system

Abstract

We provide the system. [Solution] Means for collecting data, Means for performing pre-processing, Methods for analyzing data using predictive artificial intelligence, A means of generating simulation results using generative artificial intelligence, A means of interactively changing the scenario, A means of determining the optimal scenario and creating an execution plan, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance 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] In modern society, there are complex social issues such as population decline problems, disaster countermeasures, and food crises. In response to these issues, it is difficult to formulate effective countermeasures because conventional approaches rely on a single data source or prediction model. There is also a problem that scenario prediction and generation of countermeasure plans are static and cannot respond to changes in the situation in real time. Therefore, there is a need for a system that integrates multiple data sources and dynamically adapts using advanced artificial intelligence technology.

Means for Solving the Problems

[0005] This invention provides a system that collects data from multiple data sources, performs preprocessing, analyzes the data using predictive artificial intelligence, and generates simulation results using generative artificial intelligence. Furthermore, it includes means for users to interactively change scenarios, enabling dynamic and adaptive responses to social issues by determining the optimal scenario and creating an execution plan. Specifically, it collects data via APIs or queries from databases, achieves highly accurate predictions by using multiple predictive models, and generates multiple countermeasures. This allows users to take the optimal measures in real time according to changing circumstances.

[0006] "Data collection means" refers to the function of acquiring necessary data from various information sources.

[0007] "Preprocessing means" refers to functions that clean, normalize, and impute missing values ​​in the collected data, preparing it into an analyzable format.

[0008] "Predictive artificial intelligence tools" are functions that use machine learning and statistical models to predict future scenarios from collected data.

[0009] "Generative artificial intelligence means" refers to functions that use generative adversarial networks (GANs) or natural language generation (NLGs) to generate simulation results such as text and images.

[0010] "An interactive way to change the scenario" refers to a function that allows users to provide real-time feedback to the system and dynamically modify the scenario and proposed solutions.

[0011] "Means for determining the optimal scenario and creating an execution plan" refers to a function that selects the most effective scenario based on user input or modified scenarios and formulates a concrete execution plan. [Brief explanation of the drawing]

[0012] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0013] 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.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, a tagged 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.

[0016] In the following embodiments, a tagged RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, a tagged 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.

[0018] 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).

[0019] 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."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] 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.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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".

[0033] The system according to the present invention proposes optimal solutions to social issues through data collection, preprocessing, prediction, generation, and interactive modification of scenarios. This system aims to efficiently and effectively address challenges faced by national and local governments, municipalities, and corporations (e.g., population decline, disaster countermeasures, food crises, etc.).

[0034] System Overview

[0035] 1. Data Collection

[0036] The server accesses various data sources provided by national and local governments, municipalities, and corporations to collect the necessary big data. This data collection includes using APIs and querying databases. Examples of data that may be collected include demographic data, disaster risk data, and agricultural production data.

[0037] 2. Preprocessing

[0038] The server cleans the collected data, imputing outliers and missing values. It also normalizes the data, converting each data point into a unified format that is easy to analyze.

[0039] 3. Analysis using predictive AI

[0040] The server uses a trained predictive artificial intelligence model to forecast future scenarios based on collected data. For example, it estimates population decline, predicts food production, and assesses disaster risk. Furthermore, it generates proposed countermeasures based on these predictions and evaluates their effectiveness.

[0041] 4. Simulation using generative AI

[0042] The server uses a generative artificial intelligence model to visually represent prediction results. For example, it can generate simulated images of future cityscapes and detailed text reports. This allows users to visualize concrete scenarios.

[0043] 5. User-initiated interactive scenario changes

[0044] Users can view the provided simulation results on their devices and modify the scenarios and proposed solutions as needed. For example, they might expand the scale of urban agriculture or change the focus of disaster countermeasures. The changes made by the user are sent to the server, where the analysis and simulation are performed again.

[0045] 6. Determining and Implementing the Optimal Scenario

[0046] Based on user feedback, the server re-analyzes and generates data to propose the optimal solution. Based on this optimal solution, a concrete implementation plan is formulated and provided to the user. This implementation plan includes specific action items, schedules, resource allocation, etc.

[0047] Specific example

[0048] Predicting and countermeasures for food crises

[0049] 1. Data Collection

[0050] The server collects agricultural production data, weather data, and transportation infrastructure data. Data collection utilizes various APIs and queries from databases.

[0051] 2. Preprocessing

[0052] The server cleans and normalizes the collected data, preparing it for analysis. Missing data is appropriately imputed.

[0053] 3. Analysis using predictive AI

[0054] The server uses a trained predictive model to forecast crop yields for the next year. Based on this forecast, it calculates the probability of food shortages occurring and generates several proposed countermeasures (e.g., imports from other regions, promotion of urban agriculture, etc.).

[0055] 4. Simulation using generative AI

[0056] The server uses generative AI to visualize the urban landscape after the introduction of urban agriculture. It also generates a detailed text report of proposed countermeasures.

[0057] 5. User-initiated interactive scenario changes

[0058] The user reviews the simulation results provided on their device and adjusts the scale of urban agriculture and import plans. The modified scenario is then sent to the server.

[0059] 6. Determining and Implementing the Optimal Scenario

[0060] The server re-analyzes the correction scenario and provides the user with the optimal solution. The user then determines the final scenario, develops a specific implementation plan, and executes it as a project.

[0061] This system enables a dynamic and adaptive approach to addressing social challenges by integrating multiple data collection methods and advanced artificial intelligence technologies.

[0062] The following describes the processing flow.

[0063] Step 1: Data Collection

[0064] The server collects various types of data provided by national, local, municipal, and corporate entities using APIs and database queries. This data includes demographic data, disaster risk data, and agricultural production data.

[0065] Step 2: Data Preprocessing

[0066] The server cleans the collected raw data. Specifically, it detects missing or outlier values ​​and performs interpolation or removal as needed.

[0067] The server standardizes and normalizes the format of the cleaned data, ensuring consistency between data points.

[0068] Step 3: Training the AI ​​model

[0069] The server uses normalized data to train predictive artificial intelligence models. For example, it uses regression models for population forecasting and time-series models for disaster risk prediction.

[0070] The server evaluates and optimizes the model's accuracy using techniques such as cross-validation.

[0071] Step 4: Generating Prediction Scenarios

[0072] The server uses a pre-trained predictive artificial intelligence model to forecast future scenarios. For example, it can predict the rate of population decline, the risk of disasters, and food production for the next year.

[0073] The server generates proposed countermeasures based on the prediction results and evaluates the effectiveness and risks of each.

[0074] Step 5: Simulation using generative AI

[0075] The server uses generative artificial intelligence (image generation AI, language generation AI) to represent predictive scenarios visually and as text. For example, it generates simulated images of urban landscapes after the introduction of urban agriculture and detailed reports.

[0076] Step 6: Provide simulation results

[0077] The server provides the user with the generated simulation results. The user views these results on their terminal and evaluates the effectiveness of the scenarios and proposed solutions.

[0078] Step 7: User changes scenario

[0079] Based on the simulation results, users can modify scenarios and proposed countermeasures on their devices. For example, they might expand the scale of urban agriculture or change the focus of disaster countermeasures.

[0080] The scenario modified by the user is sent from the terminal to the server.

[0081] Step 8: Reanalysis and resimulation

[0082] The server then uses predictive and generative AI to perform analysis and simulation again based on the revised scenario submitted by the user.

[0083] The server generates new simulation results and provides them to the user.

[0084] Step 9: Determining the Optimal Scenario

[0085] The user views the regenerated simulation results and determines the optimal scenario.

[0086] The server creates a specific execution plan based on the optimal scenario. This includes task details, schedule, and resource allocation.

[0087] Step 10: Implement the action plan

[0088] Based on the decided execution plan, users take specific actions, such as starting a project or allocating resources.

[0089] (Example 1)

[0090] 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."

[0091] Currently, there is a lack of systems that can quickly and effectively propose optimal solutions to social challenges faced by national and local governments, municipalities, and corporations (e.g., population decline, disaster countermeasures, food crises, etc.). In particular, there is a need for an integrated platform to collect large amounts of data, process outliers and missing values, normalize the data, predict future scenarios based on that data, and perform visual simulations. Furthermore, there is a need for a system that allows users to interactively modify proposed solutions and determine the optimal solution scenario.

[0092] 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.

[0093] In this invention, the server includes data collection means, preprocessing means, means for analyzing data using predictive artificial intelligence, means for generating simulation results using generative artificial intelligence, means for interactively changing scenarios, means for reanalyzing based on user feedback, determining the optimal scenario, and creating an execution plan, and means for displaying simulation results on a terminal. This allows the user to collect various types of data, perform preprocessing, and interactively modify and determine the optimal countermeasures through advanced prediction and simulation.

[0094] "Data collection methods" refer to means for efficiently collecting necessary data from various data sources, such as via APIs or database queries.

[0095] "Preprocessing means" refers to means of cleaning the collected data, detecting and removing outliers, and normalizing the data in order to improve the quality of the collected data.

[0096] A "predictive artificial intelligence method" is a means of using a trained predictive artificial intelligence model to predict future scenarios and generate countermeasures based on pre-processed data.

[0097] A "generative artificial intelligence means" is a means of generating visual simulation results based on prediction results using a generative artificial intelligence model.

[0098] "An interactive means of changing the scenario" refers to a method that allows users to view the provided simulation results on their device and modify the scenario or proposed countermeasures as needed.

[0099] "A means of re-analyzing based on user feedback, determining the optimal scenario, and creating an implementation plan" refers to a method of re-analyzing and generating data based on user modifications, proposing the optimal countermeasures, and creating an implementation plan.

[0100] "Means for displaying simulation results on a terminal" refers to means for displaying the generated simulation results on the user's terminal and providing them to the user.

[0101] The system according to the present invention proposes optimal solutions to social issues through data collection, preprocessing, prediction, generation, and interactive modification of scenarios. This system aims to efficiently and effectively address challenges faced by national and local governments, municipalities, and corporations (e.g., population decline, disaster countermeasures, food crises, etc.).

[0102] Data collection

[0103] The server accesses various data sources provided by national and local governments, municipalities, and corporations to collect necessary big data. This data collection includes using APIs and querying databases. Specifically, high-performance server equipment is used as hardware, and the software utilizes libraries (for example, Python's Requests) that enable access to various API endpoints. Examples of target data include demographic data, disaster risk data, and agricultural production data.

[0104] Pre-treatment

[0105] The server cleans the collected data, imputing outliers and missing values. It also normalizes the data, arranging each data point into a unified format. This transforms the data into a format that is easy to analyze. Specifically, it uses the Python Pandas library for data preprocessing.

[0106] Analysis using predictive artificial intelligence

[0107] The server uses a pre-trained predictive artificial intelligence model to forecast future scenarios based on collected data. For example, it can estimate population decline, predict food production, and assess disaster risk. Furthermore, it generates countermeasures based on these predictions and evaluates their effectiveness. Machine learning libraries such as TENSORFLOW® and Scikit-Learn can be used to implement the predictive artificial intelligence model.

