server.

[0717] Step 3:

[0718] The server receives project data and sentiment data sent from the terminal. This data is recorded in a database, and lessons learned and risks are extracted based on the project's success and failure history.

[0719] Step 4:

[0720] The server's AI model takes received emotional data into account to generate feedback about the project. Based on the user's emotional state, including their motivation and stress levels, it customizes the feedback to suggest appropriate improvements and support.

[0721] Step 5:

[0722] The server sends the generated feedback to the device. The feedback includes specific action plans and recommendations tailored to the user's emotional state.

[0723] Step 6:

[0724] The device displays the received feedback to the user. Based on the feedback provided, the user can concretize ways to improve the project and gain guidance for moving on to the next step.

[0725] Step 7:

[0726] Users carefully consider the feedback and incorporate it into the project's progress. By incorporating feedback based on emotional data, users can maintain their motivation and efficiently advance the project.

[0727] (Example 2)

[0728] 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".

[0729] In project management, providing feedback without considering the user's psychological state can hinder the smooth progress and improved results of a project. Furthermore, a challenge with traditional systems is that feedback is uniform and lacks personalization tailored to the individual user's emotions and needs.

[0730] 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.

[0731] In this invention, the server includes means for receiving and analyzing project information, means for extracting user emotional data using emotion recognition technology, and means for analyzing the emotional data using a generative AI model and creating personalized feedback for the user. This makes it possible to provide flexible and effective feedback that takes into account the user's emotional state.

[0732] "Project information" refers to the project's progress, tasks, goals, and all related data.

[0733] A "storage device" refers to a hardware or software medium for storing digital data.

[0734] "Means of analysis" refers to the process of processing received data using algorithms and programs to extract useful information.

[0735] "Emotion recognition technology" refers to technology that identifies and classifies a user's emotional state from their facial expressions, voice, text, etc.

[0736] "User emotional data" refers to information related to the user's psychological state, extracted using emotion recognition technology.

[0737] A "generative AI model" refers to an artificial intelligence system that uses machine learning algorithms to learn patterns from data and perform predictions and inferences.

[0738] "Feedback" refers to the process of providing users with evaluations and advice regarding the progress of a project.

[0739] "Personalized feedback" refers to specific advice and support tailored to the emotional state and needs of each individual user.

[0740] This system combines advanced emotion recognition technology and AI models to improve the feedback process in project management. An embodiment of this system is shown below.

[0741] The server is responsible for receiving project information and storing it in storage. The server also receives user emotion data extracted by emotion recognition technology. This technology includes algorithms that analyze the user's facial expressions and voice tone to identify emotional states such as joy, anger, and stress. The server analyzes this data using a generative AI model to create personalized feedback tailored to the user. This feedback is designed to support the efficient progress of the project.

[0742] The terminal provides an interface that allows users to easily input information about project progress. In particular, the terminal is equipped with sensors such as a camera and microphone, which are used to recognize the user's emotions in real time. This emotion data is continuously transmitted to the server during project input and used to generate feedback.

[0743] As a concrete example, when a user inputs the progress of a project, the terminal records the user's facial expressions corresponding to this information. For instance, if a user is struggling with a particular issue, the emotion recognition technology can detect subtle signs of stress. Based on this, the server generates feedback suggesting stress management techniques and appropriate ways to take breaks.

[0744] An example of a prompt message is as follows: "Please enter the recent project progress. Please describe the current issue in detail, including its content and progress. Also, please tell us how you felt when facing this issue." Through this specific example, the system generates feedback that takes the user's emotional state into account, enabling more effective project management.

[0745] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0746] Step 1:

[0747] The terminal provides an interface for users to input project-related information. Users enter task progress and related issues into the terminal. The entered information is recorded as text data. This data accurately reflects the user's intent and serves as preparation for sending detailed project management information to the server.

[0748] Step 2:

[0749] When input begins, the device activates its camera and microphone and runs an emotion engine to sense the user's emotions in real time. It detects and analyzes the user's facial expressions and voice tone to generate emotion data. This emotion data includes information about the user's psychological state. This data is temporarily stored on the device and sent to a server for subsequent analysis steps.

[0750] Step 3:

[0751] The server receives project information and sentiment data sent from the terminal. The received data is stored in a database. Next, the server analyzes this data using a generative AI model. The AI ​​model processes the text data of the project information and the sentiment data to generate personalized feedback based on the user's emotional state. The AI ​​model learns the relationships between the data and extracts information that is useful to the user.

