system

Abstract

We provide the system. [Solution] A means of collecting combat data in real time, A means for preprocessing the collected data and converting it into an analyzable format, An AI-powered analytical method for analyzing pre-processed data and identifying factors for success and failure, A means to automatically generate next tactical improvement plans based on the analysis results, A means of visualizing the generated improvement proposals and providing them to the user, 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 persona chatbot control method 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 chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] The reflection work that occurs after combat or combat operations often takes a long time and is based on subjective judgment, so there is a problem that it is difficult to formulate the next tactic. In particular, it is difficult to effectively extract the factors of failure or success, and there is a problem that the success rate of the next operation cannot be sufficiently increased.

Means for Solving the Problems

[0005] This invention provides a system that collects combat data in real time, converts the obtained data into an analyzable format through preprocessing, and identifies the factors for success and failure using artificial intelligence-based analysis. Furthermore, based on the analysis results, it automatically generates and visualizes proposed tactical improvements for the next mission, presenting them to the user, thereby enabling rapid and objective debriefing. In addition, by incorporating user feedback into the learning process, continuous improvement and optimization of tactics can be achieved.

[0006] "Combat data" refers to data collected from information such as location, communication, and action logs in battlefields and training scenarios.

[0007] "Real-time" refers to processing or information transmission occurring instantly without delay.

[0008] "Preprocessing" refers to the process of converting raw data into an analyzable format, and includes tasks such as deduplication, noise reduction, and format conversion.

[0009] Artificial intelligence refers to computer programs and systems that mimic human intellectual tasks and possess the ability to perform data analysis and pattern recognition.

[0010] "Analysis methods" refer to methods and devices used to extract specific patterns or factors from collected data.

[0011] "Visualization" refers to representing data visually in the form of graphs, charts, and other visual media to make it easier to understand intuitively.

[0012] A "user" refers to an entity that utilizes a system, receives information, and makes decisions and provides feedback.

[0013] "Feedback" refers to the process of incorporating information such as user opinions and improvement requests into the system and using it to further improve it. [Brief explanation of the drawing]

[0014] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [[ID=2)5]] [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Modes for Carrying Out the Invention

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

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

[0017] In the following embodiments, the numbered 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.

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

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

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] 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."

[0022] [First Embodiment]

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

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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.

[0029] 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.

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

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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".

[0035] This invention provides a system for efficiently and objectively analyzing post-combat data and providing suggestions for future tactical improvements. This system encompasses a series of processes, from data collection, processing, and analysis during and after combat operations to the presentation of improvement suggestions.

[0036] First, the server collects vast amounts of data generated in the battlefield and training environment in real time. By aggregating and securely storing data obtained from various devices, such as location information, communication logs, and weapon usage information, data loss and loss of accuracy are prevented.

[0037] Next, the data collected by the server is preprocessed and converted into a format suitable for advanced analysis. Specifically, data cleansing is performed to remove duplicate data and noise. In addition, data obtained from different devices is converted into a unified format, and the time and location of the data are synchronized.

[0038] Next, the server uses artificial intelligence to analyze the data. During this process, it utilizes machine learning algorithms, referencing past battle data, to identify the factors behind success and failure. This AI analysis determines which tactics are effective and which require improvement.

[0039] Subsequently, the server generates tactical improvement proposals for the next phase based on the analysis results. The generating AI designs specific tactical changes based on the insights gained and presents them in a visualized format. For example, it may suggest the tactical effectiveness of waiting at a specific location or methods for improving communication.

[0040] Next, the device presents the generated improvement suggestions to the user. The suggestions are visually organized and displayed as graphical charts and heatmaps, making them easy for the user to understand intuitively and aiding in quick decision-making.

[0041] Finally, based on the improvement suggestions provided by the user, the system develops actual tactical plans. By providing feedback, the system incorporates that information as learning data for further improvement. This allows the system to continuously learn and optimize its tactics.

[0042] For example, when a unit carries out a specific operation in a training scenario, this system can be used to immediately analyze the results and clearly identify areas for improvement for the next training session. As a result, the unit's response capabilities are strengthened, and more efficient training can be achieved.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] The server collects data in real time from sensors and devices during combat and automatically stores it in a central database. This data includes location information, communication logs, and time-series information on actions, and the collection process is continuously monitored to prevent data loss.

[0046] Step 2:

[0047] The server preprocesses the collected raw data and converts it into an analyzable format. Specifically, it cleanses the data to remove duplicates and noise. It also unifies data in different formats and synchronizes data between devices.

[0048] Step 3:

[0049] The server feeds pre-processed data into an artificial intelligence model and begins the analysis. The AI ​​uses machine learning algorithms to identify success and failure factors while comparing them with historical data. This analysis process identifies the effectiveness of specific tactical patterns and actions.

[0050] Step 4:

[0051] The server uses AI generation based on the analysis results to automatically generate tactical improvement proposals for the next phase. The generated improvement proposals are converted into a visualized format and include, for example, recommended points for tactical changes and suggestions for optimizing positioning.

[0052] Step 5:

[0053] The device presents users with visually organized improvement suggestions in real time. These suggestions are displayed as graphical charts and heatmaps, making it easy for users to intuitively understand the analysis results.

[0054] Step 6:

[0055] The user evaluates the suggested improvements and inputs feedback into the terminal to incorporate them into the next tactical plan. This feedback is then sent to the server to further improve the tactics.

[0056] Step 7:

[0057] The server receives user feedback and incorporates it into the AI ​​model as training data. This helps improve the accuracy of the analysis and generate suggestions for improvement for the next analysis. The AI ​​incorporates the feedback and derives new insights in the next data analysis.

[0058] (Example 1)

[0059] 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."

[0060] An efficient system is needed to objectively and quickly evaluate the effectiveness of tactical actions in the field and use that information to improve future actions. Traditional methods rely heavily on manual data collection and analysis, which is time-consuming and labor-intensive, and can be influenced by subjective judgments. As a result, tactical improvements are often insufficient, and rapid decision-making is difficult.

[0061] 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.

[0062] In this invention, the server includes means for aggregating information in real time at the field, means for organizing the aggregated information and converting it into an analyzable data format, and analytical means utilizing machine learning to analyze the organized data and identify specific factors. This makes it possible to efficiently process and analyze the vast amount of data obtained from the field and to scientifically and objectively improve the next tactical plan.

[0063] "Methods for collecting information in real time at the site" refers to technical methods for collecting and centrally managing data transmitted from the field.

[0064] "Means for organizing aggregated information and converting it into an analyzable data format" refers to methods for appropriately editing collected raw data, converting it into a unified format, and facilitating subsequent analysis.

[0065] "Analysis methods utilizing machine learning" are techniques that use algorithms to find patterns and causal relationships based on past data, and to make predictions and judgments.

[0066] "A means of automatically generating improvement suggestions for the next operation" refers to a system that utilizes analysis results to mechanically generate specific suggestions for efficiently improving the next action plan.

[0067] "Visual presentation methods that provide information to users" refer to technologies for displaying generated information or proposals in a way that is easy for humans to understand, such as using graphs and charts to convey information intuitively.

[0068] This invention is an information processing system aimed at improving tactical actions, and is composed primarily of a server, terminals, and users.

[0069] The server aggregates a wide variety of data collected on-site in real time. This process includes the immediate collection of data such as location information, communication logs, and activity records. The server securely stores this data in a database, which serves as the foundation for subsequent processing.

[0070] After data aggregation, the server organizes this information into a parseable format. For hardware, a server with a high-spec processor and sufficient storage capacity is recommended. Data formats such as CSV and JSON are used for standardization. A cleansing process removes noise and duplicate data, converting the data into a consistent format.

[0071] Next, the server performs machine learning analysis on the organized data. This analysis utilizes deep learning frameworks such as TENSORFLOW® and PyTorch, with generative AI models identifying success factors and areas for improvement. The machine learning algorithms learn from past data and recognize similar patterns, enabling highly accurate analysis.

[0072] The server then automatically generates suggestions for improving the next operation based on the analysis results. These suggestions are visually displayed on the dashboard, making it easy to review tactical improvements. These suggestions may include tactical measures such as changes to troop deployment or improvements to communication protocols.

[0073] Once the proposal is complete, the device visualizes and presents it to the user. Using graphs, heatmaps, and other visual aids, the device provides intuitive understanding and helps users make quick decisions.

[0074] Finally, the user develops a plan of action for the next step based on the suggestions provided. The user's feedback is incorporated into the system and used as a reference for future data analysis. This allows the system to provide more appropriate suggestions over time.

[0075] As a concrete example, in a training scenario, evaluating a specific operation and immediately identifying problems allows for appropriate improvements to be made in the next training session. An example of a prompt message is, "Analyze the results of the operation in the training scenario and propose tactical improvements for the next session."

[0076] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0077] Step 1:

[0078] The server receives raw data transmitted from the field. This data includes location information, communication logs, and activity records, collected from various sensors. The server stores this information in a database. The input here is raw data from the field, and the output is unprocessed data before it is organized.

[0079] Step 2:

[0080] The server performs data cleansing on the collected raw data. This process removes noise and duplicate entries from the data and selects valid data. The input is raw data stored in the database, and the output is clean data with noise removed and a unified format.

[0081] Step 3:

[0082] The server converts clean data into an analyzable format. It performs format conversion, integrates data from different devices, and adjusts the time series. CSV and JSON formats are used here. The input is clean data, and the output is in a data format suitable for analysis.

[0083] Step 4:

[0084] The server applies machine learning models to data suitable for analysis. Using deep learning frameworks such as TensorFlow, it analyzes the factors of success and failure while referencing historical data. The input is data in a format suitable for analysis, and the output is the analysis result (identification of success and failure factors).

[0085] Step 5:

[0086] The server automatically generates improvement plans for the next operation based on the analysis results obtained. It uses a generating AI model to design specific suggestions and displays them in a dashboard format. The input is the analysis results, and the output is the generated improvement suggestions.

[0087] Step 6:

[0088] The terminal visualizes and presents the generated improvement suggestions to the user. Using a GUI, the suggestions are visually represented with graphs and heatmaps, making them easy for the user to understand intuitively. The input is the generated improvement suggestions, and the output is the visualized suggestion information.

[0089] Step 7:

[0090] Based on the suggested improvements, users form their next action plan. Users provide feedback to the system, which is then used for future data analysis. The input here is visualized suggestion information, and the output is feedback information.

[0091] (Application Example 1)

[0092] 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."

[0093] Improving operational efficiency in logistics centers is a critical challenge. Traditional methods require significant time and effort to understand operational status and generate improvement plans, making immediate optimization difficult. To address this, real-time data analysis and immediate proposal of improvements are essential.

[0094] 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.

[0095] In this invention, the server includes means for collecting operational data in real time, means for preprocessing the collected data and converting it into an analyzable format, and means for analyzing the preprocessed data and identifying the factors for success and failure of operations using artificial intelligence. This makes it possible to analyze operational data in a logistics center in real time and immediately generate and present improvement proposals.

[0096] "Business data" refers to all information related to operations that occur within the logistics center, and this includes location information, communication information, operation logs, etc.

[0097] "Means of real-time data collection" refers to technologies and devices for instantly recording data generated during a task.

[0098] "Means of preprocessing and converting into an analyzable format" refers to techniques for shaping raw data into an appropriate format and processing it to a state suitable for analysis.

[0099] "Analysis methods using artificial intelligence" refer to methods that employ machine learning algorithms to learn from past data and evaluate current data based on the insights gained from that learning.

[0100] "Means of presenting improvement proposals to users in a visual form, such as diagrams and graphs, are technologies and devices that display analysis results in a visual form, allowing users to understand them intuitively."

[0101] The system that realizes this invention collects and analyzes operational data in real time at a logistics center and immediately provides improvement suggestions.

[0102] The server collects operational data generated from various devices within the logistics center in real time. This includes location information, communication information, and operation logs. The server then preprocesses the collected data and converts it into a format suitable for analysis. This preprocessing includes removing duplicate data and noise.

[0103] Next, the server uses artificial intelligence to analyze the data. This includes analytical methods that use machine learning algorithms that learn from past data to identify factors influencing the success and failure of operations from current data. Specifically, it uses tools such as Python and scikit-learn to perform dimensionality reduction using PCA and KMeans clustering.

[0104] The improvement suggestions generated by the server are visualized and provided to the terminal. The terminal uses visualization libraries such as Matplotlib to present the improvement suggestions to the user as graphs and heatmaps. This allows the user to gain visual insights into business processes and make immediate decisions.

[0105] As a concrete example, one day at a logistics center, it was discovered that handling a particular product was taking an excessive amount of time. The server then generated improvement suggestions, including a reassessment of the product's layout, and presented them to the user via a terminal. As a result, the work time was reduced by changing the product's placement.

[0106] An example of a prompt message is, "Generate process improvement proposals based on daily work data from the logistics center." In this way, the efficiency of logistics operations can be improved.

[0107] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0108] Step 1:

[0109] The server collects operational data in real time from various devices within the logistics center. Inputs include location information and operation logs transmitted from the devices, while outputs are collections of raw data. Data is sent directly to the server from sensors and terminals via wireless or wired communication.

[0110] Step 2:

[0111] The server preprocesses the collected raw data and converts it into a format suitable for analysis. The input is the raw data obtained in step 1, and the output is the cleaned data. Specifically, the data is shaped by removing duplicates, removing noise, and imputing missing values. This improves the accuracy of the analysis.

[0112] Step 3:

[0113] The server analyzes pre-processed data using artificial intelligence. The input is the cleaned data generated in step 2, and the output is the key factors and their evaluation as a result of the analysis. By applying machine learning algorithms, patterns in the data are identified and factors that differentiate success from failure are extracted.

[0114] Step 4:

[0115] The server generates a plan for future business improvements based on the analysis results obtained. The input is the analysis results from step 3, and the output is a list of improvement plans. Using the generation AI model, specific process changes to improve work efficiency are automatically designed.

[0116] Step 5:

[0117] The terminal visualizes the improvement suggestions sent from the server and presents them to the user. The input is the improvement suggestions created in step 4, and the output is visualized information in a format that is easy for the user to understand visually (e.g., graphs, heatmaps). The terminal utilizes visualization libraries such as Matplotlib to convert the information into a visually understandable format.

[0118] Step 6:

[0119] Users evaluate the proposed improvements and provide feedback. Input is visualization information presented from the device, and output is feedback information. This feedback is returned to the server as data useful for subsequent analysis and improvement cycles.