[0108] Simulation using generative artificial intelligence

[0109] The server uses generative artificial intelligence models to visually represent prediction results. For example, it generates simulated images of future cityscapes and detailed text reports. This allows users to visualize concrete scenarios. The generative models utilize generative AI technologies such as GANs (Generative Adversarial Networks).

[0110] User-interactive scenario changes

[0111] Users can view the provided simulation results on their devices and modify scenarios and countermeasures as needed. For example, they might expand the scale of urban agriculture or change the focus of disaster countermeasures. The changes made by the user are sent to the server, where the analysis and simulation are performed again. The user's device has a web interface implemented to display the simulation results, using JavaScript®, HTML, and CSS.

[0112] Determining and executing the optimal scenario

[0113] Based on user feedback, the server re-analyzes and generates data to propose the optimal solution. Based on this optimal solution, a concrete implementation plan is formulated and provided to the user. This plan includes specific action items, schedules, and resource allocations. The generated implementation plan is provided as a PDF report, and a download link is displayed on the user's device.

[0114] Specific example: Predicting and countermeasures for food crises

[0115] 1. Data Collection

[0116] The server collects agricultural production data, weather data, and transportation infrastructure data. Data collection utilizes various APIs and queries from databases.

[0117] 2. Preprocessing

[0118] The server cleans and normalizes the collected data, preparing it for analysis. Missing data is appropriately imputed.

[0119] 3. Analysis using predictive AI

[0120] The server uses a trained predictive model to forecast crop yields for the next year. Based on this forecast, it calculates the probability of food shortages occurring and generates several proposed countermeasures (e.g., imports from other regions, promotion of urban agriculture, etc.).

[0121] 4. Simulation using generative AI

[0122] The server uses generative AI to visualize the urban landscape after the introduction of urban agriculture. It also generates a detailed text report of proposed countermeasures.

[0123] 5. User-initiated interactive scenario changes

[0124] The user reviews the simulation results provided on their device and adjusts the scale of urban agriculture and import plans. The modified scenario is then sent to the server.

[0125] 6. Determining and Implementing the Optimal Scenario

[0126] The server re-analyzes the corrected scenario and provides the user with the optimal solution. The user then determines the final scenario, develops a specific implementation plan, and executes it as a project.

[0127] Example of a prompt

[0128] "Design a system that predicts food crises and generates optimal countermeasures. This involves collecting, cleaning, and normalizing agricultural production data, weather data, and transportation infrastructure data. The predictive model calculates the probability of food shortages and generates countermeasures. The generative model visualizes the urban landscape after the implementation of urban agriculture, allowing users to interactively change scenarios. The system then generates optimal countermeasures again and proposes concrete action plans."

[0129] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0130] Program processing flow

[0131] Step 1: Prepare for data collection

[0132] The server prepares to access various data sources provided by national and local governments, municipalities, corporations, and other organizations.

[0133] The required inputs are the API endpoint to be accessed and the authentication information.

[0134] Specifically, the process involves preparing the API URL and authentication token, and then using the Python Requests library to prepare for the connection.

[0135] As an output, a connection to the data source to be collected is established.

[0136] Step 2: Obtaining the required information

[0137] The server collects data through an API. It queries the database directly to retrieve the necessary information.

[0138] The input requires request parameters to be sent to the API endpoint.

[0139] Specifically, the Requests library is used to send a GET request and retrieve response data in JSON format.

[0140] As output, the collected raw data is stored in temporary storage.

[0141] Step 3: Data Cleaning

[0142] The server performs data cleaning to improve the quality of the collected data. This includes detecting and removing outliers and imputing missing values.

[0143] Raw data is required as input.

[0144] Specifically, the process involves using the Python Pandas library to impute missing values ​​and remove outliers.

[0145] The output will be cleaned data.

[0146] Step 4: Data Normalization

[0147] The server performs data normalization to unify data in different formats and units.

[0148] Cleaned data is required as input.

[0149] Specifically, the process involves converting all numerical data to a common unit and standardizing the data format.

[0150] The output will be normalized data.

[0151] Step 5: Loading the predictive model

[0152] The server loads a pre-trained predictive artificial intelligence model.

[0153] The input requires the path to the model file.

[0154] Specifically, the process involves loading the model from disk into memory using TensorFlow or Scikit-Learn.

[0155] As an output, the predictive model is loaded into memory.

[0156] Step 6: Run the forecast

[0157] The server inputs pre-processed data into a predictive model to forecast future scenarios.

[0158] The input requires normalized data and a loaded predictive model.

[0159] In terms of specific operations, the model's predict function is called to obtain the prediction result.

[0160] The output will be the prediction results.

[0161] Step 7: Loading the Generative Model

[0162] The server loads a generative artificial intelligence model for visualization.

[0163] The path to the generated model file is required as input.

[0164] Specifically, the process involves loading the GAN model from disk into memory.

[0165] As output, the generative model is loaded into memory.

[0166] Step 8: Run the simulation

[0167] The server generates a visual simulation based on the prediction results.

[0168] The input requires the prediction results and the loaded generative model.

[0169] In terms of specific operations, the prediction results are input into a generative model, and simulated images and text reports are generated.

[0170] The output is a visualized simulation result.

[0171] Step 9: Displaying the simulation results

[0172] Users can view the simulation results through their device.

[0173] The generated simulation results are required as input.

[0174] Specifically, a user interface (UI) is provided to display the simulation results in a web browser.

[0175] The user will see the simulation results as output.

[0176] Step 10: Scenario Revision

[0177] Users can interactively modify proposed solutions and scenarios.

[0178] The displayed simulation results are required as input.

[0179] In terms of operation, parameters are adjusted via a web interface, and those changes are sent to the server.

[0180] The revised scenario is generated as output.

[0181] Step 11: Feedback Analysis

[0182] The server will perform another analysis based on the user's feedback.

[0183] A modified scenario is required as input.

[0184] Specifically, the process involves running the prediction and simulation again using the new parameter set.

[0185] The output will include re-analyzed prediction results and simulation results.

[0186] Step 12: Provide an execution plan

[0187] The server develops a concrete implementation plan based on the optimal countermeasure and provides it to the user.

[0188] The input requires re-analyzed prediction results and simulation results.

[0189] Specifically, the system generates an execution plan as a PDF report and provides a download link to the user's device.

[0190] As output, a specific execution plan is provided to the user.

[0191] (Application Example 1)

[0192] 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."

[0193] Traditional logistics center operations management is fraught with numerous variables and uncertainties, making efficient inventory management and optimal picking route planning difficult. Furthermore, real-time data analysis and immediate feedback are challenging, resulting in limited operational flexibility. This can lead to human error, wasted resources, and decreased productivity. A system is needed to address these challenges and improve the efficiency of logistics operations.

[0194] 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.

[0195] In this invention, the server includes means for collecting data, means for preprocessing data, means for analyzing data using predictive artificial intelligence, means for generating simulation results using generative artificial intelligence, means for interactively changing scenarios, means for determining the optimal scenario and creating an execution plan, means for supporting the optimization of logistics operations, means for displaying inventory data and transportation information in real time, and means for modifying scenarios via smart glasses. This makes it possible to analyze data in real time and realize optimal logistics operations.

[0196] "Data collection means" refers to devices or systems used to collect necessary data via APIs or database queries.

[0197] "Preprocessing means" refers to devices or systems that clean, correct for outliers, and normalize collected data, and convert it into a format suitable for analysis.

[0198] A "predictive artificial intelligence tool" is a device or system that uses a pre-trained predictive model to predict future scenarios and countermeasures based on collected data.

[0199] A "generative artificial intelligence means" is a device or system that visually generates simulation results based on prediction results and provides them to the user.

[0200] "Means of interactively modifying scenarios" refer to devices or systems that allow users to view simulation results and modify scenarios or countermeasures as needed.

[0201] "Means for determining the optimal scenario and creating an implementation plan" refers to devices or systems that formulate optimal countermeasures and specific implementation plans based on user feedback.

[0202] "Means to support the optimization of logistics operations" refer to devices and systems that streamline operations within a logistics center and propose optimal inventory management and picking routes.

[0203] "Means for displaying inventory data and transportation information in real time" refers to devices and systems that display inventory data and transportation information collected within a logistics center in real time, thereby visualizing the situation.

[0204] "Means of modifying scenarios via smart glasses" refers to devices or systems that enable the use of smart glasses during logistics operations to modify scenarios and countermeasures on the spot through visual information and voice instructions.

[0205] This invention relates to a system that streamlines operations within a logistics center and proposes optimal inventory management and picking routes. The following describes specific embodiments for carrying out the invention.

[0206] Hardware and software to use

[0207] This system utilizes cloud servers, smart glasses, AI models, and a real-time database.

[0208] Cloud servers: Common cloud infrastructure such as Amazon Web Services and Google Cloud.

[0209] Smart glasses: For example, a typical smart glasses device.

[0210] Software: Libraries and tools used for data processing and analysis, such as Python, pandas, scikit-learn, and Plotly.

[0211] Data processing and data calculation

[0212] 1. Data collection methods:

[0213] The server retrieves inventory and transportation information from the distribution center by querying it via API or from the database. This data includes product IDs, inventory quantities, and inbound / outbound timestamps.

[0214] 2. Pre-treatment means:

[0215] The server cleans the collected data, imputing missing values ​​and correcting outliers. It also normalizes the data and converts it into a format suitable for analysis.

[0216] 3. Predictive artificial intelligence methods:

[0217] The server uses trained predictive AI models (such as Linear Regression or Deep Learning models) to forecast demand from collected data. This enables optimal inventory management and picking route planning.

[0218] 4. Generative artificial intelligence methods:

[0219] Based on the prediction results, an AI model is used to generate visual simulation results. For example, a visual display showing changes in shelf placement or new picking routes is created.

[0220] 5. Means of interactively changing the scenario:

[0221] Users can review the simulation results provided through smart glasses and modify the scenario via voice prompts and visual information. For example, they might use a prompt such as, "Show me next week's inventory forecast and suggest the optimal picking order."

[0222] 6. Means for determining the optimal scenario and creating an execution plan:

[0223] The server re-analyzes the user feedback and develops optimal countermeasures and a specific implementation plan. This plan includes specific action items, schedules, and resource allocations.

[0224] Specific example

[0225] For example, a newly implemented system in a logistics center uses prompt messages like the following:

[0226] "Please display the inventory forecast for next week and suggest the optimal picking order."

[0227] Following this prompt, the server analyzes the data in real time and presents the optimal scenario to the user through smart glasses. The user reviews the view and modifies the scenario as needed. This allows for efficient management of operations within the logistics center.

[0228] This system can optimize complex logistics operations in real time, significantly improving employee work efficiency.

[0229] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0230] Step 1:

[0231] The server collects inventory and transportation information from the logistics center via APIs and databases. Specific actions performed by the server include sending requests to API endpoints and executing SQL queries. This inputs raw data such as product IDs, inventory quantities, and inbound / outbound timestamps, which are then stored in the database.

[0232] Step 2:

[0233] The server preprocesses the collected data. Specifically, it cleans the data (imputing missing values, correcting outliers) and normalizes it. The input is the raw data collected in step 1, which is then converted to a unified format. The output is cleaned data suitable for analysis.