[0752] Step 4:

[0753] The server sends the generated feedback to the terminal. This feedback includes solutions to specific challenges the user is facing, as well as support that takes into account their emotional state. The terminal displays this feedback to the user, allowing them to improve or adjust the project's progress based on it. The feedback helps the user understand the project and facilitates smoother project management.

[0754] (Application Example 2)

[0755] 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".

[0756] In project management, the emotions and psychological state of workers can significantly impact project progress and productivity. However, existing systems often fail to consider user emotions, resulting in an inability to provide appropriate feedback and hindering efficient project progress. Furthermore, achieving smooth collaboration between workers and robots is difficult in physical work environments such as factories.

[0757] 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.

[0758] In this invention, the server includes means for acquiring project information, storing it in a memory device, and analyzing the information; means for updating the generated knowledge and searching past cases based on specified conditions; means for generating and providing feedback that brings convenience to the project to the user; and means for providing personalized feedback using an emotion engine that recognizes the user's emotions. This makes it possible to provide feedback that takes into account the user's emotional state in real time, streamlining the progress of the project and facilitating smooth collaboration between workers and robots.

[0759] "Project information" refers to data such as the project's progress, work content, schedule, and issues.

[0760] A "storage device" is a physical or virtual medium used to store and save data, and includes, for example, hard disks and cloud storage.

[0761] "Means of analysis" refer to the processes and methods used to analyze acquired information and extract useful insights.

[0762] "Knowledge" refers to information and guidelines obtained through analysis, which are useful for making appropriate decisions regarding specific situations.

[0763] "Means for exploring past cases" refers to a function for searching for and referencing past examples of similar projects or situations.

[0764] "Convenience-enhancing feedback" refers to constructive and helpful information and recommendations that support users' work and project management.

[0765] "Users" refers to people who use the system, including project managers and workers.

[0766] An "emotion engine" is a technology or algorithm used to recognize and analyze a user's emotional state.

[0767] "Personalized feedback" refers to feedback that is customized to the user's emotional state and circumstances, addressing specific needs and challenges.

[0768] The system that implements this application example includes three main components: a server, a terminal, and a user.

[0769] The server receives project information and stores it in storage. Storage devices such as hard disks or cloud storage are used. Next, to analyze the information, the server uses analysis software to analyze the project data and extract useful insights. Based on this analysis, a function to explore past cases is activated, searching for and referencing similar project examples. Furthermore, an emotion engine is incorporated to recognize and analyze the user's emotional state and generate personalized feedback. This enables personalized support tailored to the user's situation.

[0770] The terminal is an interface for users to input project information and is equipped with sensors and a camera. This hardware is used to detect the user's emotions in real time and transmit that data to the server. The user interface is designed to minimize the effort required to input project progress details.

[0771] Users utilize the system using smart glasses or devices to manage the status of their projects. For example, if a user experiences stress when facing a specific challenge during work, the device's emotion engine analyzes their state, and appropriate feedback is provided from the server. This allows users to solve problems more efficiently and ensure smooth project progress.

[0772] As a concrete example, consider using smart glasses to improve work efficiency among factory workers. When a worker feels anxious, they can receive specific instructions through the smart glasses, such as, "Changing the placement of parts will increase efficiency." This support reduces the psychological burden on workers and improves productivity.

[0773] The following are examples of prompts for a generative AI model:

[0774] "What unique feedback can factory robots provide when workers are feeling stressed?"

[0775] "Please explain the specific methods for using smart glasses to assist with tasks."

[0776] In this way, this system enables efficient project management and optimization of the work environment.

[0777] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0778] Step 1:

[0779] The server receives project information from the terminal. The input includes data regarding the project's progress and issues. The server stores this information in its memory, preparing it for later analysis.

[0780] Step 2:

[0781] The terminal collects project information entered through the user interface. As the user enters data, sensors and cameras on the terminal also activate, and the emotion engine recognizes the user's emotional state in real time, sending it to the server as digital data.

[0782] Step 3:

[0783] The server analyzes the received project information and sentiment data. Analysis software is used to extract characteristics of the project information, and the sentiment data is analyzed by a generating AI model. This analysis searches the database for similar past cases and identifies similar patterns.

[0784] Step 4:

[0785] The server generates feedback best suited to the user's situation based on analysis results and past cases. It utilizes an emotion engine to personalize the feedback based on emotions and build personalized support messages.

[0786] Step 5:

[0787] Users receive feedback through smart glasses or devices. This feedback includes suggestions for improving specific work procedures and increasing efficiency. For example, it may suggest improvements to the layout or changes to procedures at the work site.