[0120] 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.

[0121] This invention is a system that collects combat and training data in real time, provides suggestions for improving tactics for the next session based on the collected data, and further achieves more effective tactical feedback by combining it with an emotion engine that recognizes the user's emotions.

[0122] First, the server automatically collects combat data such as location information, communication information, and action logs in real time from battlefields and training environments using multiple sensors. This data is securely stored and organized in a format that allows for appropriate subsequent data processing.

[0123] Next, the data collected by the server is preprocessed and converted into a format suitable for analysis by artificial intelligence. This preprocessing includes data cleansing (removal of duplicates and noise reduction) and format conversion, as well as the integration of time and location information.

[0124] Subsequently, the server analyzes the pre-processed data using an artificial intelligence model. The AI ​​uses machine learning algorithms to identify success and failure factors, referencing past data, and identifies tactical patterns and the effectiveness of actions.

[0125] Based on the analysis results, the server uses AI to automatically generate tactical improvement proposals for the next battle. These proposals are graphically represented using visualization tools and prepared for presentation to the user. For example, suggestions for changing waiting locations or improving communication protocols may be presented.

[0126] In this invention, the terminal utilizes an emotion engine based on the user's visual and audio data to detect the user's emotional state and adjust the display of the analysis results. Specifically, the content and format of the displayed information can be customized considering the user's stress level and concentration level.

[0127] Next, the device provides the user with information drilled down by the emotion engine. The user can visually review the suggested improvements and input feedback and comments through the device that match their emotional state.

[0128] The server receives user feedback, integrates it into the system, and uses it to further improve the accuracy of the artificial intelligence model. This feedback process results in more refined tactical suggestions and an increased success rate for tactics.

[0129] As a concrete example, if a unit is placed in a scenario-specific stress situation during a training scenario, the introduction of an emotion engine makes it possible to provide customized improvement plans based on the psychological state of the unit members. This improves tactical adaptability and dramatically enhances the quality of training.

[0130] The following describes the processing flow.

[0131] Step 1:

[0132] The server collects sensor inputs in real time from the battlefield or training environment, gathering location information, communication logs, and behavioral data. This data is automatically stored in a database and managed without any loss.

[0133] Step 2:

[0134] The server preprocesses the collected data and converts it into an analyzable format. Data cleansing is performed to remove duplicate information and noise, creating a consistent dataset.

[0135] Step 3:

[0136] The server analyzes pre-processed data using an artificial intelligence model to identify factors for success and failure. The analysis employs machine learning algorithms to extract tactical patterns by comparing current data with historical datasets.

[0137] Step 4:

[0138] Based on the server's analysis, the AI ​​generates the next set of tactical improvement suggestions. These suggestions are visualized and converted into heatmaps and recommended action lists.

[0139] Step 5:

[0140] The device uses visual and audio data input from the user's camera and microphone to analyze the user's emotional state with its emotion engine. Based on the detected emotions, it prepares to adjust the type and format of information presented.

[0141] Step 6:

[0142] The device presents visualized improvement suggestions to the user in a tailored format. The information presented is customized according to the user's current psychological state, aiming to reduce stress and facilitate understanding.

[0143] Step 7:

[0144] Users evaluate the proposed improvements and input further opinions and emotional feedback into their devices. This feedback is sent to the server and used as training data to improve the accuracy of future analyses.

[0145] Step 8:

[0146] The server integrates user feedback into the artificial intelligence model, reflecting it in the analysis methods and improvement suggestion generation algorithms, thereby improving the overall accuracy of the system.

[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] Conventional tactical improvement systems lacked sufficient real-time data collection and analysis, resulting in limitations in the accuracy and adaptability of improvement proposals. Furthermore, the absence of feedback functions that considered the user's emotional state made it difficult to accept suggestions. Therefore, there is a need for effective and efficient tactical improvement.

[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 collecting data from the combat environment in real time, means for preprocessing the collected information and converting it into an analyzable format, and means for analyzing the preprocessed information using machine learning algorithms to identify factors for success and failure. This compensates for the conventional problems of insufficient data processing and lack of feedback, and makes it possible to provide highly accurate tactical improvement proposals.

[0152] A "combat environment" refers to the location or situation in which combat or training takes place, and is the area from which data is collected.

[0153] "Data collection" refers to the process of continuously acquiring information from the environment using sensors and other means.

[0154] "Preprocessing" refers to a series of operations, such as cleansing and format conversion, to prepare collected raw data for analysis.

[0155] A "machine learning algorithm" refers to mathematical models and methods that derive patterns and rules from data and automatically perform identification and prediction.

[0156] "Generative AI" refers to artificial intelligence technology that automatically generates new information and suggestions based on given data and conditions.

[0157] "Emotion recognition technology" refers to technology that analyzes and understands a user's emotional state from their visual and auditory data.

[0158] "Feedback" refers to the evaluations and opinions that users provide to the system, which are used to improve the system and make suggestions for future updates.

[0159] "Visualization" refers to displaying data and information in graphs, charts, and other graphical formats to make them easier for users to understand.

[0160] This invention is a system that supports the tactical improvement process through interaction between a server, a terminal, and a user. The server collects data in real time from the combat environment using multiple sensors. GPS sensors, communication modules, and accelerometers are used, and location information, communication information, and action logs obtained from these sensors are temporarily stored in secure storage.

[0161] The server performs data cleansing and format conversion on the collected data. This prepares the data for analysis by machine learning algorithms. Python is used for data processing, and libraries such as TensorFlow and PyTorch are often used for support.

[0162] The server analyzes the data using machine learning algorithms based on pre-processed data. Referring to historical data, it identifies success and failure factors and identifies the effectiveness of patterns or actions in tactics.

[0163] Subsequently, a generative AI model is used to automatically generate tactical improvement proposals for the next game. The system receives prompts such as, "Based on this data, please generate tactical improvement proposals for the next game. Please consider the user's psychological state and include measures to reduce stress." The generated improvement proposals are graphically represented using visualization tools and presented to the user in an easy-to-understand manner.

[0164] Meanwhile, the device utilizes emotion recognition technology to detect the user's emotional state from their visual and audio data. It analyzes data obtained from the camera and microphone, and dynamically adjusts the amount and layout of information, taking into account how the user perceives the displayed information.

[0165] This system allows users to receive improvement suggestions in a format best suited to their needs, and to incorporate their opinions into the system through the feedback function. This feedback is used to improve tactical suggestions, contributing to the accuracy of future suggestions.

[0166] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0167] Step 1:

[0168] The server collects location information, communication information, and activity logs in real time from various sensors installed in the combat environment. This input data is temporarily stored in storage. GPS sensors, communication modules, and accelerometers are used in this operation, and the information from each sensor is integrated and compiled into a dataset.

[0169] Step 2:

[0170] The server performs data cleansing on the data stored in storage. It uses the collected raw data as input. This process removes duplicate data, eliminates noise, and standardizes the format, outputting cleansed data suitable for analysis. This improves the accuracy of the analysis.

[0171] Step 3:

[0172] The server receives the cleansed data and passes it to an artificial intelligence for analysis. Based on the input data, it uses machine learning algorithms to perform analysis to identify factors for success and failure. Using libraries such as TensorFlow and PyTorch, it identifies tactical patterns and effects and outputs the analysis results.

[0173] Step 4:

[0174] Based on the analysis results, the server uses a generative AI model to generate tactical improvement proposals for the next session. The prompt "Generate tactical improvement proposals for the next session based on this data" is entered, and this generation process outputs the improvement proposals as text. The generated improvement proposals are then graphically represented using a visualization tool.

[0175] Step 5:

[0176] The device utilizes emotion recognition technology when displaying visualized improvement suggestions. It uses the user's visual and auditory data as input to detect the user's emotional state. It analyzes data from the camera and microphone, adjusting the displayed content to consider how the user will perceive the information. The adjusted information is then presented to the user.

[0177] Step 6:

[0178] Users review the suggested improvements displayed on their devices and provide feedback. This feedback is received via the device in the form of comments and ratings, which the system then adapts to and stores in the database.

[0179] Step 7:

[0180] The server continuously collects user feedback and uses it to train its artificial intelligence model. This feedback is processed as data for model improvement, contributing to the increased accuracy of the tactical improvement suggestions generated in subsequent updates. This allows the system to consistently provide more refined suggestions.

[0181] (Application Example 2)

[0182] 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".

[0183] In modern factory environments, improving the efficiency of production operations is crucial, but conventional technologies do not adequately collect real-time data on work processes or provide improvement suggestions that take into account the stress levels of workers. Furthermore, improving work efficiency requires considering the psychological state of workers, but there is a lack of effective systems for this purpose. As a result, not only does work efficiency decline, but problems such as the accumulation of fatigue and stress among workers arise.

[0184] 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.

[0185] In this invention, the server includes means for collecting work data in an industrial environment in real time, means for preprocessing the collected data and converting it into an analyzable format, and means for analyzing the preprocessed data and identifying the factors for success and failure of work efficiency using artificial intelligence. This not only enables concrete suggestions for improving work efficiency, but also allows for feedback that takes into account the psychological aspects of workers through an emotion analysis engine.

[0186] An "industrial environment" refers to the environment in which industrial activities take place, such as factories and manufacturing sites, and is a place where various production processes are carried out.

[0187] "Work data" refers to detailed information related to various production activities in factories and workplaces, and includes data such as location information, communication information, and activity logs.

[0188] "Means of real-time data collection" refers to technologies or methods for acquiring data immediately and continuously from the work site. These methods enable a grasp of the most recent work situation.

[0189] "Preprocessing means" refers to techniques or methods for performing data cleansing or format conversion to prepare collected data into a format suitable for analysis.

[0190] "Analyzable format" refers to a state where data has an optimal structure and format for analysis by artificial intelligence, enabling appropriate analysis.

[0191] "Analysis methods using artificial intelligence" refer to technologies or methods that use machine learning algorithms based on data to identify various patterns and features and pinpoint contributing factors.

[0192] "Factors for success and failure" refers to identifying the elements that influence work efficiency and results, and analyzing the causes that lead to success or failure.

[0193] "Means of automatic generation" refers to a technology or method for automatically creating the next work procedure or improvement plan based on the analysis results of artificial intelligence.

[0194] "Presentation means for visualization and delivery" refers to a technology or method for providing generated improvement proposals to users in an easily understandable visual format.

[0195] An "emotion analysis engine" is a technology or system that detects and analyzes an emotional state from a user's visual and auditory data.

[0196] "Users" refers to people who use the system to perform tasks in an industrial environment.

[0197] "Means of collecting feedback" refers to techniques or methods for systematically obtaining opinions and reactions from users.

[0198] "Using it for learning" refers to improving the accuracy of the model based on the feedback data collected by the artificial intelligence, and using that information to inform future suggestions and analyses.

[0199] "Sensing device" is a general term for equipment or sensors used to detect location information, communication information, and activity logs.

[0200] "Identifying stress levels" refers to the process of determining the user's physical and mental burden through an emotion analysis engine and detecting it as part of the analysis.

[0201] This system is designed to improve work efficiency in industrial environments. The server first collects real-time work data from multiple sensing devices placed throughout the factory. This includes location information, communication information, and activity logs. This data is then pre-processed, including redundancy removal and formatting standardization. Once pre-processed, the data is converted into a format that can be analyzed by artificial intelligence.

[0202] The server uses machine learning frameworks like TensorFlow for data analysis, identifying factors that contribute to the success and failure of work efficiency and production activities. Based on these analysis results, suggestions for future work improvements are automatically generated. A generative AI model is used, and prompts are used to output improvement suggestions in real time. For example, a prompt such as "Refer to past product assembly data and generate suggestions for improving current work efficiency" might be generated.

[0203] The generated improvement suggestions are presented to factory workers via a terminal in a visually easy-to-understand format. The terminal functions as smart glasses and uses an emotion analysis engine such as Hume AI to adjust the information based on the worker's emotional state, particularly stress and concentration levels. The presented improvement suggestions can be reviewed interactively by the user, enabling feedback that generally contributes to improved work efficiency and reduced workload.

[0204] As a concrete example, consider a scenario where efficiency drops during product assembly on a manufacturing line. In this case, the smart glasses display suggested changes to the assembly procedure in real time, smoothing the workflow. Furthermore, if emotional analysis determines that the stress level is high, appropriate relaxation methods are suggested. This improves the quality of the workspace and maintains productivity.

[0205] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0206] Step 1:

[0207] The server collects location information, communication information, and activity logs in real time from various sensing devices installed within the factory. The input for this step is data from the sensing devices, and the output is raw data stored on the server. At this stage, the data from the sensing devices is efficiently aggregated to prepare for subsequent processing.

[0208] Step 2:

[0209] The server performs preprocessing on the collected raw data, including noise reduction and data duplication. The input is the raw data stored in step 1, and the output is a dataset formatted for analysis. Specifically, it unifies the data format and removes redundant information.

[0210] Step 3:

[0211] The server analyzes the preprocessed data using TensorFlow to identify factors contributing to the success and failure of the work efficiency. The input for this step is the dataset formatted in step 2, and the output is the analysis results showing the factors for success and failure. This analysis extracts useful patterns from the data and identifies directions for improvement.

[0212] Step 4:

[0213] The server automatically generates improvement plans for the next work process through a generating AI model based on the analysis results. The input for this step is the analysis results obtained in step 3, and the output is specific improvement plans. At this time, the generating AI model is instructed using the prompt message, "Please refer to past assembly data and generate work efficiency improvement plans."

[0214] Step 5:

[0215] The smart glasses used as a terminal receive improvement suggestions provided by the server and visually present them to the user, the worker. The input is the improvement suggestions generated in step 4, and the output is the improvement suggestions displayed to the worker. In this step, the instructions and procedures necessary to improve work efficiency are immediately presented.

[0216] Step 6:

[0217] A device equipped with an emotion analysis engine detects the user's current emotional state from their facial expressions and voice. The input is real-time visual and audio data from the user, and the output is numerical data representing stress levels and concentration levels. Specifically, the system instantly recognizes when the user is experiencing excessive stress.

[0218] Step 7:

[0219] The terminal adjusts the display of information based on data obtained through emotion analysis, providing feedback tailored to the user's stress level and concentration. Input consists of emotion analysis data obtained in step 6 and improvement suggestions from the server, while output is the adjusted display content. This feedback allows the user to receive suggestions for improving their work procedures at the appropriate time.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] [Second Embodiment]

[0224] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0225] 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.