[0234] Step 3:

[0235] The server analyzes pre-processed data and performs demand forecasting using a trained predictive artificial intelligence model. The input is the cleaned data from step 2, and the output is future demand forecast data. Specifically, this involves inputting data into a predictive model (e.g., Linear Regression or Deep Learning model) and retrieving results.

[0236] Step 4:

[0237] The server uses a generative artificial intelligence model to generate visual simulation results based on the prediction results. The input is the demand forecast data from step 3, and the output is a visual representation of the simulation (e.g., rearranged shelves or new picking routes). The specific operations include inputting data into the generative model and generating visual content.

[0238] Step 5:

[0239] The user reviews the simulation results provided through smart glasses and makes necessary changes interactively using prompts. The input is the simulation results from step 4, and the user's modification feedback is taken into consideration. Specific actions include scenario changes based on voice prompts and visual information.

[0240] Step 6:

[0241] The server reanalyzes the data based on user feedback and develops optimal countermeasures and specific implementation plans. The input is the user correction feedback from step 5, and the output is the final countermeasures and implementation plan. Specific actions include reanalysis incorporating the feedback and creation of an implementation plan.

[0242] Step 7:

[0243] The server deploys the finalized execution plan to each piece of equipment in the logistics operation, optimizing the entire system. The input is the execution plan from step 6, and the output is the optimized state of the logistics operation. Specific actions include sending instructions to transport robots and picking robots.

[0244] 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.

[0245] The system according to the present invention provides optimized solutions to social issues by combining data collection, preprocessing, prediction, generation, and interactive scenario modification with an emotion engine that recognizes user emotions. This system aims to respond in real time and adaptively to complex challenges faced by national and local governments, municipalities, and corporations (e.g., population decline, disaster countermeasures, food crises, etc.).

[0246] System Overview

[0247] 1. Data Collection

[0248] The server collects various types of data provided by national, local, municipal, and corporate entities via APIs and database queries. This data includes demographic data, disaster risk data, and agricultural production data.

[0249] 2. Preprocessing

[0250] The server cleans the collected data, detects outliers and missing values, and performs interpolation or removal as needed.

[0251] The server normalizes the data and prepares it for analysis.

[0252] 3. Analysis using predictive AI

[0253] The server uses a pre-trained predictive artificial intelligence model to forecast future scenarios. For example, it can predict the rate of population decline, the risk of disasters, and food production for the next year.

[0254] The server generates proposed countermeasures based on the prediction results and evaluates the effectiveness and risks of each.

[0255] 4. Simulation using generative AI

[0256] The server uses generative artificial intelligence (e.g., image generation AI, language generation AI) to represent predictive scenarios visually and in text. For example, it can generate simulated images of urban landscapes after the introduction of urban agriculture and detailed reports.

[0257] 5. User-initiated interactive scenario changes

[0258] Users view the provided simulation results on their devices and modify scenarios and proposed countermeasures based on their feedback. Examples include scenarios that expand the scale of urban agriculture or changes in the focus of disaster countermeasures.

[0259] User modifications are sent from the terminal to the server, where they are analyzed and simulated again.

[0260] 6. Emotion recognition by an emotion engine

[0261] The server uses an emotion engine to collect and analyze emotional information during user feedback and interactions. It uses speech recognition and natural language processing technologies to evaluate the user's emotional state.

[0262] 7. Emotion-based scenario adjustment

[0263] The server adjusts scenarios and proposed solutions based on emotional information obtained from the emotion engine. If the user is experiencing stress, the system takes action such as providing more concise and easy-to-understand information.

[0264] 8. Determining and Implementing the Optimal Scenario

[0265] The user determines the optimal scenario based on the regenerated scenarios.

[0266] The server creates and provides the user with a specific execution plan based on the optimal scenario. This includes detailed tasks, schedules, and resource allocations.

[0267] The user takes specific actions based on the decided execution plan.

[0268] Specific example

[0269] Predicting and countermeasures for food crises

[0270] 1. Data Collection

[0271] The server collects agricultural production data, weather data, and transportation infrastructure data. Data collection utilizes various APIs and queries from databases.

[0272] 2. Preprocessing

[0273] The server cleans and normalizes the collected data and appropriately imputes missing data.

[0274] 3. Analysis using predictive AI

[0275] The server uses a predictive AI model to forecast crop production for the next year, assess the risk of food shortages, and generate multiple countermeasures.

[0276] 4. Simulation using generative AI

[0277] The server uses generative AI to visualize the urban landscape after the introduction of urban agriculture and generates a detailed text report of proposed countermeasures.

[0278] 5. Scenario Changes by Users

[0279] The user reviews the simulation results on the terminal and adjusts the scale of urban agriculture and the import plan. The modified scenario is sent to the server.

[0280] 6. Emotion Recognition by Emotion Engine

[0281] The server collects and analyzes emotion information using speech recognition and natural language processing during the user's review. For example, if the user is feeling anxious, the explanation of the countermeasure plan is gently changed based on that information.

[0282] 7. Scenario Adjustment Based on Emotions

[0283] The server fine-tunes the scenario based on the information obtained from the emotion engine and presents the countermeasure plan in a more convincing form for the user.

[0284] 8. Determination and Execution of the Optimal Scenario

[0285] The user selects the optimal countermeasure based on the regenerated scenario, and the server creates an execution plan based on that scenario.

[0286] The user implements specific actions (e.g., starting a project, allocating resources) based on the determined execution plan.

[0287] By combining the emotion engine, this system can perform interactive scenario changes and optimizations considering the user's emotional state, and is capable of providing more effective and user-friendly countermeasures.

[0288] The following describes the processing flow.

[0289] [[ID=4​​​The server collects various types of data provided by national, local, municipal, and corporate entities via APIs or database queries. For example, it retrieves demographic data, disaster risk data, and agricultural production data.

[0291] Step 2: Data Preprocessing

[0292] The server cleans the collected data, detects missing or outlier values, and imputes or removes them if necessary.

[0293] The server normalizes the cleaned data and prepares it for analysis.

[0294] Step 3: Training the AI ​​model

[0295] The server uses normalized data to train predictive artificial intelligence models. For example, it uses regression models for population forecasting and time-series models for disaster risk prediction.

[0296] The server evaluates and optimizes the model's accuracy using cross-validation techniques.

[0297] Step 4: Generating Prediction Scenarios

[0298] The server uses a trained predictive artificial intelligence model to forecast future scenarios. For example, it can predict the rate of population decline, the risk of disasters, and food production for the next year.

[0299] The server generates multiple countermeasures based on the prediction results and evaluates the effectiveness and risks of each.

[0300] Step 5: Simulation using generative AI

[0301] The server uses generative artificial intelligence (image generation AI and language generation AI) to represent predictive scenarios visually and in text. For example, it generates simulated images of urban landscapes after the introduction of urban agriculture and detailed reports.

[0302] Step 6: Providing Simulation Results

[0303] The server provides the generated simulation results to the user. The user views these results on the terminal and evaluates the effectiveness of the scenarios and countermeasures.

[0304] Step 7: Emotion Recognition by Emotion Engine

[0305] The server uses the emotion engine to detect the emotions of the user during feedback and interaction. It analyzes the user's emotional state using speech recognition and natural language processing technologies.

[0306] Step 8: Scenario Adjustment Based on Emotions

[0307] The server uses the emotion information obtained from the emotion engine to adjust the scenarios and countermeasures. For example, when the user is feeling stressed, it provides more concise and easy-to-understand information.

[0308] Step 9: Scenario Change by User

[0309] The user modifies the scenarios and countermeasures on the terminal based on the simulation results and emotions. For example, expanding the scale of urban agriculture scenarios or changing the focus of disaster countermeasures.

[0310] The scenarios modified by the user are sent from the terminal to the server.

[0311] Step 10: Reanalysis and Resimulation

[0312] The server performs analysis and simulation again using the prediction-based AI and generation-based AI based on the modified scenarios sent by the user.

[0313] The server generates new simulation results and provides them to the user.

[0314] Step 11: Determining the Optimal Scenario

[0315] The user determines the optimal scenario based on the regenerated simulation results.

[0316] The server creates a specific execution plan based on the optimal scenario. This includes detailed tasks, schedules, and resource allocations.

[0317] Step 12: Implement the Action Plan

[0318] Based on the decided execution plan, the user takes specific actions (such as starting a project or allocating resources).

[0319] (Example 2)

[0320] 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".

[0321] Traditional solutions have struggled to provide effective solutions to the complex and diverse challenges facing society (e.g., population decline, disaster preparedness, food crises) due to their lack of real-time response capabilities and adaptability. Furthermore, the difficulty in incorporating user emotions and feedback into scenario adjustments has resulted in a lack of user-friendly solutions.

[0322] 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.

[0323] In this invention, the server includes means for collecting data, means for preprocessing data, means for analyzing data using predictive artificial intelligence, means for generating simulation results using generative artificial intelligence, means for interactively changing scenarios, means for collecting and analyzing feedback using an emotion recognition engine, means for adjusting scenarios based on emotion information, and means for determining the optimal scenario and creating an execution plan. This enables real-time analysis and simulation of collected data, realizes interactive scenario adjustments that take user emotions into consideration, and enables the provision of more effective and user-friendly solutions.

[0324] "Means of collecting data" refers to the system's function of obtaining necessary data from external sources using APIs or database queries.

[0325] "Means of preprocessing" refers to the system's function of cleaning collected data, imputing or removing outliers and missing values, and normalizing the data to prepare it for analysis.

[0326] "Methods for analyzing data using predictive artificial intelligence" refers to the functionality of a system that uses a trained AI model to predict future scenarios and risks from collected and pre-processed data.

[0327] "Means for generating simulation results using generative artificial intelligence" refers to the functionality of a system that uses image generation AI and language generation AI to represent predicted scenarios visually and as text.

[0328] "An interactive means of modifying the scenario" refers to a system function that allows users to view simulation results via a terminal, modify the scenario and proposed countermeasures based on feedback, and send those modifications to the server.

[0329] "Means for collecting and analyzing feedback using an emotion recognition engine" refers to a system function that uses speech recognition and natural language processing technologies to evaluate the user's emotional state and analyze the feedback.

[0330] "Means of adjusting scenarios based on emotional information" refers to a system function that adjusts scenarios and proposed solutions in a user-friendly manner based on user emotional information obtained from an emotional recognition engine.

[0331] "Means for determining the optimal scenario and creating an execution plan" refers to a system function that determines the optimal scenario based on the regenerated scenarios, creates a specific execution plan (tasks, schedule, resource allocation) based on that scenario, and provides it to the user.

[0332] This invention provides a system that consistently performs data acquisition, preprocessing, prediction, simulation, scenario modification, sentiment recognition, and optimal scenario determination. This system operates through the cooperation of a server, terminals, and users.

[0333] System configuration and operation

[0334] Data collection

[0335] The server collects necessary data from multiple data sources using APIs and database queries. This data includes demographics, crop production, and disaster risk information. Specifically, the server sends requests to API endpoints and retrieves the data returned as responses in JSON format.