[0788] Step 6:

[0789] The terminal or server monitors the effectiveness of the feedback and verifies that users are appropriately utilizing the feedback to improve the project. If no improvement is seen, it adjusts and provides further feedback.

[0790] Inputs and outputs at each step are automatically processed and calculated within the program. Examples of prompts include specific action instructions such as, "What unique feedback can a factory robot provide when a worker is stressed?" or "Please explain specific ways to use smart glasses for work assistance."

[0791] 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.

[0792] 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.

[0793] 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.

[0794] 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.

[0795] 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.

[0796] 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.

[0797] 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.

[0798] 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.

[0799] 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."

[0800] 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.

[0801] 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.

[0802] 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.

[0803] 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.

[0804] 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.

[0805] 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.

[0806] 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.

[0807] 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.

[0808] 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.

[0809] 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.

[0810] 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.

[0811] 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.

[0812] The following is further disclosed regarding the embodiments described above.

[0813] (Claim 1)

[0814] A means of receiving project data, storing it in a database, and analyzing that data,

[0815] A means to update the generated knowledge and search for past cases based on specified conditions,

[0816] A means of generating and providing useful feedback to users for the project,

[0817] A system that includes this.

[0818] (Claim 2)

[0819] The system according to claim 1, wherein project data is input to a terminal via a user interface.

[0820] (Claim 3)

[0821] The system according to claim 1, which considers project improvement measures based on the generated feedback.

[0822] "Example 1"

[0823] (Claim 1)

[0824] A means for receiving project information, storing it in a data structure, and analyzing the said information,

[0825] A means of updating generated knowledge and exploring past cases based on specified conditions,

[0826] A means of extracting important lessons and risks based on the analysis results and providing them to users,

[0827] A means of visually displaying information through a user interface,

[0828] A means of classifying success stories and failure stories from past projects,

[0829] A means of further analyzing information using generative AI models,

[0830] A system that includes this.

[0831] (Claim 2)

[0832] The system according to claim 1, which inputs project information into a terminal via a user interface and utilizes a generated AI model.

[0833] (Claim 3)

[0834] The system according to claim 1, which considers project improvement measures based on the generated feedback and extracted lessons learned.

[0835] "Application Example 1"

[0836] (Claim 1)

[0837] A means for receiving work data, storing it in a data collection device, and analyzing the data,

[0838] A means for updating generated knowledge and searching for past examples based on specified conditions,

[0839] A means of generating and providing a return route useful for the task to the user,

[0840] A means of providing the apparatus with a return path for optimizing the manufacturing process,

[0841] A system that includes this.

[0842] (Claim 2)

[0843] The system according to claim 1, wherein work data is input to a terminal via a user input device.

[0844] (Claim 3)

[0845] The system according to claim 1, which considers measures to improve the work based on the generated return route.

[0846] "Example 2 of combining an emotion engine"

[0847] (Claim 1)

[0848] A means for receiving project information, storing it in a storage device, and analyzing the said information,

[0849] A means of extracting user emotional data using emotion recognition technology,

[0850] A means of analyzing extracted emotional data using a generation AI model to create and provide personalized feedback to users,

[0851] A means to update the generated knowledge and search for past cases based on specified conditions,

[0852] A system that includes this.

[0853] (Claim 2)

[0854] The system according to claim 1, which inputs project information into a terminal via a user interface and detects emotional changes in real time.

[0855] (Claim 3)

[0856] The system according to claim 1, which considers project improvement measures based on the generated feedback and proposes improvement measures while taking into account the emotional state of the users.

[0857] "Application example 2 of combining emotional engines"

[0858] (Claim 1)

[0859] A means for acquiring project information, storing it in a memory device, and analyzing the information,

[0860] A means of updating generated knowledge and exploring past cases based on specified conditions,

[0861] A means of generating and providing feedback that brings convenience to the project to users,

[0862] A means of providing personalized feedback using an emotion engine that recognizes the user's emotions,

[0863] A system that includes this.

[0864] (Claim 2)

[0865] The system according to claim 1, in which project information is entered via a user terminal.

[0866] (Claim 3)

[0867] The system according to claim 1, which formulates project improvement measures based on the generated feedback. [Explanation of symbols]

[0868] 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 receiving project data, storing it in a database, and analyzing that data, A means to update the generated knowledge and search for past cases based on specified conditions, A means of generating and providing useful feedback to users for the project, A system that includes this.

2. The system according to claim 1, wherein project data is input to a terminal via a user interface.

3. The system according to claim 1, which considers project improvement measures based on the generated feedback.

Images