[0226] 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).

[0227] 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.

[0228] 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.

[0229] 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).

[0230] 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.

[0231] 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.

[0232] 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.

[0233] 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.

[0234] 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.

[0235] 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".

[0236] This invention provides a system for efficiently and objectively analyzing post-combat data and providing suggestions for future tactical improvements. This system encompasses a series of processes, from data collection, processing, and analysis during and after combat operations to the presentation of improvement suggestions.

[0237] First, the server collects vast amounts of data generated in the battlefield and training environment in real time. By aggregating and securely storing data obtained from various devices, such as location information, communication logs, and weapon usage information, data loss and loss of accuracy are prevented.

[0238] Next, the data collected by the server is preprocessed and converted into a format suitable for advanced analysis. Specifically, data cleansing is performed to remove duplicate data and noise. In addition, data obtained from different devices is converted into a unified format, and the time and location of the data are synchronized.

[0239] Next, the server uses artificial intelligence to analyze the data. During this process, it utilizes machine learning algorithms, referencing past battle data, to identify the factors behind success and failure. This AI analysis determines which tactics are effective and which require improvement.

[0240] Subsequently, the server generates tactical improvement proposals for the next phase based on the analysis results. The generating AI designs specific tactical changes based on the insights gained and presents them in a visualized format. For example, it may suggest the tactical effectiveness of waiting at a specific location or methods for improving communication.

[0241] Next, the device presents the generated improvement suggestions to the user. The suggestions are visually organized and displayed as graphical charts and heatmaps, making them easy for the user to understand intuitively and aiding in quick decision-making.

[0242] Finally, based on the improvement suggestions provided by the user, the system develops actual tactical plans. By providing feedback, the system incorporates that information as learning data for further improvement. This allows the system to continuously learn and optimize its tactics.

[0243] For example, when a unit carries out a specific operation in a training scenario, this system can be used to immediately analyze the results and clearly identify areas for improvement for the next training session. As a result, the unit's response capabilities are strengthened, and more efficient training can be achieved.

[0244] The following describes the processing flow.

[0245] Step 1:

[0246] The server collects data in real time from sensors and devices during combat and automatically stores it in a central database. This data includes location information, communication logs, and time-series information on actions, and the collection process is continuously monitored to prevent data loss.

[0247] Step 2:

[0248] The server preprocesses the collected raw data and converts it into an analyzable format. Specifically, it cleanses the data to remove duplicates and noise. It also unifies data in different formats and synchronizes data between devices.

[0249] Step 3:

[0250] The server feeds pre-processed data into an artificial intelligence model and begins the analysis. The AI ​​uses machine learning algorithms to identify success and failure factors while comparing them with historical data. This analysis process identifies the effectiveness of specific tactical patterns and actions.

[0251] Step 4:

[0252] The server uses AI generation based on the analysis results to automatically generate tactical improvement proposals for the next phase. The generated improvement proposals are converted into a visualized format and include, for example, recommended points for tactical changes and suggestions for optimizing positioning.

[0253] Step 5:

[0254] The device presents users with visually organized improvement suggestions in real time. These suggestions are displayed as graphical charts and heatmaps, making it easy for users to intuitively understand the analysis results.

[0255] Step 6:

[0256] The user evaluates the suggested improvements and inputs feedback into the terminal to incorporate them into the next tactical plan. This feedback is then sent to the server to further improve the tactics.

[0257] Step 7:

[0258] The server receives user feedback and incorporates it into the AI ​​model as training data. This helps improve the accuracy of the analysis and generate suggestions for future improvements. The AI ​​incorporates the feedback and derives new insights in subsequent data analyses.

[0259] (Example 1)

[0260] 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."

[0261] An efficient system is needed to objectively and quickly evaluate the effectiveness of tactical actions in the field and use that information to improve future actions. Traditional methods rely heavily on manual data collection and analysis, which is time-consuming and labor-intensive, and can be influenced by subjective judgments. As a result, tactical improvements are often insufficient, and rapid decision-making is difficult.

[0262] 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.

[0263] In this invention, the server includes means for aggregating information in real time at the field, means for organizing the aggregated information and converting it into an analyzable data format, and analytical means utilizing machine learning to analyze the organized data and identify specific factors. This makes it possible to efficiently process and analyze the vast amount of data obtained from the field and to scientifically and objectively improve the next tactical plan.

[0264] "Methods for collecting information in real time at the site" refers to technical methods for collecting and centrally managing data transmitted from the field.

[0265] "Means for organizing aggregated information and converting it into an analyzable data format" refers to methods for appropriately editing collected raw data, converting it into a unified format, and facilitating subsequent analysis.

[0266] "Analysis methods utilizing machine learning" are techniques that use algorithms to find patterns and causal relationships based on past data, and to make predictions and judgments.

[0267] "A means of automatically generating improvement suggestions for the next operation" refers to a system that utilizes analysis results to mechanically generate specific suggestions for efficiently improving the next action plan.

[0268] "Visual presentation methods that provide information to users" refer to technologies for displaying generated information or proposals in a way that is easy for humans to understand, such as using graphs and charts to convey information intuitively.

[0269] This invention is an information processing system aimed at improving tactical actions, and is composed primarily of a server, terminals, and users.

[0270] The server aggregates a wide variety of data collected on-site in real time. This process includes the immediate collection of data such as location information, communication logs, and activity records. The server securely stores this data in a database, which serves as the foundation for subsequent processing.

[0271] After data aggregation, the server organizes this information into a parseable format. For hardware, a server with a high-spec processor and sufficient storage capacity is recommended. Data formats such as CSV and JSON are used for standardization. A cleansing process removes noise and duplicate data, converting the data into a consistent format.

[0272] Next, the server performs machine learning analysis on the organized data. This analysis utilizes deep learning frameworks such as TensorFlow and PyTorch, with generative AI models identifying success factors and areas for improvement. The machine learning algorithms learn from past data and recognize similar patterns, enabling highly accurate analysis.

[0273] The server then automatically generates suggestions for improving the next operation based on the analysis results. These suggestions are visually displayed on the dashboard, making it easy to review tactical improvements. These suggestions may include tactical measures such as changes to troop deployment or improvements to communication protocols.

[0274] Once the proposal is complete, the device visualizes and presents it to the user. Using graphs, heatmaps, and other visual aids, the device provides intuitive understanding and helps users make quick decisions.

[0275] Finally, the user develops a plan of action for the next step based on the suggestions provided. The user's feedback is incorporated into the system and used as a reference for future data analysis. This allows the system to provide more appropriate suggestions over time.

[0276] As a concrete example, in a training scenario, evaluating a specific operation and immediately identifying problems allows for appropriate improvements to be made in the next training session. An example of a prompt message is, "Analyze the results of the operation in the training scenario and propose tactical improvements for the next session."

[0277] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0278] Step 1:

[0279] The server receives raw data transmitted from the site. This data includes location information, communication logs, and activity records, which are collected from various sensors. The server stores this information in a database. Here, the input is the raw data from the site, and the output is the unprocessed data before being sorted out.

[0280] Step 2:

[0281] The server performs data cleansing on the collected unprocessed data. This is a process of removing noise and duplicate entries in the data and selecting valid data. The input is the unprocessed data stored in the database, and the output is clean data with noise removed and the format unified.

[0282] Step 3:

[0283] The server converts the clean data into an analyzable format. It performs format conversion, integrates data from different devices, and adjusts the time series. Here, the CSV or JSON format is used. The input is the clean data, and the output is the data format suitable for analysis.

[0284] Step 4:

[0285] The server applies a machine learning model to the data suitable for analysis. Using a deep learning framework such as TensorFlow, it analyzes the factors of success and failure while referring to past data. The input is the data format suitable for analysis, and the output is the analysis result (identification of success factors and failure factors).

[0286] Step 5:

[0287] Based on the obtained analysis results, the server automatically generates a plan for improving the next operation. Using a generative AI model, it designs specific proposals and displays them in the form of a dashboard. The input is the analysis result, and the output is the generated improvement proposal.

[0288] Step 6:

[0289] The terminal visualizes and presents the generated improvement suggestions to the user. Using a GUI, the suggestions are visually represented with graphs and heatmaps, making them easy for the user to understand intuitively. The input is the generated improvement suggestions, and the output is the visualized suggestion information.

[0290] Step 7:

[0291] Based on the suggested improvements, users form their next action plan. Users provide feedback to the system, which is then used for future data analysis. The input here is visualized suggestion information, and the output is feedback information.

[0292] (Application Example 1)

[0293] 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 glasses 214 will be referred to as the "terminal."

[0294] Improving operational efficiency in logistics centers is a critical challenge. Traditional methods require significant time and effort to understand operational status and generate improvement plans, making immediate optimization difficult. To address this, real-time data analysis and immediate proposal of improvements are essential.

[0295] 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.

[0296] In this invention, the server includes means for collecting operational data in real time, means for preprocessing the collected data and converting it into an analyzable format, and means for analyzing the preprocessed data and identifying the factors for success and failure of operations using artificial intelligence. This makes it possible to analyze operational data in a logistics center in real time and immediately generate and present improvement proposals.

[0297] "Business data" refers to all information related to operations that occur within the logistics center, and this includes location information, communication information, operation logs, etc.

[0298] "Means of real-time data collection" refers to technologies and devices for instantly recording data generated during a task.

[0299] "Means of preprocessing and converting into an analyzable format" refers to techniques for shaping raw data into an appropriate format and processing it to a state suitable for analysis.

[0300] "Analysis methods using artificial intelligence" refer to methods that employ machine learning algorithms to learn from past data and evaluate current data based on the insights gained from that learning.

[0301] "Means of presenting improvement proposals to users in a visual form, such as diagrams and graphs, are technologies and devices that display analysis results in a visual form, allowing users to understand them intuitively.

[0302] The system that realizes this invention collects and analyzes operational data in real time at a logistics center and immediately provides improvement suggestions.

[0303] The server collects operational data generated from various devices within the logistics center in real time. This includes location information, communication information, and operation logs. The server then preprocesses the collected data and converts it into a format suitable for analysis. This preprocessing includes removing duplicate data and noise.

[0304] Next, the server uses artificial intelligence to analyze the data. This includes analytical methods that use machine learning algorithms that learn from past data to identify factors influencing the success and failure of operations from current data. Specifically, it uses tools such as Python and scikit-learn to perform dimensionality reduction using PCA and KMeans clustering.

[0305] The improvement plan generated by the server is visualized and provided to the terminal. The terminal uses a visualization library such as Matplotlib to present the improvement plan to the user as a graph or heatmap. The user can thereby obtain visual insights into the business process and make immediate decisions.

[0306] As a specific example, one day in a logistics center, it was discovered that handling a certain product took an excessive amount of time. At that time, the server generated an improvement plan to reexamine the product placement and presented it to the user through the terminal. As a result, by changing the product placement, the working hours could be shortened.

[0307] An example of a prompt sentence is "Please generate a process improvement plan based on the daily operation data of the logistics center." In this way, the efficiency of the logistics business can be achieved.

[0308] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0309] Step 1:

[0310] The server collects business data obtained from various devices in the logistics center in real time. The input is data such as location information and operation logs transmitted from the devices, and the output is a set of raw data. The data is sent directly to the server from sensors and terminals via wireless or wired communication.

[0311] Step 2:

[0312] The server performs preprocessing on the collected raw data and converts it into a format suitable for analysis. The input is the raw data obtained in Step 1, and the output is the cleaned data. Specifically, the data is shaped by eliminating data duplicates, removing noise, and filling in missing values. This improves the accuracy of the analysis.

[0313] Step 3:

[0314] The server analyzes pre-processed data using artificial intelligence. The input is the cleaned data generated in step 2, and the output is the key factors and their evaluation as a result of the analysis. By applying machine learning algorithms, patterns in the data are identified and factors that differentiate success from failure are extracted.

[0315] Step 4:

[0316] The server generates a plan for future business improvements based on the analysis results obtained. The input is the analysis results from step 3, and the output is a list of improvement plans. Using the generation AI model, specific process changes to improve work efficiency are automatically designed.

[0317] Step 5:

[0318] The terminal visualizes the improvement suggestions sent from the server and presents them to the user. The input is the improvement suggestions created in step 4, and the output is visualized information in a format that is easy for the user to understand visually (e.g., graphs, heatmaps). The terminal utilizes visualization libraries such as Matplotlib to convert the information into a visually understandable format.

[0319] Step 6:

[0320] Users evaluate the proposed improvements and provide feedback. Input is visualization information presented from the device, and output is feedback information. This feedback is returned to the server as data useful for subsequent analysis and improvement cycles.

[0321] 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.

[0322] This invention is a system that collects combat and training data in real time, provides suggestions for improving tactics for the next session based on the collected data, and further achieves more effective tactical feedback by combining it with an emotion engine that recognizes the user's emotions.

[0323] First, the server automatically collects combat data such as location information, communication information, and action logs in real time from battlefields and training environments using multiple sensors. This data is securely stored and organized in a format that allows for appropriate subsequent data processing.

[0324] Next, the data collected by the server is preprocessed and converted into a format suitable for analysis by artificial intelligence. This preprocessing includes data cleansing (removal of duplicates and noise reduction) and format conversion, as well as the integration of time and location information.

[0325] Subsequently, the server analyzes the pre-processed data using an artificial intelligence model. The AI ​​uses machine learning algorithms to identify success and failure factors, referencing past data, and identifies tactical patterns and the effectiveness of actions.

[0326] Based on the analysis results, the server uses AI to automatically generate tactical improvement proposals for the next battle. These proposals are graphically represented using visualization tools and prepared for presentation to the user. For example, suggestions for changing waiting locations or improving communication protocols may be presented.

[0327] In this invention, the terminal utilizes an emotion engine based on the user's visual and audio data to detect the user's emotional state and adjust the display of the analysis results. Specifically, the content and format of the displayed information can be customized considering the user's stress level and concentration level.

[0328] Next, the device provides the user with information drilled down by the emotion engine. The user can visually review the suggested improvements and input feedback and comments through the device that match their emotional state.

[0329] The server receives user feedback, integrates it into the system, and uses it to further improve the accuracy of the artificial intelligence model. This feedback process results in more refined tactical suggestions and an increased success rate for tactics.

[0330] As a concrete example, if a unit is placed in a scenario-specific stress situation during a training scenario, the introduction of an emotion engine makes it possible to provide customized improvement plans based on the psychological state of the unit members. This improves tactical adaptability and dramatically enhances the quality of training.