[0336] Pre-treatment

[0337] The server cleans the collected data, detecting and imputing outliers and missing values. Each dataset is normalized and prepared for analysis. Specifically, missing values ​​are imputed with the data's mean, and the data range is scaled from 0 to 1.

[0338] Analysis using predictive AI

[0339] The server uses a pre-trained predictive AI model to forecast future scenarios. This includes forecasts of the next year's population decline rate, disaster risk, and food production volume. The server inputs data into the AI ​​model and outputs the prediction results. The pre-trained model may include a deep learning-based model.

[0340] Simulation using generative AI

[0341] The server uses a generative AI model to represent predictive scenarios in visual and textual formats. For example, it can generate simulated images of urban landscapes after the introduction of urban agriculture, or generate detailed action plan reports. At this time, the server will input prompts such as the following:

[0342] Prompt: "Based on forecasts of next year's crop production and an assessment of the resulting risk of food shortages, conduct a simulation of what happens after the introduction of urban agriculture, and generate the results as a visualization and a detailed text report."

[0343] User-interactive scenario changes

[0344] Users view the simulation results provided on their devices and modify the scenarios and proposed countermeasures. For example, they might adjust the scale of urban agriculture or change the focus of disaster response. The modified scenarios are sent from the device to the server for reanalysis and resimulation.

[0345] Emotion recognition by an emotion engine

[0346] The server uses an emotion engine to collect and analyze emotional information during user feedback and interactions. It evaluates the user's emotional state using speech recognition and natural language processing technologies, and makes adjustments based on the collected emotional information to reduce the user's anxiety and stress.

[0347] Emotion-based scenario adjustment

[0348] The server adjusts the scenario based on emotional information obtained from the emotion engine. For example, if the user is feeling stressed, the system will provide more concise and easy-to-understand information.

[0349] Determining and executing the optimal scenario

[0350] The user determines the optimal scenario based on the regenerated scenarios. The server creates a specific execution plan (tasks, schedule, resource allocation) based on that scenario. The user then takes specific actions according to the execution plan.

[0351] Thus, this system can respond in real time, taking user emotions into consideration, from data collection to the execution of optimal scenarios. Through close collaboration between the server, terminal, and user at each processing step, effective solutions to complex social issues are provided.

[0352] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0353] Step 1: Data Collection

[0354] The server uses APIs and database queries to collect the necessary data from multiple data sources.

[0355] Input: API endpoint or database query

[0356] Specific operation: The server sends a request to the API endpoint "https: / / api.example.com / population_stats" and retrieves demographic data in JSON format as a response.

[0357] Output: Collected data

[0358] Step 2: Pretreatment

[0359] The server cleans the collected data, detecting and imputing or removing outliers and missing values.

[0360] Input: Collected data

[0361] Specific operation: The server performs a data scan and imputes missing data with the mean value. It normalizes the data and prepares it for analysis.

[0362] Output: Preprocessed data

[0363] Step 3: Analysis using predictive AI

[0364] The server inputs the pre-processed data into a trained predictive AI model to predict future scenarios.

[0365] Input: Preprocessed data

[0366] Specific operation: The server inputs data into a predictive model and forecasts the population decline rate, disaster risk, and food production volume for the next year. Based on the prediction results, it generates proposed countermeasures and evaluates their effectiveness.

[0367] Output: Prediction results and proposed countermeasures

[0368] Step 4: Simulation using generative AI

[0369] The server uses a generative AI model based on the prediction results to represent the simulation results in both visual and textual formats.

[0370] Input: Prediction results and proposed countermeasures

[0371] Specific operation: The server inputs prompt text into the generative AI, which then generates simulated images of urban agriculture after its implementation and a detailed report of proposed countermeasures.

[0372] Prompt: "Based on forecasts of next year's crop production and an assessment of the resulting risk of food shortages, conduct a simulation of what happens after the introduction of urban agriculture, and generate the results as a visualization and a detailed text report."

[0373] Output: Simulation results, simulated images, and proposed countermeasures report.

[0374] Step 5: User-initiated interactive scenario changes

[0375] Users can view the simulation results provided through their device and modify the scenarios and proposed solutions as needed.

[0376] Input: Simulation results

[0377] Specific operation: The user checks the simulation results on their device and makes adjustments, such as adjusting the scale of urban agriculture. These adjustments are then sent from the device to the server.

[0378] Output: Modified scenario

[0379] Step 6: Emotion recognition by the emotion engine

[0380] The server uses an emotion engine to collect and analyze emotional information during user feedback and interactions.

[0381] Input: User feedback

[0382] Specific operation: The server uses speech recognition and natural language processing technologies to evaluate the user's emotional state and stores the analysis results as emotional information.

[0383] Output: Analyzed sentiment information

[0384] Step 7: Scenario adjustment based on emotional information

[0385] The server adjusts the scenario based on emotional information.

[0386] Input: Analyzed emotional information

[0387] Specific operation: Based on the results of the emotion recognition engine, the server adjusts the scenario and proposed solutions to be user-friendly and provides concise and easy-to-understand information.

[0388] Output: Adjusted scenario

[0389] Step 8: Determine and execute the optimal scenario

[0390] The user determines the optimal scenario based on the regenerated scenarios. The server then creates a specific execution plan based on that scenario and provides it to the user.

[0391] Input: Adjusted scenario

[0392] Specific operation: The user selects the optimal scenario, and the server creates an execution plan (tasks, schedule, resource allocation). The user then performs specific actions according to that plan.

[0393] Output: Optimal execution plan and specific actions

[0394] In this way, the system operates in close coordination at each processing step, from data collection to the execution of the optimal scenario.

[0395] (Application Example 2)

[0396] 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".

[0397] Fluctuations and shortages in food supply necessitate increased efficiency in food delivery services. Furthermore, flexible responses tailored to user needs and emotions are also required. This invention aims to address these challenges by improving the quality and efficiency of food delivery services through real-time prediction and optimization, as well as feedback based on user emotions.

[0398] 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.

[0399] In this invention, the server includes means for collecting data, means for preprocessing data, means for analyzing data using predictive artificial intelligence, means for generating simulation results using generative artificial intelligence, means for recognizing user emotions, means for adjusting scenarios based on user emotions, means for interactively changing scenarios, and means for determining the optimal scenario and creating an execution plan. This enables real-time supply and demand forecasting and supply planning for food delivery services, as well as flexible service provision that responds to user emotions.

[0400] "Means of data collection" refers to methods for collecting necessary data from various sources, including methods via APIs and queries from databases.

[0401] "Means of preprocessing" refers to means of cleaning and normalizing collected data and preparing it into an analyzable format.

[0402] "Methods for analyzing data using predictive artificial intelligence" refer to methods for analyzing collected data and predicting future scenarios using trained artificial intelligence models.

[0403] "Means for generating simulation results using generative artificial intelligence" refers to means for generating simulation results in visual and textual formats based on predicted scenarios.

[0404] "Means of recognizing user emotions" refers to means of collecting and analyzing emotional information during user feedback and interactions, and includes speech recognition and natural language processing technologies.

[0405] "Means of adjusting scenarios based on user emotions" refers to methods for flexibly adjusting scenarios and countermeasures using recognized user emotion information.

[0406] "An interactive means of changing the scenario" refers to a method by which users can view simulation results through their devices and modify the scenario or proposed countermeasures based on their feedback.

[0407] "Means for determining the optimal scenario and creating an execution plan" refers to the means for creating a specific execution plan based on the optimal scenario determined by the user.

[0408] The system according to the present invention aims to optimize food delivery services and improve the user experience. This system includes data collection, preprocessing, data analysis using predictive artificial intelligence, generation of simulation results using generative artificial intelligence, user emotion recognition and scenario adjustment, interactive scenario modification, determination of the optimal scenario, and creation of an execution plan.

[0409] The system will be implemented as follows:

[0410] First, the server collects necessary data, such as crop production data and weather data, via APIs or database queries. This data is then preprocessed, undergoing cleaning and normalization to prepare it for analysis.

[0411] Next, the server analyzes the collected data using a trained predictive artificial intelligence model. For example, it can predict the supply of agricultural products for the next year and evaluate the supply-demand balance in food delivery services. It also uses generative artificial intelligence to generate simulation results based on these predictions in visual and text formats. This could include, for example, optimizing delivery routes and suggesting delivery schedules.

[0412] Users view simulation results through their devices and provide feedback. The server collects and analyzes user sentiment information using speech recognition and natural language processing technologies. The sentiment engine flexibly adjusts the scenario based on user feedback and suggests the optimal course of action.

[0413] For example, a user might provide the following feedback:

[0414] "Today's delivery was delayed, but the quality was good. I wish it could be delivered a little faster."

[0415] In response to this feedback, the server performs sentiment recognition and analyzes whether the delivery delay is causing dissatisfaction with the user. Based on this, the delivery plan is optimized again to improve service to the user.

[0416] This enables real-time supply and demand forecasting and supply planning for food delivery services, as well as flexible service delivery tailored to user preferences. The system aims to improve the efficiency of food delivery and enhance user satisfaction.

[0417] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0418] Step 1:

[0419] The server collects necessary data, such as crop production data and weather data, via APIs or database queries. This provides the production and weather data to be used as input data. This data is then used in subsequent preprocessing steps.

[0420] Step 2:

[0421] The server preprocesses the collected data. Specifically, it cleans and normalizes the data to prepare it for analysis. The input to this step is the data collected in step 1, and the output is the cleaned data. This includes imputing missing values ​​and removing outliers.

[0422] Step 3:

[0423] The server uses a trained predictive artificial intelligence model to analyze preprocessed data and predict future scenarios. The input for this step is cleaned data, and the output is a predicted supply quantity. Examples include predicting crop supply quantities or evaluating supply-demand balance in food delivery services.

[0424] Step 4:

[0425] The server uses generative artificial intelligence to generate simulation results based on predicted scenarios in both visual and textual formats. The input for this step is the predicted scenario, and the output is visualized simulation results and a text report. Examples include optimizing delivery routes and suggesting delivery schedules.

[0426] Step 5:

[0427] The user reviews the simulation results generated by the server through their terminal and provides feedback. A concrete example of feedback might be, "Today's delivery was delayed, but the quality was good. I wish it could be delivered a little earlier." The input for this step is the visualized simulation results, and the output is user feedback.

[0428] Step 6:

[0429] The server uses speech recognition and natural language processing technologies to collect and analyze emotional information from user feedback. The input for this step is user feedback, and the output is user emotional information. The emotional recognition engine analyzes user stress and dissatisfaction to help adjust the system.

[0430] Step 7:

[0431] The server adjusts the scenario based on the user's sentiment information and suggests the optimal course of action. The input for this step is the user's sentiment information, and the output is the adjusted simulation results and new proposed solutions. For example, it might suggest re-optimizing the delivery plan or improving the service.

[0432] Step 8:

[0433] Ultimately, after the optimal scenario is determined by the user, the server creates a specific execution plan based on that scenario. The input to this step is the adjusted scenario, and the output is the execution plan. The execution plan includes detailed tasks, schedules, resource allocations, and more.

[0434] 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.

[0435] 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.

[0436] 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.

[0437] [Second Embodiment]

[0438] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0439] 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.

[0440] 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).

[0441] 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.

[0442] 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.