[0331] The following describes the processing flow.

[0332] Step 1:

[0333] The server collects sensor inputs in real time from the battlefield or training environment, gathering location information, communication logs, and behavioral data. This data is automatically stored in a database and managed without any loss.

[0334] Step 2:

[0335] The server preprocesses the collected data and converts it into an analyzable format. Data cleansing is performed to remove duplicate information and noise, creating a consistent dataset.

[0336] Step 3:

[0337] The server analyzes pre-processed data using an artificial intelligence model to identify factors for success and failure. The analysis employs machine learning algorithms to extract tactical patterns by comparing current data with historical datasets.

[0338] Step 4:

[0339] Based on the server's analysis, the AI ​​generates the next set of tactical improvement suggestions. These suggestions are visualized and converted into heatmaps and recommended action lists.

[0340] Step 5:

[0341] The device uses visual and audio data input from the user's camera and microphone to analyze the user's emotional state with its emotion engine. Based on the detected emotions, it prepares to adjust the type and format of information presented.

[0342] Step 6:

[0343] The device presents visualized improvement suggestions to the user in a tailored format. The information presented is customized according to the user's current psychological state, aiming to reduce stress and facilitate understanding.

[0344] Step 7:

[0345] Users evaluate the proposed improvements and input further opinions and emotional feedback into their devices. This feedback is sent to the server and used as training data to improve the accuracy of future analyses.

[0346] Step 8:

[0347] The server integrates user feedback into the artificial intelligence model, reflecting it in the analysis methods and improvement suggestion generation algorithms, thereby improving the overall accuracy of the system.

[0348] (Example 2)

[0349] 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".

[0350] Conventional tactical improvement systems lacked sufficient real-time data collection and analysis, resulting in limitations in the accuracy and adaptability of improvement proposals. Furthermore, the absence of feedback functions that considered the user's emotional state made it difficult to accept suggestions. Therefore, there is a need for effective and efficient tactical improvement.

[0351] 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.

[0352] In this invention, the server includes means for collecting data from the combat environment in real time, means for preprocessing the collected information and converting it into an analyzable format, and means for analyzing the preprocessed information using machine learning algorithms to identify factors for success and failure. This compensates for the conventional problems of insufficient data processing and lack of feedback, and makes it possible to provide highly accurate tactical improvement proposals.

[0353] A "combat environment" refers to the location or situation in which combat or training takes place, and is the area from which data is collected.

[0354] "Data collection" refers to the process of continuously acquiring information from the environment using sensors and other means.

[0355] "Preprocessing" refers to a series of operations, such as cleansing and format conversion, to prepare collected raw data for analysis.

[0356] A "machine learning algorithm" refers to mathematical models and methods that derive patterns and rules from data and automatically perform identification and prediction.

[0357] "Generative AI" refers to artificial intelligence technology that automatically generates new information and suggestions based on given data and conditions.

[0358] "Emotion recognition technology" refers to technology that analyzes and understands a user's emotional state from their visual and auditory data.

[0359] "Feedback" refers to the evaluations and opinions that users provide to the system, which are used to improve the system and make suggestions for future updates.

[0360] "Visualization" refers to displaying data and information in graphs, charts, and other graphical formats to make them easier for users to understand.

[0361] This invention is a system that supports the tactical improvement process through interaction between a server, a terminal, and a user. The server collects data in real time from the combat environment using multiple sensors. GPS sensors, communication modules, and accelerometers are used, and location information, communication information, and action logs obtained from these sensors are temporarily stored in secure storage.

[0362] The server performs data cleansing and format conversion on the collected data. This prepares the data for analysis by machine learning algorithms. Python is used for data processing, and libraries such as TensorFlow and PyTorch are often used for support.

[0363] The server analyzes the data using machine learning algorithms based on pre-processed data. Referring to historical data, it identifies success and failure factors and identifies the effectiveness of patterns or actions in tactics.

[0364] Subsequently, a generative AI model is used to automatically generate tactical improvement proposals for the next game. The system receives prompts such as, "Based on this data, please generate tactical improvement proposals for the next game. Please consider the user's psychological state and include measures to reduce stress." The generated improvement proposals are graphically represented using visualization tools and presented to the user in an easy-to-understand manner.

[0365] Meanwhile, the device utilizes emotion recognition technology to detect the user's emotional state from their visual and audio data. It analyzes data obtained from the camera and microphone, and dynamically adjusts the amount and layout of information, taking into account how the user perceives the displayed information.

[0366] This system allows users to receive improvement suggestions in a format best suited to their needs, and to incorporate their opinions into the system through the feedback function. This feedback is used to improve tactical suggestions, contributing to the accuracy of future suggestions.

[0367] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0368] Step 1:

[0369] The server collects location information, communication information, and activity logs in real time from various sensors installed in the combat environment. This input data is temporarily stored in storage. GPS sensors, communication modules, and accelerometers are used in this operation, and the information from each sensor is integrated and compiled into a dataset.

[0370] Step 2:

[0371] The server performs data cleansing on the data stored in storage. It uses the collected raw data as input. This process removes duplicate data, eliminates noise, and standardizes the format, outputting cleansed data suitable for analysis. This improves the accuracy of the analysis.

[0372] Step 3:

[0373] The server receives the cleansed data and passes it to an artificial intelligence for analysis. Based on the input data, it uses machine learning algorithms to perform analysis to identify factors for success and failure. Using libraries such as TensorFlow and PyTorch, it identifies tactical patterns and effects and outputs the analysis results.

[0374] Step 4:

[0375] Based on the analysis results, the server uses a generative AI model to generate tactical improvement proposals for the next session. The prompt "Generate tactical improvement proposals for the next session based on this data" is entered, and this generation process outputs the improvement proposals as text. The generated improvement proposals are then graphically represented using a visualization tool.

[0376] Step 5:

[0377] The device utilizes emotion recognition technology when displaying visualized improvement suggestions. It uses the user's visual and auditory data as input to detect the user's emotional state. It analyzes data from the camera and microphone, adjusting the displayed content to consider how the user will perceive the information. The adjusted information is then presented to the user.

[0378] Step 6:

[0379] Users review the suggested improvements displayed on their devices and provide feedback. This feedback is received via the device in the form of comments and ratings, which the system then adapts to and stores in the database.

[0380] Step 7:

[0381] The server continuously collects user feedback and uses it to train its artificial intelligence model. This feedback is processed as data for model improvement, contributing to the increased accuracy of the tactical improvement suggestions generated in subsequent updates. This allows the system to consistently provide more refined suggestions.

[0382] (Application Example 2)

[0383] 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."

[0384] In modern factory environments, improving the efficiency of production operations is crucial, but conventional technologies do not adequately collect real-time data on work processes or provide improvement suggestions that take into account the stress levels of workers. Furthermore, improving work efficiency requires considering the psychological state of workers, but there is a lack of effective systems for this purpose. As a result, not only does work efficiency decline, but problems such as the accumulation of fatigue and stress among workers arise.

[0385] 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.

[0386] In this invention, the server includes means for collecting work data in an industrial environment in real time, means for preprocessing the collected data and converting it into an analyzable format, and means for analyzing the preprocessed data and identifying the factors for success and failure of work efficiency using artificial intelligence. This not only enables concrete suggestions for improving work efficiency, but also allows for feedback that takes into account the psychological aspects of workers through an emotion analysis engine.

[0387] An "industrial environment" refers to the environment in which industrial activities take place, such as factories and manufacturing sites, and is a place where various production processes are carried out.

[0388] "Work data" refers to detailed information related to various production activities in factories and workplaces, and includes data such as location information, communication information, and activity logs.

[0389] "Means of real-time data collection" refers to technologies or methods for acquiring data immediately and continuously from the work site. These methods enable a grasp of the most recent work situation.

[0390] "Preprocessing means" refers to techniques or methods for performing data cleansing or format conversion to prepare collected data into a format suitable for analysis.

[0391] "Analyzable format" refers to a state where data has an optimal structure and format for analysis by artificial intelligence, enabling appropriate analysis.

[0392] "Analysis methods using artificial intelligence" refer to technologies or methods that use machine learning algorithms based on data to identify various patterns and features and pinpoint contributing factors.

[0393] "Factors for success and failure" refers to identifying the elements that influence work efficiency and results, and analyzing the causes that lead to success or failure.

[0394] "Means of automatic generation" refers to a technology or method for automatically creating the next work procedure or improvement plan based on the analysis results of artificial intelligence.

[0395] "Presentation means for visualization and delivery" refers to a technology or method for providing generated improvement proposals to users in an easily understandable visual format.

[0396] An "emotion analysis engine" is a technology or system that detects and analyzes an emotional state from a user's visual and auditory data.

[0397] "Users" refers to people who use the system to perform tasks in an industrial environment.

[0398] "Means of collecting feedback" refers to techniques or methods for systematically obtaining opinions and reactions from users.

[0399] "Using it for learning" refers to improving the accuracy of the model based on the feedback data collected by the artificial intelligence, and using that information to inform future suggestions and analyses.

[0400] "Sensing device" is a general term for equipment or sensors used to detect location information, communication information, and activity logs.

[0401] "Identifying stress levels" refers to the process of determining the user's physical and mental burden through an emotion analysis engine and detecting it as part of the analysis.

[0402] This system is designed to improve work efficiency in industrial environments. The server first collects real-time work data from multiple sensing devices placed throughout the factory. This includes location information, communication information, and activity logs. This data is then pre-processed, including redundancy removal and formatting standardization. Once pre-processed, the data is converted into a format that can be analyzed by artificial intelligence.

[0403] The server uses machine learning frameworks like TensorFlow for data analysis, identifying factors that contribute to the success and failure of work efficiency and production activities. Based on these analysis results, suggestions for future work improvements are automatically generated. A generative AI model is used, and prompts are used to output improvement suggestions in real time. For example, a prompt such as "Refer to past product assembly data and generate suggestions for improving current work efficiency" might be generated.

[0404] The generated improvement suggestions are presented to factory workers via a terminal in a visually easy-to-understand format. The terminal functions as smart glasses and uses an emotion analysis engine such as Hume AI to adjust the information based on the worker's emotional state, particularly stress and concentration levels. The presented improvement suggestions can be reviewed interactively by the user, enabling feedback that generally contributes to improved work efficiency and reduced workload.

[0405] As a concrete example, consider a scenario where efficiency drops during product assembly on a manufacturing line. In this case, the smart glasses display suggested changes to the assembly procedure in real time, smoothing the workflow. Furthermore, if emotional analysis determines that the stress level is high, appropriate relaxation methods are suggested. This improves the quality of the workspace and maintains productivity.

[0406] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0407] Step 1:

[0408] The server collects location information, communication information, and activity logs in real time from various sensing devices installed within the factory. The input for this step is data from the sensing devices, and the output is raw data stored on the server. At this stage, the data from the sensing devices is efficiently aggregated to prepare for subsequent processing.

[0409] Step 2:

[0410] The server performs preprocessing on the collected raw data, including noise reduction and data duplication. The input is the raw data stored in step 1, and the output is a dataset formatted for analysis. Specifically, it unifies the data format and removes redundant information.

[0411] Step 3:

[0412] The server analyzes the preprocessed data using TensorFlow to identify factors contributing to the success and failure of the work efficiency. The input for this step is the dataset formatted in step 2, and the output is the analysis results showing the factors for success and failure. This analysis extracts useful patterns from the data and identifies directions for improvement.

[0413] Step 4:

[0414] The server automatically generates improvement plans for the next work process through a generating AI model based on the analysis results. The input for this step is the analysis results obtained in step 3, and the output is specific improvement plans. At this time, the generating AI model is instructed using the prompt message, "Please refer to past assembly data and generate work efficiency improvement plans."

[0415] Step 5:

[0416] The smart glasses used as a terminal receive improvement suggestions provided by the server and visually present them to the user, the worker. The input is the improvement suggestions generated in step 4, and the output is the improvement suggestions displayed to the worker. In this step, the instructions and procedures necessary to improve work efficiency are immediately presented.

[0417] Step 6:

[0418] A device equipped with an emotion analysis engine detects the user's current emotional state from their facial expressions and voice. The input is real-time visual and audio data from the user, and the output is numerical data representing stress levels and concentration levels. Specifically, the system instantly recognizes when the user is experiencing excessive stress.

[0419] Step 7:

[0420] The terminal adjusts the display of information based on data obtained through emotion analysis, providing feedback tailored to the user's stress level and concentration. Input consists of emotion analysis data obtained in step 6 and improvement suggestions from the server, while output is the adjusted display content. This feedback allows the user to receive suggestions for improving their work procedures at the appropriate time.

[0421] 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.

[0422] 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.

[0423] 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.

[0424] [Third Embodiment]

[0425] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0426] 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.

[0427] 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).

[0428] 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.

[0429] 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.

[0430] 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).

[0431] 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.

[0432] 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.

[0433] 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.

[0434] 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.

[0435] 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.

[0436] 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".

[0437] This invention provides a system for efficiently and objectively analyzing post-combat data and providing suggestions for future tactical improvements. This system encompasses a series of processes, from data collection, processing, and analysis during and after combat operations to the presentation of improvement suggestions.

[0438] First, the server collects vast amounts of data generated in the battlefield and training environment in real time. By aggregating and securely storing data obtained from various devices, such as location information, communication logs, and weapon usage information, data loss and loss of accuracy are prevented.

[0439] Next, the data collected by the server is preprocessed and converted into a format suitable for advanced analysis. Specifically, data cleansing is performed to remove duplicate data and noise. In addition, data obtained from different devices is converted into a unified format, and the time and location of the data are synchronized.

[0440] Next, the server uses artificial intelligence to analyze the data. During this process, it utilizes machine learning algorithms, referencing past battle data, to identify the factors behind success and failure. This AI analysis determines which tactics are effective and which require improvement.

[0441] Subsequently, the server generates tactical improvement proposals for the next phase based on the analysis results. The generating AI designs specific tactical changes based on the insights gained and presents them in a visualized format. For example, it may suggest the tactical effectiveness of waiting at a specific location or methods for improving communication.

[0442] Next, the device presents the generated improvement suggestions to the user. The suggestions are visually organized and displayed as graphical charts and heatmaps, making them easy for the user to understand intuitively and aiding in quick decision-making.

[0443] Finally, based on the improvement suggestions provided by the user, the system develops actual tactical plans. By providing feedback, the system incorporates that information as learning data for further improvement. This allows the system to continuously learn and optimize its tactics.