[0443] 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).

[0444] 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.

[0445] 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.

[0446] 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.

[0447] 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.

[0448] 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.

[0449] 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".

[0450] The system according to the present invention proposes optimal solutions to social issues through data collection, preprocessing, prediction, generation, and interactive modification of scenarios. This system aims to efficiently and effectively address challenges faced by national and local governments, municipalities, and corporations (e.g., population decline, disaster countermeasures, food crises, etc.).

[0451] System Overview

[0452] 1. Data Collection

[0453] The server accesses various data sources provided by national and local governments, municipalities, and corporations to collect the necessary big data. This data collection includes using APIs and querying databases. Examples of data that may be collected include demographic data, disaster risk data, and agricultural production data.

[0454] 2. Preprocessing

[0455] The server cleans the collected data, imputing outliers and missing values. It also normalizes the data, converting each data point into a unified format that is easy to analyze.

[0456] 3. Analysis using predictive AI

[0457] The server uses a trained predictive artificial intelligence model to forecast future scenarios based on collected data. For example, it estimates population decline, predicts food production, and assesses disaster risk. Furthermore, it generates proposed countermeasures based on these predictions and evaluates their effectiveness.

[0458] 4. Simulation using generative AI

[0459] The server uses a generative artificial intelligence model to visually represent prediction results. For example, it can generate simulated images of future cityscapes and detailed text reports. This allows users to visualize concrete scenarios.

[0460] 5. User-initiated interactive scenario changes

[0461] Users can view the provided simulation results on their devices and modify the scenarios and proposed solutions as needed. For example, they might expand the scale of urban agriculture or change the focus of disaster countermeasures. The changes made by the user are sent to the server, where the analysis and simulation are performed again.

[0462] 6. Determining and Implementing the Optimal Scenario

[0463] Based on user feedback, the server re-analyzes and generates data to propose the optimal solution. Based on this optimal solution, a concrete implementation plan is formulated and provided to the user. This implementation plan includes specific action items, schedules, resource allocation, etc.

[0464] Specific example

[0465] Predicting and countermeasures for food crises

[0466] 1. Data Collection

[0467] The server collects agricultural production data, weather data, and transportation infrastructure data. Data collection utilizes various APIs and queries from databases.

[0468] 2. Preprocessing

[0469] The server cleans and normalizes the collected data, preparing it for analysis. Missing data is appropriately imputed.

[0470] 3. Analysis using predictive AI

[0471] The server uses a trained predictive model to forecast crop yields for the next year. Based on this forecast, it calculates the probability of food shortages occurring and generates several proposed countermeasures (e.g., imports from other regions, promotion of urban agriculture, etc.).

[0472] 4. Simulation using generative AI

[0473] The server uses generative AI to visualize the urban landscape after the introduction of urban agriculture. It also generates a detailed text report of proposed countermeasures.

[0474] 5. User-initiated interactive scenario changes

[0475] The user reviews the simulation results provided on their device and adjusts the scale of urban agriculture and import plans. The modified scenario is then sent to the server.

[0476] 6. Determining and Implementing the Optimal Scenario

[0477] The server re-analyzes the correction scenario and provides the user with the optimal solution. The user then determines the final scenario, develops a specific implementation plan, and executes it as a project.

[0478] This system enables a dynamic and adaptive approach to addressing social challenges by integrating multiple data collection methods and advanced artificial intelligence technologies.

[0479] The following describes the processing flow.

[0480] Step 1: Data Collection

[0481] The server collects various types of data provided by national, local, municipal, and corporate entities using APIs and database queries. This data includes demographic data, disaster risk data, and agricultural production data.

[0482] Step 2: Data Preprocessing

[0483] The server cleans the collected raw data. Specifically, it detects missing or outlier values ​​and performs interpolation or removal as needed.

[0484] The server standardizes and normalizes the format of the cleaned data, ensuring consistency between data points.

[0485] Step 3: Training the AI ​​model

[0486] The server uses normalized data to train predictive artificial intelligence models. For example, it uses regression models for population forecasting and time-series models for disaster risk prediction.

[0487] The server evaluates and optimizes the model's accuracy using techniques such as cross-validation.

[0488] Step 4: Generating Prediction Scenarios

[0489] The server uses a pre-trained predictive artificial intelligence model to forecast future scenarios. For example, it can predict the rate of population decline, the risk of disasters, and food production for the next year.

[0490] The server generates proposed countermeasures based on the prediction results and evaluates the effectiveness and risks of each.

[0491] Step 5: Simulation using generative AI

[0492] The server uses generative artificial intelligence (image generation AI, language generation AI) to represent predictive scenarios visually and as text. For example, it generates simulated images of urban landscapes after the introduction of urban agriculture and detailed reports.

[0493] Step 6: Provide simulation results

[0494] The server provides the user with the generated simulation results. The user views these results on their terminal and evaluates the effectiveness of the scenarios and proposed solutions.

[0495] Step 7: User changes scenario

[0496] Based on the simulation results, users can modify scenarios and proposed countermeasures on their devices. For example, they might expand the scale of urban agriculture or change the focus of disaster countermeasures.

[0497] The scenario modified by the user is sent from the terminal to the server.

[0498] Step 8: Reanalysis and resimulation

[0499] The server then uses predictive and generative AI to perform analysis and simulation again based on the revised scenario submitted by the user.

[0500] The server generates new simulation results and provides them to the user.

[0501] Step 9: Determining the Optimal Scenario

[0502] The user views the regenerated simulation results and determines the optimal scenario.

[0503] The server creates a specific execution plan based on the optimal scenario. This includes task details, schedule, and resource allocation.

[0504] Step 10: Implement the action plan

[0505] Based on the decided execution plan, users take specific actions, such as starting a project or allocating resources.

[0506] (Example 1)

[0507] 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."

[0508] Currently, there is a lack of systems that can quickly and effectively propose optimal solutions to social challenges faced by national and local governments, municipalities, and corporations (e.g., population decline, disaster countermeasures, food crises, etc.). In particular, there is a need for an integrated platform to collect large amounts of data, process outliers and missing values, normalize the data, predict future scenarios based on that data, and perform visual simulations. Furthermore, there is a need for a system that allows users to interactively modify proposed solutions and determine the optimal solution scenario.

[0509] 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.

[0510] In this invention, the server includes data collection means, preprocessing means, means for analyzing data using predictive artificial intelligence, means for generating simulation results using generative artificial intelligence, means for interactively changing scenarios, means for reanalyzing based on user feedback, determining the optimal scenario, and creating an execution plan, and means for displaying simulation results on a terminal. This allows the user to collect various types of data, perform preprocessing, and interactively modify and determine the optimal countermeasures through advanced prediction and simulation.

[0511] "Data collection methods" refer to means for efficiently collecting necessary data from various data sources, such as via APIs or database queries.

[0512] "Preprocessing means" refers to means of cleaning the collected data, detecting and removing outliers, and normalizing the data in order to improve the quality of the collected data.

[0513] A "predictive artificial intelligence method" is a means of using a trained predictive artificial intelligence model to predict future scenarios and generate countermeasures based on pre-processed data.

[0514] A "generative artificial intelligence means" is a means of generating visual simulation results based on prediction results using a generative artificial intelligence model.

[0515] "An interactive means of changing the scenario" refers to a method that allows users to view the provided simulation results on their device and modify the scenario or proposed countermeasures as needed.

[0516] "A means of re-analyzing based on user feedback, determining the optimal scenario, and creating an implementation plan" refers to a method of re-analyzing and generating data based on user modifications, proposing the optimal countermeasures, and creating an implementation plan.

[0517] "Means for displaying simulation results on a terminal" refers to means for displaying the generated simulation results on the user's terminal and providing them to the user.

[0518] The system according to the present invention proposes optimal solutions to social issues through data collection, preprocessing, prediction, generation, and interactive modification of scenarios. This system aims to efficiently and effectively address challenges faced by national and local governments, municipalities, and corporations (e.g., population decline, disaster countermeasures, food crises, etc.).

[0519] Data collection

[0520] The server accesses various data sources provided by national and local governments, municipalities, and corporations to collect necessary big data. This data collection includes using APIs and querying databases. Specifically, high-performance server equipment is used as hardware, and the software utilizes libraries (for example, Python's Requests) that enable access to various API endpoints. Examples of target data include demographic data, disaster risk data, and agricultural production data.

[0521] Pre-treatment

[0522] The server cleans the collected data, imputing outliers and missing values. It also normalizes the data, arranging each data point into a unified format. This transforms the data into a format that is easy to analyze. Specifically, it uses the Python Pandas library for data preprocessing.

[0523] Analysis using predictive artificial intelligence

[0524] The server uses a pre-trained predictive artificial intelligence model to forecast future scenarios based on collected data. For example, it can estimate population decline, predict food production, and assess disaster risk. Furthermore, it generates countermeasures based on these predictions and evaluates their effectiveness. Machine learning libraries such as TensorFlow and Scikit-Learn can be used to implement the predictive AI model.

[0525] Simulation using generative artificial intelligence

[0526] The server uses generative artificial intelligence models to visually represent prediction results. For example, it generates simulated images of future cityscapes and detailed text reports. This allows users to visualize concrete scenarios. The generative models utilize generative AI technologies such as GANs (Generative Adversarial Networks).

[0527] User-interactive scenario changes

[0528] Users view the provided simulation results on their devices and modify the scenarios and proposed solutions as needed. For example, they might expand the scale of urban agriculture or change the focus of disaster countermeasures. The changes made by the user are sent to the server, where the analysis and simulation are performed again. The user's device has a web interface implemented to display the simulation results, using JavaScript, HTML, and CSS.

[0529] Determining and executing the optimal scenario

[0530] Based on user feedback, the server re-analyzes and generates data to propose the optimal solution. Based on this optimal solution, a concrete implementation plan is formulated and provided to the user. This plan includes specific action items, schedules, and resource allocations. The generated implementation plan is provided as a PDF report, and a download link is displayed on the user's device.

[0531] Specific example: Predicting and countermeasures for food crises

[0532] 1. Data Collection

[0533] The server collects agricultural production data, weather data, and transportation infrastructure data. Data collection utilizes various APIs and queries from databases.

[0534] 2. Preprocessing

[0535] The server cleans and normalizes the collected data, preparing it for analysis. Missing data is appropriately imputed.

[0536] 3. Analysis using predictive AI

[0537] The server uses a trained predictive model to forecast crop yields for the next year. Based on this forecast, it calculates the probability of food shortages occurring and generates several proposed countermeasures (e.g., imports from other regions, promotion of urban agriculture, etc.).

[0538] 4. Simulation using generative AI

[0539] The server uses generative AI to visualize the urban landscape after the introduction of urban agriculture. It also generates a detailed text report of proposed countermeasures.

[0540] 5. User-initiated interactive scenario changes

[0541] The user reviews the simulation results provided on their device and adjusts the scale of urban agriculture and import plans. The modified scenario is then sent to the server.

[0542] 6. Determining and Implementing the Optimal Scenario

[0543] The server re-analyzes the corrected scenario and provides the user with the optimal solution. The user then determines the final scenario, develops a specific implementation plan, and executes it as a project.

[0544] Example of a prompt

[0545] "Design a system that predicts food crises and generates optimal countermeasures. This involves collecting, cleaning, and normalizing agricultural production data, weather data, and transportation infrastructure data. The predictive model calculates the probability of food shortages and generates countermeasures. The generative model visualizes the urban landscape after the implementation of urban agriculture, allowing users to interactively change scenarios. The system then generates optimal countermeasures again and proposes concrete action plans."

[0546] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0547] Program processing flow

[0548] Step 1: Prepare for data collection

[0549] The server prepares to access various data sources provided by national and local governments, municipalities, corporations, and other organizations.

[0550] The required inputs are the API endpoint to be accessed and the authentication information.

[0551] Specifically, the process involves preparing the API URL and authentication token, and then using the Python Requests library to prepare for the connection.

[0552] As an output, a connection to the data source to be collected is established.

[0553] Step 2: Obtaining the required information

[0554] The server collects data through an API. It queries the database directly to retrieve the necessary information.

[0555] The input requires request parameters to be sent to the API endpoint.

[0556] Specifically, the Requests library is used to send a GET request and retrieve response data in JSON format.

[0557] As output, the collected raw data is stored in temporary storage.

[0558] Step 3: Data Cleaning

[0559] The server performs data cleaning to improve the quality of the collected data. This includes detecting and removing outliers and imputing missing values.

[0560] Raw data is required as input.

[0561] Specifically, the process involves using the Python Pandas library to impute missing values ​​and remove outliers.

[0562] The output will be cleaned data.

[0563] Step 4: Data Normalization

[0564] The server performs data normalization to unify data in different formats and units.

[0565] Cleaned data is required as input.

[0566] Specifically, the process involves converting all numerical data to a common unit and standardizing the data format.

[0567] The output will be normalized data.

[0568] Step 5: Loading the predictive model

[0569] The server loads a pre-trained predictive artificial intelligence model.

[0570] The input requires the path to the model file.

[0571] Specifically, the process involves loading the model from disk into memory using TensorFlow or Scikit-Learn.

[0572] As an output, the predictive model is loaded into memory.

[0573] Step 6: Run the forecast

[0574] The server inputs pre-processed data into a predictive model to forecast future scenarios.

[0575] The input requires normalized data and a loaded predictive model.

[0576] In terms of specific operations, the model's predict function is called to obtain the prediction result.

[0577] The output will be the prediction results.

[0578] Step 7: Loading the Generative Model

[0579] The server loads a generative artificial intelligence model for visualization.

[0580] The path to the generated model file is required as input.

[0581] Specifically, the process involves loading the GAN model from disk into memory.

[0582] As output, the generative model is loaded into memory.

[0583] Step 8: Run the simulation

[0584] The server generates a visual simulation based on the prediction results.

[0585] The input requires the prediction results and the loaded generative model.

[0586] In terms of specific operations, the prediction results are input into a generative model, and simulated images and text reports are generated.

[0587] The output is a visualized simulation result.

[0588] Step 9: Displaying the simulation results

[0589] Users can view the simulation results through their device.

[0590] The generated simulation results are required as input.

[0591] Specifically, a user interface (UI) is provided to display the simulation results in a web browser.

[0592] The user will see the simulation results as output.

[0593] Step 10: Scenario Revision

[0594] Users can interactively modify proposed solutions and scenarios.

[0595] The displayed simulation results are required as input.

[0596] In terms of operation, parameters are adjusted via a web interface, and those changes are sent to the server.

[0597] The revised scenario is generated as output.

[0598] Step 11: Feedback Analysis

[0599] The server will perform another analysis based on the user's feedback.

[0600] A modified scenario is required as input.

[0601] Specifically, the process involves running the prediction and simulation again using the new parameter set.

[0602] The output will include re-analyzed prediction results and simulation results.

[0603] Step 12: Provide an execution plan

[0604] The server develops a concrete implementation plan based on the optimal countermeasure and provides it to the user.

[0605] The input requires re-analyzed prediction results and simulation results.

[0606] Specifically, the system generates an execution plan as a PDF report and provides a download link to the user's device.

[0607] As output, a specific execution plan is provided to the user.

[0608] (Application Example 1)

[0609] 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."

[0610] Traditional logistics center operations management is fraught with numerous variables and uncertainties, making efficient inventory management and optimal picking route planning difficult. Furthermore, real-time data analysis and immediate feedback are challenging, resulting in limited operational flexibility. This can lead to human error, wasted resources, and decreased productivity. A system is needed to address these challenges and improve the efficiency of logistics operations.

[0611] 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.

[0612] In this invention, the server includes means for collecting data, means for preprocessing data, means for analyzing data using predictive artificial intelligence, means for generating simulation results using generative artificial intelligence, means for interactively changing scenarios, means for determining the optimal scenario and creating an execution plan, means for supporting the optimization of logistics operations, means for displaying inventory data and transportation information in real time, and means for modifying scenarios via smart glasses. This makes it possible to analyze data in real time and realize optimal logistics operations.

[0613] "Data collection means" refers to devices or systems used to collect necessary data via APIs or database queries.

[0614] "Preprocessing means" refers to devices or systems that clean, correct for outliers, and normalize collected data, and convert it into a format suitable for analysis.

[0615] A "predictive artificial intelligence tool" is a device or system that uses a pre-trained predictive model to predict future scenarios and countermeasures based on collected data.

[0616] A "generative artificial intelligence means" is a device or system that visually generates simulation results based on prediction results and provides them to the user.

[0617] "Means of interactively modifying scenarios" refer to devices or systems that allow users to view simulation results and modify scenarios or countermeasures as needed.

[0618] "Means for determining the optimal scenario and creating an implementation plan" refers to devices or systems that formulate optimal countermeasures and specific implementation plans based on user feedback.

[0619] "Means to support the optimization of logistics operations" refer to devices and systems that streamline operations within a logistics center and propose optimal inventory management and picking routes.

[0620] "Means for displaying inventory data and transportation information in real time" refers to devices and systems that display inventory data and transportation information collected within a logistics center in real time, thereby visualizing the situation.

[0621] "Means of modifying scenarios via smart glasses" refers to devices or systems that enable the use of smart glasses during logistics operations to modify scenarios and countermeasures on the spot through visual information and voice instructions.

[0622] This invention relates to a system that streamlines operations within a logistics center and proposes optimal inventory management and picking routes. The following describes specific embodiments for carrying out the invention.

[0623] Hardware and software to use

[0624] This system utilizes cloud servers, smart glasses, AI models, and a real-time database.

[0625] Cloud servers: Common cloud infrastructure such as Amazon Web Services and Google Cloud.

[0626] Smart glasses: For example, a typical smart glasses device.

[0627] Software: Libraries and tools used for data processing and analysis, such as Python, pandas, scikit-learn, and Plotly.

[0628] Data processing and data calculation

[0629] 1. Data collection methods:

[0630] The server retrieves inventory and transportation information from the distribution center by querying it via API or from the database. This data includes product IDs, inventory quantities, and inbound / outbound timestamps.

[0631] 2. Pre-treatment means:

[0632] The server cleans the collected data, imputing missing values ​​and correcting outliers. It also normalizes the data and converts it into a format suitable for analysis.

[0633] 3. Predictive artificial intelligence methods:

[0634] The server uses trained predictive AI models (such as Linear Regression or Deep Learning models) to forecast demand from collected data. This enables optimal inventory management and picking route planning.

[0635] 4. Generative artificial intelligence methods:

[0636] Based on the prediction results, an AI model is used to generate visual simulation results. For example, a visual display showing changes in shelf placement or new picking routes is created.

[0637] 5. Means of interactively changing the scenario:

[0638] Users can review the simulation results provided through smart glasses and modify the scenario via voice prompts and visual information. For example, they might use a prompt such as, "Show me next week's inventory forecast and suggest the optimal picking order."

[0639] 6. Means for determining the optimal scenario and creating an execution plan:

[0640] The server re-analyzes the user feedback and develops optimal countermeasures and a specific implementation plan. This plan includes specific action items, schedules, and resource allocations.

[0641] Specific example

[0642] For example, a newly implemented system in a logistics center uses prompt messages like the following:

[0643] "Please display the inventory forecast for next week and suggest the optimal picking order."

[0644] Following this prompt, the server analyzes the data in real time and presents the optimal scenario to the user through smart glasses. The user reviews the view and modifies the scenario as needed. This allows for efficient management of operations within the logistics center.

[0645] This system can optimize complex logistics operations in real time, significantly improving employee work efficiency.

[0646] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0647] Step 1:

[0648] The server collects inventory and transportation information from the logistics center via APIs and databases. Specific actions performed by the server include sending requests to API endpoints and executing SQL queries. This inputs raw data such as product IDs, inventory quantities, and inbound / outbound timestamps, which are then stored in the database.

[0649] Step 2:

[0650] The server preprocesses the collected data. Specifically, it cleans the data (imputing missing values, correcting outliers) and normalizes it. The input is the raw data collected in step 1, which is then converted to a unified format. The output is cleaned data suitable for analysis.

[0651] Step 3:

[0652] The server analyzes pre-processed data and performs demand forecasting using a trained predictive artificial intelligence model. The input is the cleaned data from step 2, and the output is future demand forecast data. Specifically, this involves inputting data into a predictive model (e.g., Linear Regression or Deep Learning model) and retrieving results.

[0653] Step 4:

[0654] The server uses a generative artificial intelligence model to generate visual simulation results based on the prediction results. The input is the demand forecast data from step 3, and the output is a visual representation of the simulation (e.g., rearranged shelves or new picking routes). The specific operations include inputting data into the generative model and generating visual content.

[0655] Step 5:

[0656] The user reviews the simulation results provided through smart glasses and makes necessary changes interactively using prompts. The input is the simulation results from step 4, and the user's modification feedback is taken into consideration. Specific actions include scenario changes based on voice prompts and visual information.

[0657] Step 6:

[0658] The server reanalyzes the data based on user feedback and develops optimal countermeasures and specific implementation plans. The input is the user correction feedback from step 5, and the output is the final countermeasures and implementation plan. Specific actions include reanalysis incorporating the feedback and creation of an implementation plan.

[0659] Step 7:

[0660] The server deploys the finalized execution plan to each piece of equipment in the logistics operation, optimizing the entire system. The input is the execution plan from step 6, and the output is the optimized state of the logistics operation. Specific actions include sending instructions to transport robots and picking robots.

[0661] 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.

[0662] The system according to the present invention provides optimized solutions to social issues by combining data collection, preprocessing, prediction, generation, and interactive scenario modification with an emotion engine that recognizes user emotions. This system aims to respond in real time and adaptively to complex challenges faced by national and local governments, municipalities, and corporations (e.g., population decline, disaster countermeasures, food crises, etc.).

[0663] System Overview

[0664] 1. Data Collection

[0665] The server collects various types of data provided by national, local, municipal, and corporate entities via APIs and database queries. This data includes demographic data, disaster risk data, and agricultural production data.

[0666] 2. Preprocessing

[0667] The server cleans the collected data, detects outliers and missing values, and performs interpolation or removal as needed.