[0444] For example, when a unit carries out a specific operation in a training scenario, this system can be used to immediately analyze the results and clearly identify areas for improvement for the next training session. As a result, the unit's response capabilities are strengthened, and more efficient training can be achieved.

[0445] The following describes the processing flow.

[0446] Step 1:

[0447] The server collects data in real time from sensors and devices during combat and automatically stores it in a central database. This data includes location information, communication logs, and time-series information on actions, and the collection process is continuously monitored to prevent data loss.

[0448] Step 2:

[0449] The server preprocesses the collected raw data and converts it into an analyzable format. Specifically, it cleanses the data to remove duplicates and noise. It also unifies data in different formats and synchronizes data between devices.

[0450] Step 3:

[0451] The server feeds pre-processed data into an artificial intelligence model and begins the analysis. The AI ​​uses machine learning algorithms to identify success and failure factors while comparing them with historical data. This analysis process identifies the effectiveness of specific tactical patterns and actions.

[0452] Step 4:

[0453] The server uses AI generation based on the analysis results to automatically generate tactical improvement proposals for the next phase. The generated improvement proposals are converted into a visualized format and include, for example, recommended points for tactical changes and suggestions for optimizing positioning.

[0454] Step 5:

[0455] The device presents users with visually organized improvement suggestions in real time. These suggestions are displayed as graphical charts and heatmaps, making it easy for users to intuitively understand the analysis results.

[0456] Step 6:

[0457] The user evaluates the suggested improvements and inputs feedback into the terminal to incorporate them into the next tactical plan. This feedback is then sent to the server to further improve the tactics.

[0458] Step 7:

[0459] The server receives user feedback and incorporates it into the AI ​​model as training data. This helps improve the accuracy of the analysis and generate suggestions for future improvements. The AI ​​incorporates the feedback and derives new insights in subsequent data analyses.

[0460] (Example 1)

[0461] 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."

[0462] An efficient system is needed to objectively and quickly evaluate the effectiveness of tactical actions in the field and use that information to improve future actions. Traditional methods rely heavily on manual data collection and analysis, which is time-consuming and labor-intensive, and can be influenced by subjective judgments. As a result, tactical improvements are often insufficient, and rapid decision-making is difficult.

[0463] 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.

[0464] In this invention, the server includes means for aggregating information in real time at the field, means for organizing the aggregated information and converting it into an analyzable data format, and analytical means utilizing machine learning to analyze the organized data and identify specific factors. This makes it possible to efficiently process and analyze the vast amount of data obtained from the field and to scientifically and objectively improve the next tactical plan.

[0465] "Methods for collecting information in real time at the site" refers to technical methods for collecting and centrally managing data transmitted from the field.

[0466] "Means for organizing aggregated information and converting it into an analyzable data format" refers to methods for appropriately editing collected raw data, converting it into a unified format, and facilitating subsequent analysis.

[0467] "Analysis methods utilizing machine learning" are techniques that use algorithms to find patterns and causal relationships based on past data, and to make predictions and judgments.

[0468] "A means of automatically generating improvement suggestions for the next operation" refers to a system that utilizes analysis results to mechanically generate specific suggestions for efficiently improving the next action plan.

[0469] "Visual presentation methods that provide information to users" refer to technologies for displaying generated information or proposals in a way that is easy for humans to understand, such as using graphs and charts to convey information intuitively.

[0470] This invention is an information processing system aimed at improving tactical actions, and is composed primarily of a server, terminals, and users.

[0471] The server aggregates a wide variety of data collected on-site in real time. This process includes the immediate collection of data such as location information, communication logs, and activity records. The server securely stores this data in a database, which serves as the foundation for subsequent processing.

[0472] After data aggregation, the server organizes this information into a parseable format. For hardware, a server with a high-spec processor and sufficient storage capacity is recommended. Data formats such as CSV and JSON are used for standardization. A cleansing process removes noise and duplicate data, converting the data into a consistent format.

[0473] Next, the server performs machine learning analysis on the organized data. This analysis utilizes deep learning frameworks such as TensorFlow and PyTorch, with generative AI models identifying success factors and areas for improvement. The machine learning algorithms learn from past data and recognize similar patterns, enabling highly accurate analysis.

[0474] The server then automatically generates suggestions for improving the next operation based on the analysis results. These suggestions are visually displayed on the dashboard, making it easy to review tactical improvements. These suggestions may include tactical measures such as changes to troop deployment or improvements to communication protocols.

[0475] Once the proposal is complete, the device visualizes and presents it to the user. Using graphs, heatmaps, and other visual aids, the device provides intuitive understanding and helps users make quick decisions.

[0476] Finally, the user develops a plan of action for the next step based on the suggestions provided. The user's feedback is incorporated into the system and used as a reference for future data analysis. This allows the system to provide more appropriate suggestions over time.

[0477] As a concrete example, in a training scenario, evaluating a specific operation and immediately identifying problems allows for appropriate improvements to be made in the next training session. An example of a prompt message is, "Analyze the results of the operation in the training scenario and propose tactical improvements for the next session."

[0478] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0479] Step 1:

[0480] The server receives raw data transmitted from the field. This data includes location information, communication logs, and activity records, collected from various sensors. The server stores this information in a database. The input here is raw data from the field, and the output is unprocessed data before it is organized.

[0481] Step 2:

[0482] The server performs data cleansing on the collected raw data. This process removes noise and duplicate entries from the data and selects valid data. The input is raw data stored in the database, and the output is clean data with noise removed and a unified format.

[0483] Step 3:

[0484] The server converts clean data into an analyzable format. It performs format conversion, integrates data from different devices, and adjusts the time series. CSV and JSON formats are used here. The input is clean data, and the output is in a data format suitable for analysis.

[0485] Step 4:

[0486] The server applies machine learning models to data suitable for analysis. Using deep learning frameworks such as TensorFlow, it analyzes the factors of success and failure while referencing historical data. The input is data in a format suitable for analysis, and the output is the analysis result (identification of success and failure factors).

[0487] Step 5:

[0488] The server automatically generates improvement plans for the next operation based on the analysis results obtained. It uses a generating AI model to design specific suggestions and displays them in a dashboard format. The input is the analysis results, and the output is the generated improvement suggestions.

[0489] Step 6:

[0490] The terminal visualizes and presents the generated improvement suggestions to the user. Using a GUI, the suggestions are visually represented with graphs and heatmaps, making them easy for the user to understand intuitively. The input is the generated improvement suggestions, and the output is the visualized suggestion information.

[0491] Step 7:

[0492] Based on the suggested improvements, users form their next action plan. Users provide feedback to the system, which is then used for future data analysis. The input here is visualized suggestion information, and the output is feedback information.

[0493] (Application Example 1)

[0494] 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."

[0495] Improving operational efficiency in logistics centers is a critical challenge. Traditional methods require significant time and effort to understand operational status and generate improvement plans, making immediate optimization difficult. To address this, real-time data analysis and immediate proposal of improvements are essential.

[0496] 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.

[0497] In this invention, the server includes means for collecting operational data in real time, means for preprocessing the collected data and converting it into an analyzable format, and means for analyzing the preprocessed data and identifying the factors for success and failure of operations using artificial intelligence. This makes it possible to analyze operational data in a logistics center in real time and immediately generate and present improvement proposals.

[0498] "Business data" refers to all information related to operations that occur within the logistics center, and this includes location information, communication information, operation logs, etc.

[0499] "Means of real-time data collection" refers to technologies and devices for instantly recording data generated during a task.

[0500] "Means of preprocessing and converting into an analyzable format" refers to techniques for shaping raw data into an appropriate format and processing it to a state suitable for analysis.

[0501] "Analysis methods using artificial intelligence" refer to methods that employ machine learning algorithms to learn from past data and evaluate current data based on the insights gained from that learning.

[0502] "Means of presenting improvement proposals to users in a visual form, such as diagrams and graphs, are technologies and devices that display analysis results in a visual form, allowing users to understand them intuitively.

[0503] The system that realizes this invention collects and analyzes operational data in real time at a logistics center and immediately provides improvement suggestions.

[0504] The server collects operational data generated from various devices within the logistics center in real time. This includes location information, communication information, and operation logs. The server then preprocesses the collected data and converts it into a format suitable for analysis. This preprocessing includes removing duplicate data and noise.

[0505] Next, the server uses artificial intelligence to analyze the data. This includes analytical methods that use machine learning algorithms that learn from past data to identify factors influencing the success and failure of operations from current data. Specifically, it uses tools such as Python and scikit-learn to perform dimensionality reduction using PCA and KMeans clustering.

[0506] The improvement suggestions generated by the server are visualized and provided to the terminal. The terminal uses visualization libraries such as Matplotlib to present the improvement suggestions to the user as graphs and heatmaps. This allows the user to gain visual insights into business processes and make immediate decisions.

[0507] As a concrete example, one day at a logistics center, it was discovered that handling a particular product was taking an excessive amount of time. The server then generated improvement suggestions, including a reassessment of the product's layout, and presented them to the user via a terminal. As a result, the work time was reduced by changing the product's placement.

[0508] An example of a prompt message is, "Generate process improvement proposals based on daily work data from the logistics center." In this way, the efficiency of logistics operations can be improved.

[0509] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0510] Step 1:

[0511] The server collects operational data in real time from various devices within the logistics center. Inputs include location information and operation logs transmitted from the devices, while outputs are collections of raw data. Data is sent directly to the server from sensors and terminals via wireless or wired communication.

[0512] Step 2:

[0513] The server preprocesses the collected raw data and converts it into a format suitable for analysis. The input is the raw data obtained in step 1, and the output is the cleaned data. Specifically, the data is shaped by removing duplicates, removing noise, and imputing missing values. This improves the accuracy of the analysis.

[0514] Step 3:

[0515] The server analyzes pre-processed data using artificial intelligence. The input is the cleaned data generated in step 2, and the output is the key factors and their evaluation as a result of the analysis. By applying machine learning algorithms, patterns in the data are identified and factors that differentiate success from failure are extracted.

[0516] Step 4:

[0517] The server generates a plan for future business improvements based on the analysis results obtained. The input is the analysis results from step 3, and the output is a list of improvement plans. Using the generation AI model, specific process changes to improve work efficiency are automatically designed.

[0518] Step 5:

[0519] The terminal visualizes the improvement suggestions sent from the server and presents them to the user. The input is the improvement suggestions created in step 4, and the output is visualized information in a format that is easy for the user to understand visually (e.g., graphs, heatmaps). The terminal utilizes visualization libraries such as Matplotlib to convert the information into a visually understandable format.

[0520] Step 6:

[0521] Users evaluate the proposed improvements and provide feedback. Input is visualization information presented from the device, and output is feedback information. This feedback is returned to the server as data useful for subsequent analysis and improvement cycles.

[0522] 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.

[0523] This invention is a system that collects combat and training data in real time, provides suggestions for improving tactics for the next session based on the collected data, and further achieves more effective tactical feedback by combining it with an emotion engine that recognizes the user's emotions.

[0524] First, the server automatically collects combat data such as location information, communication information, and action logs in real time from battlefields and training environments using multiple sensors. This data is securely stored and organized in a format that allows for appropriate subsequent data processing.

[0525] Next, the data collected by the server is preprocessed and converted into a format suitable for analysis by artificial intelligence. This preprocessing includes data cleansing (removal of duplicates and noise reduction) and format conversion, as well as the integration of time and location information.

[0526] Subsequently, the server analyzes the pre-processed data using an artificial intelligence model. The AI ​​uses machine learning algorithms to identify success and failure factors, referencing past data, and identifies tactical patterns and the effectiveness of actions.

[0527] Based on the analysis results, the server uses AI to automatically generate tactical improvement proposals for the next battle. These proposals are graphically represented using visualization tools and prepared for presentation to the user. For example, suggestions for changing waiting locations or improving communication protocols may be presented.

[0528] In this invention, the terminal utilizes an emotion engine based on the user's visual and audio data to detect the user's emotional state and adjust the display of the analysis results. Specifically, the content and format of the displayed information can be customized considering the user's stress level and concentration level.

[0529] Next, the device provides the user with information drilled down by the emotion engine. The user can visually review the suggested improvements and input feedback and comments through the device that match their emotional state.

[0530] The server receives user feedback, integrates it into the system, and uses it to further improve the accuracy of the artificial intelligence model. This feedback process results in more refined tactical suggestions and an increased success rate for tactics.

[0531] As a concrete example, if a unit is placed in a scenario-specific stress situation during a training scenario, the introduction of an emotion engine makes it possible to provide customized improvement plans based on the psychological state of the unit members. This improves tactical adaptability and dramatically enhances the quality of training.

[0532] The following describes the processing flow.

[0533] Step 1:

[0534] The server collects sensor inputs in real time from the battlefield or training environment, gathering location information, communication logs, and behavioral data. This data is automatically stored in a database and managed without any loss.

[0535] Step 2:

[0536] The server preprocesses the collected data and converts it into an analyzable format. Data cleansing is performed to remove duplicate information and noise, creating a consistent dataset.

[0537] Step 3:

[0538] The server analyzes pre-processed data using an artificial intelligence model to identify factors for success and failure. The analysis employs machine learning algorithms to extract tactical patterns by comparing current data with historical datasets.

[0539] Step 4:

[0540] Based on the server's analysis, the AI ​​generates the next set of tactical improvement suggestions. These suggestions are visualized and converted into heatmaps and recommended action lists.

[0541] Step 5:

[0542] The device uses visual and audio data input from the user's camera and microphone to analyze the user's emotional state with its emotion engine. Based on the detected emotions, it prepares to adjust the type and format of information presented.

[0543] Step 6:

[0544] The device presents visualized improvement suggestions to the user in a tailored format. The information presented is customized according to the user's current psychological state, aiming to reduce stress and facilitate understanding.

[0545] Step 7:

[0546] Users evaluate the proposed improvements and input further opinions and emotional feedback into their devices. This feedback is sent to the server and used as training data to improve the accuracy of future analyses.

[0547] Step 8:

[0548] The server integrates user feedback into the artificial intelligence model, reflecting it in the analysis methods and improvement suggestion generation algorithms, thereby improving the overall accuracy of the system.

[0549] (Example 2)

[0550] 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."

[0551] Conventional tactical improvement systems lacked sufficient real-time data collection and analysis, resulting in limitations in the accuracy and adaptability of improvement proposals. Furthermore, the absence of feedback functions that considered the user's emotional state made it difficult to accept suggestions. Therefore, there is a need for effective and efficient tactical improvement.