[0668] The server normalizes the data and prepares it for analysis.

[0669] 3. Analysis using predictive AI

[0670] The server uses a pre-trained predictive artificial intelligence model to forecast future scenarios. For example, it can predict the rate of population decline, the risk of disasters, and food production for the next year.

[0671] The server generates proposed countermeasures based on the prediction results and evaluates the effectiveness and risks of each.

[0672] 4. Simulation using generative AI

[0673] The server uses generative artificial intelligence (e.g., image generation AI, language generation AI) to represent predictive scenarios visually and in text. For example, it can generate simulated images of urban landscapes after the introduction of urban agriculture and detailed reports.

[0674] 5. User-initiated interactive scenario changes

[0675] Users view the provided simulation results on their devices and modify scenarios and proposed countermeasures based on their feedback. Examples include scenarios that expand the scale of urban agriculture or changes in the focus of disaster countermeasures.

[0676] User modifications are sent from the terminal to the server, where they are analyzed and simulated again.

[0677] 6. Emotion recognition by an emotion engine

[0678] The server uses an emotion engine to collect and analyze emotional information during user feedback and interactions. It uses speech recognition and natural language processing technologies to evaluate the user's emotional state.

[0679] 7. Emotion-based scenario adjustment

[0680] The server adjusts scenarios and proposed solutions based on emotional information obtained from the emotion engine. If the user is experiencing stress, the system takes action such as providing more concise and easy-to-understand information.

[0681] 8. Determining and Implementing the Optimal Scenario

[0682] The user determines the optimal scenario based on the regenerated scenarios.

[0683] The server creates and provides the user with a specific execution plan based on the optimal scenario. This includes detailed tasks, schedules, and resource allocations.

[0684] The user takes specific actions based on the decided execution plan.

[0685] Specific example

[0686] Predicting and countermeasures for food crises

[0687] 1. Data Collection

[0688] The server collects agricultural production data, weather data, and transportation infrastructure data. Data collection utilizes various APIs and queries from databases.

[0689] 2. Preprocessing

[0690] The server cleans and normalizes the collected data and appropriately imputes missing data.

[0691] 3. Analysis using predictive AI

[0692] The server uses a predictive AI model to forecast crop production for the next year, assess the risk of food shortages, and generate multiple countermeasures.

[0693] 4. Simulation using generative AI

[0694] The server uses generative AI to visualize the urban landscape after the introduction of urban agriculture and generates a detailed text report of proposed countermeasures.

[0695] 5. User-initiated scenario changes

[0696] Users review the simulation results on their terminal and adjust the scale of urban agriculture and import plans. The revised scenario is then sent to the server.

[0697] 6. Emotion recognition by an emotion engine

[0698] The server collects and analyzes emotional information using speech recognition and natural language processing during user reviews. For example, if a user is feeling anxious, the server uses that information to soften the explanation of the suggested solutions.

[0699] 7. Emotion-based scenario adjustment

[0700] The server fine-tunes the scenario based on information obtained from the emotion engine, presenting solutions in a way that is more convincing to the user.

[0701] 8. Determining and Implementing the Optimal Scenario

[0702] The user selects the optimal countermeasure based on the regenerated scenario, and the server creates an execution plan based on that scenario.

[0703] The user takes specific actions (e.g., starting a project, allocating resources) based on the determined execution plan.

[0704] By combining this system with an emotion engine, it can perform interactive scenario changes and optimizations that take into account the user's emotional state, enabling it to provide more effective and user-friendly solutions.

[0705] The following describes the processing flow.

[0706] Step 1: Data Collection

[0707] The server collects various types of data provided by national, local, municipal, and corporate entities via APIs or database queries. For example, it retrieves demographic data, disaster risk data, and agricultural production data.

[0708] Step 2: Data Preprocessing

[0709] The server cleans the collected data, detects missing or outlier values, and imputes or removes them if necessary.

[0710] The server normalizes the cleaned data and prepares it for analysis.

[0711] Step 3: Training the AI ​​model

[0712] The server uses normalized data to train predictive artificial intelligence models. For example, it uses regression models for population forecasting and time-series models for disaster risk prediction.

[0713] The server evaluates and optimizes the model's accuracy using cross-validation techniques.

[0714] Step 4: Generating Prediction Scenarios

[0715] The server uses a trained predictive artificial intelligence model to forecast future scenarios. For example, it can predict the rate of population decline, the risk of disasters, and food production for the next year.

[0716] The server generates multiple countermeasures based on the prediction results and evaluates the effectiveness and risks of each.

[0717] Step 5: Simulation using generative AI

[0718] The server uses generative artificial intelligence (image generation AI and language generation AI) to represent predictive scenarios visually and in text. For example, it generates simulated images of urban landscapes after the introduction of urban agriculture and detailed reports.

[0719] Step 6: Provide simulation results

[0720] The server provides the user with the generated simulation results. The user views these results on their terminal and evaluates the effectiveness of the scenarios and proposed solutions.

[0721] Step 7: Emotion recognition by the emotion engine

[0722] The server uses an emotion engine to detect user feedback and emotions during interactions. It analyzes the user's emotional state using speech recognition and natural language processing technologies.

[0723] Step 8: Emotion-based scenario adjustments

[0724] The server uses emotional information obtained from the emotion engine to adjust scenarios and suggested solutions. For example, if the user is feeling stressed, it will provide more concise and easy-to-understand information.

[0725] Step 9: User changes scenario

[0726] Users modify scenarios and proposed solutions on their devices based on simulation results and their emotions. For example, they might expand the scale of urban agriculture or change the focus of disaster preparedness.

[0727] The scenario modified by the user is sent from the terminal to the server.

[0728] Step 10: Reanalysis and resimulation

[0729] The server then uses predictive and generative AI to perform analysis and simulation again based on the revised scenario submitted by the user.

[0730] The server generates new simulation results and provides them to the user.

[0731] Step 11: Determining the Optimal Scenario

[0732] The user determines the optimal scenario based on the regenerated simulation results.

[0733] The server creates a specific execution plan based on the optimal scenario. This includes detailed tasks, schedules, and resource allocations.

[0734] Step 12: Implement the Action Plan

[0735] Based on the decided execution plan, the user takes specific actions (such as starting a project or allocating resources).

[0736] (Example 2)

[0737] 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".

[0738] Traditional solutions have struggled to provide effective solutions to the complex and diverse challenges facing society (e.g., population decline, disaster preparedness, food crises) due to their lack of real-time response capabilities and adaptability. Furthermore, the difficulty in incorporating user emotions and feedback into scenario adjustments has resulted in a lack of user-friendly solutions.

[0739] 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.

[0740] In this invention, the server includes means for collecting data, means for preprocessing data, means for analyzing data using predictive artificial intelligence, means for generating simulation results using generative artificial intelligence, means for interactively changing scenarios, means for collecting and analyzing feedback using an emotion recognition engine, means for adjusting scenarios based on emotion information, and means for determining the optimal scenario and creating an execution plan. This enables real-time analysis and simulation of collected data, realizes interactive scenario adjustments that take user emotions into consideration, and enables the provision of more effective and user-friendly solutions.

[0741] "Means of collecting data" refers to the system's function of obtaining necessary data from external sources using APIs or database queries.

[0742] "Means of preprocessing" refers to the system's function of cleaning collected data, imputing or removing outliers and missing values, and normalizing the data to prepare it for analysis.

[0743] "Methods for analyzing data using predictive artificial intelligence" refers to the functionality of a system that uses a trained AI model to predict future scenarios and risks from collected and pre-processed data.

[0744] "Means for generating simulation results using generative artificial intelligence" refers to the functionality of a system that uses image generation AI and language generation AI to represent predicted scenarios visually and as text.

[0745] "An interactive means of modifying the scenario" refers to a system function that allows users to view simulation results via a terminal, modify the scenario and proposed countermeasures based on feedback, and send those modifications to the server.

[0746] "Means for collecting and analyzing feedback using an emotion recognition engine" refers to a system function that uses speech recognition and natural language processing technologies to evaluate the user's emotional state and analyze the feedback.

[0747] "Means of adjusting scenarios based on emotional information" refers to a system function that adjusts scenarios and proposed solutions in a user-friendly manner based on user emotional information obtained from an emotional recognition engine.

[0748] "Means for determining the optimal scenario and creating an execution plan" refers to a system function that determines the optimal scenario based on the regenerated scenarios, creates a specific execution plan (tasks, schedule, resource allocation) based on that scenario, and provides it to the user.

[0749] This invention provides a system that consistently performs data acquisition, preprocessing, prediction, simulation, scenario modification, sentiment recognition, and optimal scenario determination. This system operates through the cooperation of a server, terminals, and users.

[0750] System configuration and operation

[0751] Data collection

[0752] The server collects necessary data from multiple data sources using APIs and database queries. This data includes demographics, crop production, and disaster risk information. Specifically, the server sends requests to API endpoints and retrieves the data returned as responses in JSON format.

[0753] Pre-treatment

[0754] The server cleans the collected data, detecting and imputing outliers and missing values. Each dataset is normalized and prepared for analysis. Specifically, missing values ​​are imputed with the data's mean, and the data range is scaled from 0 to 1.

[0755] Analysis using predictive AI

[0756] The server uses a pre-trained predictive AI model to forecast future scenarios. This includes forecasts of the next year's population decline rate, disaster risk, and food production volume. The server inputs data into the AI ​​model and outputs the prediction results. The pre-trained model may include a deep learning-based model.

[0757] Simulation using generative AI

[0758] The server uses a generative AI model to represent predictive scenarios in visual and textual formats. For example, it can generate simulated images of urban landscapes after the introduction of urban agriculture, or generate detailed action plan reports. At this time, the server will input prompts such as the following:

[0759] Prompt: "Based on forecasts of next year's crop production and an assessment of the resulting risk of food shortages, conduct a simulation of what happens after the introduction of urban agriculture, and generate the results as a visualization and a detailed text report."

[0760] User-interactive scenario changes

[0761] Users view the simulation results provided on their devices and modify the scenarios and proposed countermeasures. For example, they might adjust the scale of urban agriculture or change the focus of disaster response. The modified scenarios are sent from the device to the server for reanalysis and resimulation.

[0762] Emotion recognition by an emotion engine

[0763] The server uses an emotion engine to collect and analyze emotional information during user feedback and interactions. It evaluates the user's emotional state using speech recognition and natural language processing technologies, and makes adjustments based on the collected emotional information to reduce the user's anxiety and stress.

[0764] Emotion-based scenario adjustment

[0765] The server adjusts the scenario based on emotional information obtained from the emotion engine. For example, if the user is feeling stressed, the system will provide more concise and easy-to-understand information.

[0766] Determining and executing the optimal scenario

[0767] The user determines the optimal scenario based on the regenerated scenarios. The server creates a specific execution plan (tasks, schedule, resource allocation) based on that scenario. The user then takes specific actions according to the execution plan.

[0768] Thus, this system can respond in real time, taking user emotions into consideration, from data collection to the execution of optimal scenarios. Through close collaboration between the server, terminal, and user at each processing step, effective solutions to complex social issues are provided.

[0769] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0770] Step 1: Data Collection

[0771] The server uses APIs and database queries to collect the necessary data from multiple data sources.