[0552] 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.

[0553] In this invention, the server includes means for collecting data from the combat environment in real time, means for preprocessing the collected information and converting it into an analyzable format, and means for analyzing the preprocessed information using machine learning algorithms to identify factors for success and failure. This compensates for the conventional problems of insufficient data processing and lack of feedback, and makes it possible to provide highly accurate tactical improvement proposals.

[0554] A "combat environment" refers to the location or situation in which combat or training takes place, and is the area from which data is collected.

[0555] "Data collection" refers to the process of continuously acquiring information from the environment using sensors and other means.

[0556] "Preprocessing" refers to a series of operations, such as cleansing and format conversion, to prepare collected raw data for analysis.

[0557] A "machine learning algorithm" refers to mathematical models and methods that derive patterns and rules from data and automatically perform identification and prediction.

[0558] "Generative AI" refers to artificial intelligence technology that automatically generates new information and suggestions based on given data and conditions.

[0559] "Emotion recognition technology" refers to technology that analyzes and understands a user's emotional state from their visual and auditory data.

[0560] "Feedback" refers to the evaluations and opinions that users provide to the system, which are used to improve the system and make suggestions for future updates.

[0561] "Visualization" refers to displaying data and information in graphs, charts, and other graphical formats to make them easier for users to understand.

[0562] This invention is a system that supports the tactical improvement process through interaction between a server, a terminal, and a user. The server collects data in real time from the combat environment using multiple sensors. GPS sensors, communication modules, and accelerometers are used, and location information, communication information, and action logs obtained from these sensors are temporarily stored in secure storage.

[0563] The server performs data cleansing and format conversion on the collected data. This prepares the data for analysis by machine learning algorithms. Python is used for data processing, and libraries such as TensorFlow and PyTorch are often used for support.

[0564] The server analyzes the data using machine learning algorithms based on pre-processed data. Referring to historical data, it identifies success and failure factors and identifies the effectiveness of patterns or actions in tactics.

[0565] Subsequently, a generative AI model is used to automatically generate tactical improvement proposals for the next game. The system receives prompts such as, "Based on this data, please generate tactical improvement proposals for the next game. Please consider the user's psychological state and include measures to reduce stress." The generated improvement proposals are graphically represented using visualization tools and presented to the user in an easy-to-understand manner.

[0566] Meanwhile, the device utilizes emotion recognition technology to detect the user's emotional state from their visual and audio data. It analyzes data obtained from the camera and microphone, and dynamically adjusts the amount and layout of information, taking into account how the user perceives the displayed information.

[0567] This system allows users to receive improvement suggestions in a format best suited to their needs, and to incorporate their opinions into the system through the feedback function. This feedback is used to improve tactical suggestions, contributing to the accuracy of future suggestions.

[0568] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0569] Step 1:

[0570] The server collects location information, communication information, and activity logs in real time from various sensors installed in the combat environment. This input data is temporarily stored in storage. GPS sensors, communication modules, and accelerometers are used in this operation, and the information from each sensor is integrated and compiled into a dataset.

[0571] Step 2:

[0572] The server performs data cleansing on the data stored in storage. It uses the collected raw data as input. This process removes duplicate data, eliminates noise, and standardizes the format, outputting cleansed data suitable for analysis. This improves the accuracy of the analysis.

[0573] Step 3:

[0574] The server receives the cleansed data and passes it to an artificial intelligence for analysis. Based on the input data, it uses machine learning algorithms to perform analysis to identify factors for success and failure. Using libraries such as TensorFlow and PyTorch, it identifies tactical patterns and effects and outputs the analysis results.

[0575] Step 4:

[0576] Based on the analysis results, the server uses a generative AI model to generate tactical improvement proposals for the next session. The prompt "Generate tactical improvement proposals for the next session based on this data" is entered, and this generation process outputs the improvement proposals as text. The generated improvement proposals are then graphically represented using a visualization tool.

[0577] Step 5:

[0578] The device utilizes emotion recognition technology when displaying visualized improvement suggestions. It uses the user's visual and auditory data as input to detect the user's emotional state. It analyzes data from the camera and microphone, adjusting the displayed content to consider how the user will perceive the information. The adjusted information is then presented to the user.

[0579] Step 6:

[0580] Users review the suggested improvements displayed on their devices and provide feedback. This feedback is received via the device in the form of comments and ratings, which the system then adapts to and stores in the database.

[0581] Step 7:

[0582] The server continuously collects user feedback and uses it to train its artificial intelligence model. This feedback is processed as data for model improvement, contributing to the increased accuracy of the tactical improvement suggestions generated in subsequent updates. This allows the system to consistently provide more refined suggestions.

[0583] (Application Example 2)

[0584] 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."

[0585] In modern factory environments, improving the efficiency of production operations is crucial, but conventional technologies do not adequately collect real-time data on work processes or provide improvement suggestions that take into account the stress levels of workers. Furthermore, improving work efficiency requires considering the psychological state of workers, but there is a lack of effective systems for this purpose. As a result, not only does work efficiency decline, but problems such as the accumulation of fatigue and stress among workers arise.

[0586] 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.

[0587] In this invention, the server includes means for collecting work data in an industrial environment in real time, means for preprocessing the collected data and converting it into an analyzable format, and means for analyzing the preprocessed data and identifying the factors for success and failure of work efficiency using artificial intelligence. This not only enables concrete suggestions for improving work efficiency, but also allows for feedback that takes into account the psychological aspects of workers through an emotion analysis engine.

[0588] An "industrial environment" refers to the environment in which industrial activities take place, such as factories and manufacturing sites, and is a place where various production processes are carried out.

[0589] "Work data" refers to detailed information related to various production activities in factories and workplaces, and includes data such as location information, communication information, and activity logs.

[0590] "Means of real-time data collection" refers to technologies or methods for acquiring data immediately and continuously from the work site. These methods enable a grasp of the most recent work situation.

[0591] "Preprocessing means" refers to techniques or methods for performing data cleansing or format conversion to prepare collected data into a format suitable for analysis.

[0592] "Analyzable format" refers to a state where data has an optimal structure and format for analysis by artificial intelligence, enabling appropriate analysis.

[0593] "Analysis methods using artificial intelligence" refer to technologies or methods that use machine learning algorithms based on data to identify various patterns and features and pinpoint contributing factors.

[0594] "Factors for success and failure" refers to identifying the elements that influence work efficiency and results, and analyzing the causes that lead to success or failure.

[0595] "Means of automatic generation" refers to a technology or method for automatically creating the next work procedure or improvement plan based on the analysis results of artificial intelligence.

[0596] "Presentation means for visualization and delivery" refers to a technology or method for providing generated improvement proposals to users in an easily understandable visual format.

[0597] An "emotion analysis engine" is a technology or system that detects and analyzes an emotional state from a user's visual and auditory data.

[0598] "Users" refers to people who use the system to perform tasks in an industrial environment.

[0599] "Means of collecting feedback" refers to techniques or methods for systematically obtaining opinions and reactions from users.

[0600] "Using it for learning" refers to improving the accuracy of the model based on the feedback data collected by the artificial intelligence, and using that information to inform future suggestions and analyses.

[0601] "Sensing device" is a general term for equipment or sensors used to detect location information, communication information, and activity logs.

[0602] "Identifying stress levels" refers to the process of determining the user's physical and mental burden through an emotion analysis engine and detecting it as part of the analysis.

[0603] This system is designed to improve work efficiency in industrial environments. The server first collects real-time work data from multiple sensing devices placed throughout the factory. This includes location information, communication information, and activity logs. This data is then pre-processed, including redundancy removal and formatting standardization. Once pre-processed, the data is converted into a format that can be analyzed by artificial intelligence.

[0604] The server uses machine learning frameworks like TensorFlow for data analysis, identifying factors that contribute to the success and failure of work efficiency and production activities. Based on these analysis results, suggestions for future work improvements are automatically generated. A generative AI model is used, and prompts are used to output improvement suggestions in real time. For example, a prompt such as "Refer to past product assembly data and generate suggestions for improving current work efficiency" might be generated.

[0605] The generated improvement suggestions are presented to factory workers via a terminal in a visually easy-to-understand format. The terminal functions as smart glasses and uses an emotion analysis engine such as Hume AI to adjust the information based on the worker's emotional state, particularly stress and concentration levels. The presented improvement suggestions can be reviewed interactively by the user, enabling feedback that generally contributes to improved work efficiency and reduced workload.

[0606] As a concrete example, consider a scenario where efficiency drops during product assembly on a manufacturing line. In this case, the smart glasses display suggested changes to the assembly procedure in real time, smoothing the workflow. Furthermore, if emotional analysis determines that the stress level is high, appropriate relaxation methods are suggested. This improves the quality of the workspace and maintains productivity.

[0607] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0608] Step 1:

[0609] The server collects location information, communication information, and activity logs in real time from various sensing devices installed within the factory. The input for this step is data from the sensing devices, and the output is raw data stored on the server. At this stage, the data from the sensing devices is efficiently aggregated to prepare for subsequent processing.

[0610] Step 2:

[0611] The server performs preprocessing on the collected raw data, including noise reduction and data duplication. The input is the raw data stored in step 1, and the output is a dataset formatted for analysis. Specifically, it unifies the data format and removes redundant information.

[0612] Step 3:

[0613] The server analyzes the preprocessed data using TensorFlow to identify factors contributing to the success and failure of the work efficiency. The input for this step is the dataset formatted in step 2, and the output is the analysis results showing the factors for success and failure. This analysis extracts useful patterns from the data and identifies directions for improvement.

[0614] Step 4:

[0615] The server automatically generates improvement plans for the next work process through a generating AI model based on the analysis results. The input for this step is the analysis results obtained in step 3, and the output is specific improvement plans. At this time, the generating AI model is instructed using the prompt message, "Please refer to past assembly data and generate work efficiency improvement plans."

[0616] Step 5:

[0617] The smart glasses used as a terminal receive improvement suggestions provided by the server and visually present them to the user, the worker. The input is the improvement suggestions generated in step 4, and the output is the improvement suggestions displayed to the worker. In this step, the instructions and procedures necessary to improve work efficiency are immediately presented.

[0618] Step 6:

[0619] A device equipped with an emotion analysis engine detects the user's current emotional state from their facial expressions and voice. The input is real-time visual and audio data from the user, and the output is numerical data representing stress levels and concentration levels. Specifically, the system instantly recognizes when the user is experiencing excessive stress.

[0620] Step 7:

[0621] The terminal adjusts the display of information based on data obtained through emotion analysis, providing feedback tailored to the user's stress level and concentration. Input consists of emotion analysis data obtained in step 6 and improvement suggestions from the server, while output is the adjusted display content. This feedback allows the user to receive suggestions for improving their work procedures at the appropriate time.

[0622] 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.

[0623] 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.

[0624] 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.

[0625] [Fourth Embodiment]

[0626] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0627] 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.

[0628] 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).

[0629] 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.

[0630] 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.

[0631] 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).

[0632] 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.

[0633] 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.

[0634] 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.

[0635] 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.

[0636] 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.

[0637] 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.

[0638] 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".

[0639] This invention provides a system for efficiently and objectively analyzing post-combat data and providing suggestions for future tactical improvements. This system encompasses a series of processes, from data collection, processing, and analysis during and after combat operations to the presentation of improvement suggestions.

[0640] First, the server collects vast amounts of data generated in the battlefield and training environment in real time. By aggregating and securely storing data obtained from various devices, such as location information, communication logs, and weapon usage information, data loss and loss of accuracy are prevented.

[0641] Next, the data collected by the server is preprocessed and converted into a format suitable for advanced analysis. Specifically, data cleansing is performed to remove duplicate data and noise. In addition, data obtained from different devices is converted into a unified format, and the time and location of the data are synchronized.

[0642] Next, the server uses artificial intelligence to analyze the data. During this process, it utilizes machine learning algorithms, referencing past battle data, to identify the factors behind success and failure. This AI analysis determines which tactics are effective and which require improvement.

[0643] Subsequently, the server generates tactical improvement proposals for the next phase based on the analysis results. The generating AI designs specific tactical changes based on the insights gained and presents them in a visualized format. For example, it may suggest the tactical effectiveness of waiting at a specific location or methods for improving communication.

[0644] Next, the device presents the generated improvement suggestions to the user. The suggestions are visually organized and displayed as graphical charts and heatmaps, making them easy for the user to understand intuitively and aiding in quick decision-making.

[0645] Finally, based on the improvement suggestions provided by the user, the system develops actual tactical plans. By providing feedback, the system incorporates that information as learning data for further improvement. This allows the system to continuously learn and optimize its tactics.

[0646] For example, when a unit carries out a specific operation in a training scenario, this system can be used to immediately analyze the results and clearly identify areas for improvement for the next training session. As a result, the unit's response capabilities are strengthened, and more efficient training can be achieved.

[0647] The following describes the processing flow.

[0648] Step 1:

[0649] The server collects data in real time from sensors and devices during combat and automatically stores it in a central database. This data includes location information, communication logs, and time-series information on actions, and the collection process is continuously monitored to prevent data loss.

[0650] Step 2:

[0651] The server preprocesses the collected raw data and converts it into an analyzable format. Specifically, it cleanses the data to remove duplicates and noise. It also unifies data in different formats and synchronizes data between devices.

[0652] Step 3:

[0653] The server feeds pre-processed data into an artificial intelligence model and begins the analysis. The AI ​​uses machine learning algorithms to identify success and failure factors while comparing them with historical data. This analysis process identifies the effectiveness of specific tactical patterns and actions.

[0654] Step 4:

[0655] The server uses AI generation based on the analysis results to automatically generate tactical improvement proposals for the next phase. The generated improvement proposals are converted into a visualized format and include, for example, recommended points for tactical changes and suggestions for optimizing positioning.

[0656] Step 5:

[0657] The device presents users with visually organized improvement suggestions in real time. These suggestions are displayed as graphical charts and heatmaps, making it easy for users to intuitively understand the analysis results.

[0658] Step 6:

[0659] The user evaluates the suggested improvements and inputs feedback into the terminal to incorporate them into the next tactical plan. This feedback is then sent to the server to further improve the tactics.

[0660] Step 7:

[0661] The server receives user feedback and incorporates it into the AI ​​model as training data. This helps improve the accuracy of the analysis and generate suggestions for improvement for the next analysis. The AI ​​incorporates the feedback and derives new insights in the next data analysis.

[0662] (Example 1)

[0663] 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".

[0664] An efficient system is needed to objectively and quickly evaluate the effectiveness of tactical actions in the field and use that information to improve future actions. Traditional methods rely heavily on manual data collection and analysis, which is time-consuming and labor-intensive, and can be influenced by subjective judgments. As a result, tactical improvements are often insufficient, and rapid decision-making is difficult.

[0665] 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.

[0666] In this invention, the server includes means for aggregating information in real time at the field, means for organizing the aggregated information and converting it into an analyzable data format, and analytical means utilizing machine learning to analyze the organized data and identify specific factors. This makes it possible to efficiently process and analyze the vast amount of data obtained from the field and to scientifically and objectively improve the next tactical plan.

[0667] "Methods for collecting information in real time at the site" refers to technical methods for collecting and centrally managing data transmitted from the field.

[0668] "Means for organizing aggregated information and converting it into an analyzable data format" refers to methods for appropriately editing collected raw data, converting it into a unified format, and facilitating subsequent analysis.

[0669] "Analysis methods utilizing machine learning" are techniques that use algorithms to find patterns and causal relationships based on past data, and to make predictions and judgments.

[0670] "A means of automatically generating improvement suggestions for the next operation" refers to a system that utilizes analysis results to mechanically generate specific suggestions for efficiently improving the next action plan.

[0671] "Visual presentation methods that provide information to users" refer to technologies for displaying generated information or proposals in a way that is easy for humans to understand, such as using graphs and charts to convey information intuitively.

[0672] This invention is an information processing system aimed at improving tactical actions, and is composed primarily of a server, terminals, and users.

[0673] The server aggregates a wide variety of data collected on-site in real time. This process includes the immediate collection of data such as location information, communication logs, and activity records. The server securely stores this data in a database, which serves as the foundation for subsequent processing.

[0674] After data aggregation, the server organizes this information into a parseable format. For hardware, a server with a high-spec processor and sufficient storage capacity is recommended. Data formats such as CSV and JSON are used for standardization. A cleansing process removes noise and duplicate data, converting the data into a consistent format.

[0675] Next, the server performs machine learning analysis on the organized data. This analysis utilizes deep learning frameworks such as TensorFlow and PyTorch, with generative AI models identifying success factors and areas for improvement. The machine learning algorithms learn from past data and recognize similar patterns, enabling highly accurate analysis.

[0676] The server then automatically generates suggestions for improving the next operation based on the analysis results. These suggestions are visually displayed on the dashboard, making it easy to review tactical improvements. These suggestions may include tactical measures such as changes to troop deployment or improvements to communication protocols.

[0677] Once the proposal is complete, the device visualizes and presents it to the user. Using graphs, heatmaps, and other visual aids, the device provides intuitive understanding and helps users make quick decisions.

[0678] Finally, the user develops a plan of action for the next step based on the suggestions provided. The user's feedback is incorporated into the system and used as a reference for future data analysis. This allows the system to provide more appropriate suggestions over time.

[0679] As a concrete example, in a training scenario, evaluating a specific operation and immediately identifying problems allows for appropriate improvements to be made in the next training session. An example of a prompt message is, "Analyze the results of the operation in the training scenario and propose tactical improvements for the next session."

[0680] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0681] Step 1:

[0682] The server receives raw data transmitted from the field. This data includes location information, communication logs, and activity records, collected from various sensors. The server stores this information in a database. The input here is raw data from the field, and the output is unprocessed data before it is organized.

[0683] Step 2:

[0684] The server performs data cleansing on the collected raw data. This process removes noise and duplicate entries from the data and selects valid data. The input is raw data stored in the database, and the output is clean data with noise removed and a unified format.

[0685] Step 3:

[0686] The server converts clean data into an analyzable format. It performs format conversion, integrates data from different devices, and adjusts the time series. CSV and JSON formats are used here. The input is clean data, and the output is in a data format suitable for analysis.

[0687] Step 4:

[0688] The server applies machine learning models to data suitable for analysis. Using deep learning frameworks such as TensorFlow, it analyzes the factors of success and failure while referencing historical data. The input is data in a format suitable for analysis, and the output is the analysis result (identification of success and failure factors).

[0689] Step 5:

[0690] The server automatically generates improvement plans for the next operation based on the analysis results obtained. It uses a generating AI model to design specific suggestions and displays them in a dashboard format. The input is the analysis results, and the output is the generated improvement suggestions.

[0691] Step 6:

[0692] The terminal visualizes and presents the generated improvement suggestions to the user. Using a GUI, the suggestions are visually represented with graphs and heatmaps, making them easy for the user to understand intuitively. The input is the generated improvement suggestions, and the output is the visualized suggestion information.

[0693] Step 7:

[0694] Based on the suggested improvements, users form their next action plan. Users provide feedback to the system, which is then used for future data analysis. The input here is visualized suggestion information, and the output is feedback information.

[0695] (Application Example 1)

[0696] 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".

[0697] Improving operational efficiency in logistics centers is a critical challenge. Traditional methods require significant time and effort to understand operational status and generate improvement plans, making immediate optimization difficult. To address this, real-time data analysis and immediate proposal of improvements are essential.

[0698] 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.

[0699] In this invention, the server includes means for collecting operational data in real time, means for preprocessing the collected data and converting it into an analyzable format, and means for analyzing the preprocessed data and identifying the factors for success and failure of operations using artificial intelligence. This makes it possible to analyze operational data in a logistics center in real time and immediately generate and present improvement proposals.

[0700] "Business data" refers to all information related to operations that occur within the logistics center, and this includes location information, communication information, operation logs, etc.

[0701] "Means of real-time data collection" refers to technologies and devices for instantly recording data generated during a task.

[0702] "Means of preprocessing and converting into an analyzable format" refers to techniques for shaping raw data into an appropriate format and processing it to a state suitable for analysis.

[0703] "Analysis methods using artificial intelligence" refer to methods that employ machine learning algorithms to learn from past data and evaluate current data based on the insights gained from that learning.

[0704] "Means of presenting improvement proposals to users in a visual form, such as diagrams and graphs, are technologies and devices that display analysis results in a visual form, allowing users to understand them intuitively."

[0705] The system that realizes this invention collects and analyzes operational data in real time at a logistics center and immediately provides improvement suggestions.

[0706] The server collects operational data generated from various devices within the logistics center in real time. This includes location information, communication information, and operation logs. The server then preprocesses the collected data and converts it into a format suitable for analysis. This preprocessing includes removing duplicate data and noise.

[0707] Next, the server uses artificial intelligence to analyze the data. This includes analytical methods that use machine learning algorithms that learn from past data to identify factors influencing the success and failure of operations from current data. Specifically, it uses tools such as Python and scikit-learn to perform dimensionality reduction using PCA and KMeans clustering.

[0708] The improvement suggestions generated by the server are visualized and provided to the terminal. The terminal uses visualization libraries such as Matplotlib to present the improvement suggestions to the user as graphs and heatmaps. This allows the user to gain visual insights into business processes and make immediate decisions.

[0709] As a concrete example, one day at a logistics center, it was discovered that handling a particular product was taking an excessive amount of time. The server then generated improvement suggestions, including a reassessment of the product's layout, and presented them to the user via a terminal. As a result, the work time was reduced by changing the product's placement.

[0710] An example of a prompt message is, "Generate process improvement proposals based on daily work data from the logistics center." In this way, the efficiency of logistics operations can be improved.

[0711] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0712] Step 1:

[0713] The server collects operational data in real time from various devices within the logistics center. Inputs include location information and operation logs transmitted from the devices, while outputs are collections of raw data. Data is sent directly to the server from sensors and terminals via wireless or wired communication.

[0714] Step 2:

[0715] The server preprocesses the collected raw data and converts it into a format suitable for analysis. The input is the raw data obtained in step 1, and the output is the cleaned data. Specifically, the data is shaped by removing duplicates, removing noise, and imputing missing values. This improves the accuracy of the analysis.

[0716] Step 3:

[0717] The server analyzes pre-processed data using artificial intelligence. The input is the cleaned data generated in step 2, and the output is the key factors and their evaluation as a result of the analysis. By applying machine learning algorithms, patterns in the data are identified and factors that differentiate success from failure are extracted.

[0718] Step 4:

[0719] The server generates a plan for future business improvements based on the analysis results obtained. The input is the analysis results from step 3, and the output is a list of improvement plans. Using the generation AI model, specific process changes to improve work efficiency are automatically designed.

[0720] Step 5:

[0721] The terminal visualizes the improvement suggestions sent from the server and presents them to the user. The input is the improvement suggestions created in step 4, and the output is visualized information in a format that is easy for the user to understand visually (e.g., graphs, heatmaps). The terminal utilizes visualization libraries such as Matplotlib to convert the information into a visually understandable format.

[0722] Step 6:

[0723] Users evaluate the proposed improvements and provide feedback. Input is visualization information presented from the device, and output is feedback information. This feedback is returned to the server as data useful for subsequent analysis and improvement cycles.

[0724] 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.

[0725] This invention is a system that collects combat and training data in real time, provides suggestions for improving tactics for the next session based on the collected data, and further achieves more effective tactical feedback by combining it with an emotion engine that recognizes the user's emotions.

[0726] First, the server automatically collects combat data such as location information, communication information, and action logs in real time from battlefields and training environments using multiple sensors. This data is securely stored and organized in a format that allows for appropriate subsequent data processing.

[0727] Next, the data collected by the server is preprocessed and converted into a format suitable for analysis by artificial intelligence. This preprocessing includes data cleansing (removal of duplicates and noise reduction) and format conversion, as well as the integration of time and location information.

[0728] Subsequently, the server analyzes the pre-processed data using an artificial intelligence model. The AI ​​uses machine learning algorithms to identify success and failure factors, referencing past data, and identifies tactical patterns and the effectiveness of actions.

[0729] Based on the analysis results, the server uses AI to automatically generate tactical improvement proposals for the next battle. These proposals are graphically represented using visualization tools and prepared for presentation to the user. For example, suggestions for changing waiting locations or improving communication protocols may be presented.

[0730] In this invention, the terminal utilizes an emotion engine based on the user's visual and audio data to detect the user's emotional state and adjust the display of the analysis results. Specifically, the content and format of the displayed information can be customized considering the user's stress level and concentration level.

[0731] Next, the device provides the user with information drilled down by the emotion engine. The user can visually review the suggested improvements and input feedback and comments through the device that match their emotional state.

[0732] The server receives user feedback, integrates it into the system, and uses it to further improve the accuracy of the artificial intelligence model. This feedback process results in more refined tactical suggestions and an increased success rate for tactics.

[0733] As a concrete example, if a unit is placed in a scenario-specific stress situation during a training scenario, the introduction of an emotion engine makes it possible to provide customized improvement plans based on the psychological state of the unit members. This improves tactical adaptability and dramatically enhances the quality of training.

[0734] The following describes the processing flow.

[0735] Step 1:

[0736] The server collects sensor inputs in real time from the battlefield or training environment, gathering location information, communication logs, and behavioral data. This data is automatically stored in a database and managed without any loss.

[0737] Step 2:

[0738] The server preprocesses the collected data and converts it into an analyzable format. Data cleansing is performed to remove duplicate information and noise, creating a consistent dataset.

[0739] Step 3:

[0740] The server analyzes pre-processed data using an artificial intelligence model to identify factors for success and failure. The analysis employs machine learning algorithms to extract tactical patterns by comparing current data with historical datasets.

[0741] Step 4:

[0742] Based on the server's analysis, the AI ​​generates the next set of tactical improvement suggestions. These suggestions are visualized and converted into heatmaps and recommended action lists.

[0743] Step 5:

[0744] The device uses visual and audio data input from the user's camera and microphone to analyze the user's emotional state with its emotion engine. Based on the detected emotions, it prepares to adjust the type and format of information presented.

[0745] Step 6:

[0746] The device presents visualized improvement suggestions to the user in a tailored format. The information presented is customized according to the user's current psychological state, aiming to reduce stress and facilitate understanding.

[0747] Step 7:

[0748] Users evaluate the proposed improvements and input further opinions and emotional feedback into their devices. This feedback is sent to the server and used as training data to improve the accuracy of future analyses.

[0749] Step 8:

[0750] The server integrates user feedback into the artificial intelligence model, reflecting it in the analysis methods and improvement suggestion generation algorithms, thereby improving the overall accuracy of the system.

[0751] (Example 2)

[0752] 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".

[0753] Conventional tactical improvement systems lacked sufficient real-time data collection and analysis, resulting in limitations in the accuracy and adaptability of improvement proposals. Furthermore, the absence of feedback functions that considered the user's emotional state made it difficult to accept suggestions. Therefore, there is a need for effective and efficient tactical improvement.

[0754] 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.

[0755] In this invention, the server includes means for collecting data from the combat environment in real time, means for preprocessing the collected information and converting it into an analyzable format, and means for analyzing the preprocessed information using machine learning algorithms to identify factors for success and failure. This compensates for the conventional problems of insufficient data processing and lack of feedback, and makes it possible to provide highly accurate tactical improvement proposals.

[0756] A "combat environment" refers to the location or situation in which combat or training takes place, and is the area from which data is collected.

[0757] "Data collection" refers to the process of continuously acquiring information from the environment using sensors and other means.

[0758] "Preprocessing" refers to a series of operations, such as cleansing and format conversion, to prepare collected raw data for analysis.

[0759] A "machine learning algorithm" refers to mathematical models and methods that derive patterns and rules from data and automatically perform identification and prediction.

[0760] "Generative AI" refers to artificial intelligence technology that automatically generates new information and suggestions based on given data and conditions.

[0761] "Emotion recognition technology" refers to technology that analyzes and understands a user's emotional state from their visual and auditory data.

[0762] "Feedback" refers to the evaluations and opinions that users provide to the system, which are used to improve the system and make suggestions for future updates.

[0763] "Visualization" refers to displaying data and information in graphs, charts, and other graphical formats to make them easier for users to understand.

[0764] This invention is a system that supports the tactical improvement process through interaction between a server, a terminal, and a user. The server collects data in real time from the combat environment using multiple sensors. GPS sensors, communication modules, and accelerometers are used, and location information, communication information, and action logs obtained from these sensors are temporarily stored in secure storage.

[0765] The server performs data cleansing and format conversion on the collected data. This prepares the data for analysis by machine learning algorithms. Python is used for data processing, and libraries such as TensorFlow and PyTorch are often used for support.

[0766] The server analyzes the data using machine learning algorithms based on pre-processed data. Referring to historical data, it identifies success and failure factors and identifies the effectiveness of patterns or actions in tactics.

[0767] Subsequently, a generative AI model is used to automatically generate tactical improvement proposals for the next game. The system receives prompts such as, "Based on this data, please generate tactical improvement proposals for the next game. Please consider the user's psychological state and include measures to reduce stress." The generated improvement proposals are graphically represented using visualization tools and presented to the user in an easy-to-understand manner.

[0768] Meanwhile, the device utilizes emotion recognition technology to detect the user's emotional state from their visual and audio data. It analyzes data obtained from the camera and microphone, and dynamically adjusts the amount and layout of information, taking into account how the user perceives the displayed information.

[0769] This system allows users to receive improvement suggestions in a format best suited to their needs, and to incorporate their opinions into the system through the feedback function. This feedback is used to improve tactical suggestions, contributing to the accuracy of future suggestions.

[0770] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0771] Step 1:

[0772] The server collects location information, communication information, and activity logs in real time from various sensors installed in the combat environment. This input data is temporarily stored in storage. GPS sensors, communication modules, and accelerometers are used in this operation, and the information from each sensor is integrated and compiled into a dataset.

[0773] Step 2:

[0774] The server performs data cleansing on the data stored in storage. It uses the collected raw data as input. This process removes duplicate data, eliminates noise, and standardizes the format, outputting cleansed data suitable for analysis. This improves the accuracy of the analysis.

[0775] Step 3:

[0776] The server receives the cleansed data and passes it to an artificial intelligence for analysis. Based on the input data, it uses machine learning algorithms to perform analysis to identify factors for success and failure. Using libraries such as TensorFlow and PyTorch, it identifies tactical patterns and effects and outputs the analysis results.

[0777] Step 4:

[0778] Based on the analysis results, the server uses a generative AI model to generate tactical improvement proposals for the next session. The prompt "Generate tactical improvement proposals for the next session based on this data" is entered, and this generation process outputs the improvement proposals as text. The generated improvement proposals are then graphically represented using a visualization tool.

[0779] Step 5:

[0780] The device utilizes emotion recognition technology when displaying visualized improvement suggestions. It uses the user's visual and auditory data as input to detect the user's emotional state. It analyzes data from the camera and microphone, adjusting the displayed content to consider how the user will perceive the information. The adjusted information is then presented to the user.

[0781] Step 6:

[0782] Users review the suggested improvements displayed on their devices and provide feedback. This feedback is received via the device in the form of comments and ratings, which the system then adapts to and stores in the database.

[0783] Step 7:

[0784] The server continuously collects user feedback and uses it to train its artificial intelligence model. This feedback is processed as data for model improvement, contributing to the increased accuracy of the tactical improvement suggestions generated in subsequent updates. This allows the system to consistently provide more refined suggestions.

[0785] (Application Example 2)

[0786] 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".

[0787] In modern factory environments, improving the efficiency of production operations is crucial, but conventional technologies do not adequately collect real-time data on work processes or provide improvement suggestions that take into account the stress levels of workers. Furthermore, improving work efficiency requires considering the psychological state of workers, but there is a lack of effective systems for this purpose. As a result, not only does work efficiency decline, but problems such as the accumulation of fatigue and stress among workers arise.

[0788] 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.

[0789] In this invention, the server includes means for collecting work data in an industrial environment in real time, means for preprocessing the collected data and converting it into an analyzable format, and means for analyzing the preprocessed data and identifying the factors for success and failure of work efficiency using artificial intelligence. This not only enables concrete suggestions for improving work efficiency, but also allows for feedback that takes into account the psychological aspects of workers through an emotion analysis engine.

[0790] An "industrial environment" refers to the environment in which industrial activities take place, such as factories and manufacturing sites, and is a place where various production processes are carried out.

[0791] "Work data" refers to detailed information related to various production activities in factories and workplaces, and includes data such as location information, communication information, and activity logs.

[0792] "Means of real-time data collection" refers to technologies or methods for acquiring data immediately and continuously from the work site. These methods enable a grasp of the most recent work situation.

[0793] "Preprocessing means" refers to techniques or methods for performing data cleansing or format conversion to prepare collected data into a format suitable for analysis.

[0794] "Analyzable format" refers to a state where data has an optimal structure and format for analysis by artificial intelligence, enabling appropriate analysis.

[0795] "Analysis methods using artificial intelligence" refer to technologies or methods that use machine learning algorithms based on data to identify various patterns and features and pinpoint contributing factors.

[0796] "Factors for success and failure" refers to identifying the elements that influence work efficiency and results, and analyzing the causes that lead to success or failure.

[0797] "Means of automatic generation" refers to a technology or method for automatically creating the next work procedure or improvement plan based on the analysis results of artificial intelligence.

[0798] "Presentation means for visualization and delivery" refers to a technology or method for providing generated improvement proposals to users in an easily understandable visual format.

[0799] An "emotion analysis engine" is a technology or system that detects and analyzes an emotional state from a user's visual and auditory data.

[0800] "Users" refers to people who use the system to perform tasks in an industrial environment.

[0801] "Means of collecting feedback" refers to techniques or methods for systematically obtaining opinions and reactions from users.

[0802] "Using it for learning" refers to improving the accuracy of the model based on the feedback data collected by the artificial intelligence, and using that information to inform future suggestions and analyses.

[0803] "Sensing device" is a general term for equipment or sensors used to detect location information, communication information, and activity logs.

[0804] "Identifying stress levels" refers to the process of determining the user's physical and mental burden through an emotion analysis engine and detecting it as part of the analysis.

[0805] This system is designed to improve work efficiency in industrial environments. The server first collects real-time work data from multiple sensing devices placed throughout the factory. This includes location information, communication information, and activity logs. This data is then pre-processed, including redundancy removal and formatting standardization. Once pre-processed, the data is converted into a format that can be analyzed by artificial intelligence.

[0806] The server uses machine learning frameworks like TensorFlow for data analysis, identifying factors that contribute to the success and failure of work efficiency and production activities. Based on these analysis results, suggestions for future work improvements are automatically generated. A generative AI model is used, and prompts are used to output improvement suggestions in real time. For example, a prompt such as "Refer to past product assembly data and generate suggestions for improving current work efficiency" might be generated.

[0807] The generated improvement suggestions are presented to factory workers via a terminal in a visually easy-to-understand format. The terminal functions as smart glasses and uses an emotion analysis engine such as Hume AI to adjust the information based on the worker's emotional state, particularly stress and concentration levels. The presented improvement suggestions can be reviewed interactively by the user, enabling feedback that generally contributes to improved work efficiency and reduced workload.

[0808] As a concrete example, consider a scenario where efficiency drops during product assembly on a manufacturing line. In this case, the smart glasses display suggested changes to the assembly procedure in real time, smoothing the workflow. Furthermore, if emotional analysis determines that the stress level is high, appropriate relaxation methods are suggested. This improves the quality of the workspace and maintains productivity.

[0809] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0810] Step 1:

[0811] The server collects location information, communication information, and activity logs in real time from various sensing devices installed within the factory. The input for this step is data from the sensing devices, and the output is raw data stored on the server. At this stage, the data from the sensing devices is efficiently aggregated to prepare for subsequent processing.

[0812] Step 2:

[0813] The server performs preprocessing on the collected raw data, including noise reduction and data duplication. The input is the raw data stored in step 1, and the output is a dataset formatted for analysis. Specifically, it unifies the data format and removes redundant information.

[0814] Step 3:

[0815] The server analyzes the preprocessed data using TensorFlow to identify factors contributing to the success and failure of the work efficiency. The input for this step is the dataset formatted in step 2, and the output is the analysis results showing the factors for success and failure. This analysis extracts useful patterns from the data and identifies directions for improvement.

[0816] Step 4:

[0817] The server automatically generates improvement plans for the next work process through a generating AI model based on the analysis results. The input for this step is the analysis results obtained in step 3, and the output is specific improvement plans. At this time, the generating AI model is instructed using the prompt message, "Please refer to past assembly data and generate work efficiency improvement plans."

[0818] Step 5:

[0819] The smart glasses used as a terminal receive improvement suggestions provided by the server and visually present them to the user, the worker. The input is the improvement suggestions generated in step 4, and the output is the improvement suggestions displayed to the worker. In this step, the instructions and procedures necessary to improve work efficiency are immediately presented.

[0820] Step 6:

[0821] A device equipped with an emotion analysis engine detects the user's current emotional state from their facial expressions and voice. The input is real-time visual and audio data from the user, and the output is numerical data representing stress levels and concentration levels. Specifically, the system instantly recognizes when the user is experiencing excessive stress.

[0822] Step 7:

[0823] The terminal adjusts the display of information based on data obtained through emotion analysis, providing feedback tailored to the user's stress level and concentration. Input consists of emotion analysis data obtained in step 6 and improvement suggestions from the server, while output is the adjusted display content. This feedback allows the user to receive suggestions for improving their work procedures at the appropriate time.

[0824] 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.

[0825] 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.

[0826] 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.

[0827] 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.

[0828] 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.

[0829] 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.

[0830] 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.

[0831] 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.

[0832] 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."

[0833] 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.

[0834] 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.

[0835] 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.

[0836] 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.

[0837] 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.

[0838] 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.

[0839] 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.

[0840] 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.

[0841] 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.

[0842] 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.

[0843] 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.

[0844] 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.

[0845] The following is further disclosed regarding the embodiments described above.

[0846] (Claim 1)

[0847] A means of collecting combat data in real time,

[0848] A means for preprocessing the collected data and converting it into an analyzable format,

[0849] An AI-powered analytical method for analyzing pre-processed data and identifying factors for success and failure,

[0850] A means to automatically generate next tactical improvement plans based on the analysis results,

[0851] A means of visualizing the generated improvement proposals and providing them to the user,

[0852] A system that includes this.

[0853] (Claim 2)

[0854] The system according to claim 1, wherein feedback is collected from users based on the visualized improvement proposals, and the artificial intelligence uses that feedback for learning.

[0855] (Claim 3)

[0856] The system according to claim 1, which, in collecting combat data, automatically acquires data including location information, communication information, and action logs obtained from multiple sensors used on the battlefield.

[0857] "Example 1"

[0858] (Claim 1)

[0859] A means of collecting information in real time on-site,

[0860] A means of organizing aggregated information and converting it into an analyzable data format,

[0861] Analytical methods that utilize machine learning to analyze organized data and identify specific factors,

[0862] A means to automatically generate improvement suggestions for the next strategy based on the analysis results,

[0863] A means of presenting the generated proposals visually and providing them to the user,

[0864] A system that includes this.

[0865] (Claim 2)

[0866] The system according to claim 1, which collects opinions from users based on the visually represented proposal and uses those opinions for learning in the machine learning.

[0867] (Claim 3)

[0868] The system according to claim 1, which, in aggregating the aforementioned information, automatically acquires information including location data, communication data, and activity records obtained from multiple sensors used on site.

[0869] "Application Example 1"

[0870] (Claim 1)

[0871] Means for collecting business data in real time,

[0872] A means for preprocessing the collected data and converting it into an analyzable format,

[0873] An AI-powered analytical tool for analyzing pre-processed data to identify factors in business success and failure,

[0874] A means to automatically generate next business improvement plans based on the analysis results,

[0875] A means of visualizing the generated improvement proposals and providing them to users,

[0876] A system that includes this.

[0877] (Claim 2)

[0878] The system according to claim 1, wherein feedback is collected from users based on the visualized improvement proposals, and the artificial intelligence uses that feedback for learning.

[0879] (Claim 3)

[0880] The system according to claim 1, which, in collecting the aforementioned business data, automatically acquires data including location information, communication information, and operation logs obtained from multiple devices used within the facility.

[0881] "Example 2 of combining an emotion engine"

[0882] (Claim 1)

[0883] A means of collecting data from the combat environment in real time,

[0884] A means for preprocessing the collected information and converting it into an analyzable format,

[0885] Based on preprocessed information, an analytical method using machine learning algorithms to identify factors for success and failure,

[0886] A generation method using a generative AI that generates tactical improvement proposals for the next round based on the analysis results,

[0887] A means of presenting the generated improvement proposals visually and providing them to the user,

[0888] A means for detecting the user's emotional state using emotion recognition technology and adjusting the presented content accordingly,

[0889] A system that includes this.

[0890] (Claim 2)

[0891] The system according to claim 1, wherein feedback is collected from users based on the visually represented improvement proposals, and the machine learning algorithm uses that feedback for learning.

[0892] (Claim 3)

[0893] The system according to claim 1, which, in collecting information in the aforementioned combat environment, automatically acquires data including location information, communication information, and action logs obtained from multiple sensors used in the environment.

[0894] "Application example 2 when combining with an emotional engine"

[0895] (Claim 1)

[0896] A means of collecting work data in an industrial environment in real time,

[0897] A means for preprocessing the collected data and converting it into an analyzable format,

[0898] An AI-based analytical method for analyzing pre-processed data and identifying factors contributing to the success and failure of work efficiency,

[0899] A means to automatically generate next work improvement plans based on the analysis results,

[0900] A means of visualizing the generated improvement proposals and providing them to the user,

[0901] A means for detecting the user's emotional state using an emotion analysis engine and adjusting the presented information based on that state,

[0902] A system that includes this.

[0903] (Claim 2)

[0904] The system according to claim 1, wherein feedback is collected from the user based on the visualized improvement proposal, and the artificial intelligence uses that feedback for learning.

[0905] (Claim 3)

[0906] The system according to claim 1, which, in collecting industrial data, automatically acquires data including location information, communication information, and behavioral logs obtained from multiple sensing devices used in an industrial environment, and further identifies the user's stress state based on emotion analysis. [Explanation of symbols]

[0907] 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 collecting combat data in real time, A means for preprocessing the collected data and converting it into an analyzable format, An AI-powered analytical method for analyzing pre-processed data and identifying factors for success and failure, A means to automatically generate next tactical improvement plans based on the analysis results, A means of visualizing the generated improvement proposals and providing them to the user, A system that includes this.

2. The system according to claim 1, wherein feedback is collected from users based on the visualized improvement proposals, and the artificial intelligence uses that feedback for learning.

3. The system according to claim 1, which, in collecting combat data, automatically acquires data including location information, communication information, and action logs obtained from multiple sensors used on the battlefield.

Images