[0772] Input: API endpoint or database query

[0773] Specific operation: The server sends a request to the API endpoint "https: / / api.example.com / population_stats" and retrieves demographic data in JSON format as a response.

[0774] Output: Collected data

[0775] Step 2: Pretreatment

[0776] The server cleans the collected data, detecting and imputing or removing outliers and missing values.

[0777] Input: Collected data

[0778] Specific operation: The server performs a data scan and imputes missing data with the mean value. It normalizes the data and prepares it for analysis.

[0779] Output: Preprocessed data

[0780] Step 3: Analysis using predictive AI

[0781] The server inputs the pre-processed data into a trained predictive AI model to predict future scenarios.

[0782] Input: Preprocessed data

[0783] Specific operation: The server inputs data into a predictive model and forecasts the population decline rate, disaster risk, and food production volume for the next year. Based on the prediction results, it generates proposed countermeasures and evaluates their effectiveness.

[0784] Output: Prediction results and proposed countermeasures

[0785] Step 4: Simulation using generative AI

[0786] The server uses a generative AI model based on the prediction results to represent the simulation results in both visual and textual formats.

[0787] Input: Prediction results and proposed countermeasures

[0788] Specific operation: The server inputs prompt text into the generative AI, which then generates simulated images of urban agriculture after its implementation and a detailed report of proposed countermeasures.

[0789] Prompt: "Based on forecasts of next year's crop production and an assessment of the resulting risk of food shortages, conduct a simulation of what happens after the introduction of urban agriculture, and generate the results as a visualization and a detailed text report."

[0790] Output: Simulation results, simulated images, and proposed countermeasures report.

[0791] Step 5: User-initiated interactive scenario changes

[0792] Users can view the simulation results provided through their device and modify the scenarios and proposed solutions as needed.

[0793] Input: Simulation results

[0794] Specific operation: The user checks the simulation results on their device and makes adjustments, such as adjusting the scale of urban agriculture. These adjustments are then sent from the device to the server.

[0795] Output: Modified scenario

[0796] Step 6: Emotion recognition by the emotion engine

[0797] The server uses an emotion engine to collect and analyze emotional information during user feedback and interactions.

[0798] Input: User feedback

[0799] Specific operation: The server uses speech recognition and natural language processing technologies to evaluate the user's emotional state and stores the analysis results as emotional information.

[0800] Output: Analyzed sentiment information

[0801] Step 7: Scenario adjustment based on emotional information

[0802] The server adjusts the scenario based on emotional information.

[0803] Input: Analyzed emotional information

[0804] Specific operation: Based on the results of the emotion recognition engine, the server adjusts the scenario and proposed solutions to be user-friendly and provides concise and easy-to-understand information.

[0805] Output: Adjusted scenario

[0806] Step 8: Determine and execute the optimal scenario

[0807] The user determines the optimal scenario based on the regenerated scenarios. The server then creates a specific execution plan based on that scenario and provides it to the user.

[0808] Input: Adjusted scenario

[0809] Specific operation: The user selects the optimal scenario, and the server creates an execution plan (tasks, schedule, resource allocation). The user then performs specific actions according to that plan.

[0810] Output: Optimal execution plan and specific actions

[0811] In this way, the system operates in close coordination at each processing step, from data collection to the execution of the optimal scenario.

[0812] (Application Example 2)

[0813] 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."

[0814] Fluctuations and shortages in food supply necessitate increased efficiency in food delivery services. Furthermore, flexible responses tailored to user needs and emotions are also required. This invention aims to address these challenges by improving the quality and efficiency of food delivery services through real-time prediction and optimization, as well as feedback based on user emotions.

[0815] 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.

[0816] In this invention, the server includes means for collecting data, means for preprocessing data, means for analyzing data using predictive artificial intelligence, means for generating simulation results using generative artificial intelligence, means for recognizing user emotions, means for adjusting scenarios based on user emotions, means for interactively changing scenarios, and means for determining the optimal scenario and creating an execution plan. This enables real-time supply and demand forecasting and supply planning for food delivery services, as well as flexible service provision that responds to user emotions.

[0817] "Means of data collection" refers to methods for collecting necessary data from various sources, including methods via APIs and queries from databases.

[0818] "Means of preprocessing" refers to means of cleaning and normalizing collected data and preparing it into an analyzable format.

[0819] "Methods for analyzing data using predictive artificial intelligence" refer to methods for analyzing collected data and predicting future scenarios using trained artificial intelligence models.

[0820] "Means for generating simulation results using generative artificial intelligence" refers to means for generating simulation results in visual and textual formats based on predicted scenarios.

[0821] "Means of recognizing user emotions" refers to means of collecting and analyzing emotional information during user feedback and interactions, and includes speech recognition and natural language processing technologies.

[0822] "Means of adjusting scenarios based on user emotions" refers to methods for flexibly adjusting scenarios and countermeasures using recognized user emotion information.

[0823] "An interactive means of changing the scenario" refers to a method by which users can view simulation results through their devices and modify the scenario or proposed countermeasures based on their feedback.

[0824] "Means for determining the optimal scenario and creating an execution plan" refers to the means for creating a specific execution plan based on the optimal scenario determined by the user.

[0825] The system according to the present invention aims to optimize food delivery services and improve the user experience. This system includes data collection, preprocessing, data analysis using predictive artificial intelligence, generation of simulation results using generative artificial intelligence, user emotion recognition and scenario adjustment, interactive scenario modification, determination of the optimal scenario, and creation of an execution plan.

[0826] The system will be implemented as follows:

[0827] First, the server collects necessary data, such as crop production data and weather data, via APIs or database queries. This data is then preprocessed, undergoing cleaning and normalization to prepare it for analysis.

[0828] Next, the server analyzes the collected data using a trained predictive artificial intelligence model. For example, it can predict the supply of agricultural products for the next year and evaluate the supply-demand balance in food delivery services. It also uses generative artificial intelligence to generate simulation results based on these predictions in visual and text formats. This could include, for example, optimizing delivery routes and suggesting delivery schedules.

[0829] Users view simulation results through their devices and provide feedback. The server collects and analyzes user sentiment information using speech recognition and natural language processing technologies. The sentiment engine flexibly adjusts the scenario based on user feedback and suggests the optimal course of action.

[0830] For example, a user might provide the following feedback:

[0831] "Today's delivery was delayed, but the quality was good. I wish it could be delivered a little faster."

[0832] In response to this feedback, the server performs sentiment recognition and analyzes whether the delivery delay is causing dissatisfaction with the user. Based on this, the delivery plan is optimized again to improve service to the user.

[0833] This enables real-time supply and demand forecasting and supply planning for food delivery services, as well as flexible service delivery tailored to user preferences. The system aims to improve the efficiency of food delivery and enhance user satisfaction.

[0834] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0835] Step 1:

[0836] The server collects necessary data, such as crop production data and weather data, via APIs or database queries. This provides the production and weather data to be used as input data. This data is then used in subsequent preprocessing steps.

[0837] Step 2:

[0838] The server preprocesses the collected data. Specifically, it cleans and normalizes the data to prepare it for analysis. The input to this step is the data collected in step 1, and the output is the cleaned data. This includes imputing missing values ​​and removing outliers.

[0839] Step 3:

[0840] The server uses a trained predictive artificial intelligence model to analyze preprocessed data and predict future scenarios. The input for this step is cleaned data, and the output is a predicted supply quantity. Examples include predicting crop supply quantities or evaluating supply-demand balance in food delivery services.

[0841] Step 4:

[0842] The server uses generative artificial intelligence to generate simulation results based on predicted scenarios in both visual and textual formats. The input for this step is the predicted scenario, and the output is visualized simulation results and a text report. Examples include optimizing delivery routes and suggesting delivery schedules.

[0843] Step 5:

[0844] The user reviews the simulation results generated by the server through their terminal and provides feedback. A concrete example of feedback might be, "Today's delivery was delayed, but the quality was good. I wish it could be delivered a little earlier." The input for this step is the visualized simulation results, and the output is user feedback.

[0845] Step 6:

[0846] The server uses speech recognition and natural language processing technologies to collect and analyze emotional information from user feedback. The input for this step is user feedback, and the output is user emotional information. The emotional recognition engine analyzes user stress and dissatisfaction to help adjust the system.

[0847] Step 7:

[0848] The server adjusts the scenario based on the user's sentiment information and suggests the optimal course of action. The input for this step is the user's sentiment information, and the output is the adjusted simulation results and new proposed solutions. For example, it might suggest re-optimizing the delivery plan or improving the service.

[0849] Step 8:

[0850] Ultimately, after the optimal scenario is determined by the user, the server creates a specific execution plan based on that scenario. The input to this step is the adjusted scenario, and the output is the execution plan. The execution plan includes detailed tasks, schedules, resource allocations, and more.

[0851] 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.

[0852] 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.

[0853] 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.

[0854] [Third Embodiment]

[0855] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0856] 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.

[0857] 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).

[0858] 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.

[0859] 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.

[0860] 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).

[0861] 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.

[0862] 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.

[0863] 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.

[0864] 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.

[0865] 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.

[0866] 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".

[0867] The system according to the present invention proposes optimal solutions to social issues through data collection, preprocessing, prediction, generation, and interactive modification of scenarios. This system aims to efficiently and effectively address challenges faced by national and local governments, municipalities, and corporations (e.g., population decline, disaster countermeasures, food crises, etc.).

[0868] System Overview

[0869] 1. Data Collection

[0870] The server accesses various data sources provided by national and local governments, municipalities, and corporations to collect the necessary big data. This data collection includes using APIs and querying databases. Examples of data that may be collected include demographic data, disaster risk data, and agricultural production data.

[0871] 2. Preprocessing

[0872] The server cleans the collected data, imputing outliers and missing values. It also normalizes the data, converting each data point into a unified format that is easy to analyze.

[0873] 3. Analysis using predictive AI

[0874] The server uses a trained predictive artificial intelligence model to forecast future scenarios based on collected data. For example, it estimates population decline, predicts food production, and assesses disaster risk. Furthermore, it generates proposed countermeasures based on these predictions and evaluates their effectiveness.

[0875] 4. Simulation using generative AI

[0876] The server uses a generative artificial intelligence model to visually represent prediction results. For example, it can generate simulated images of future cityscapes and detailed text reports. This allows users to visualize concrete scenarios.

[0877] 5. User-initiated interactive scenario changes

[0878] Users can view the provided simulation results on their devices and modify the scenarios and proposed solutions as needed. For example, they might expand the scale of urban agriculture or change the focus of disaster countermeasures. The changes made by the user are sent to the server, where the analysis and simulation are performed again.

[0879] 6. Determining and Implementing the Optimal Scenario

[0880] Based on user feedback, the server re-analyzes and generates data to propose the optimal solution. Based on this optimal solution, a concrete implementation plan is formulated and provided to the user. This implementation plan includes specific action items, schedules, resource allocation, etc.

[0881] Specific example

[0882] Predicting and countermeasures for food crises

[0883] 1. Data Collection

[0884] The server collects agricultural production data, weather data, and transportation infrastructure data. Data collection utilizes various APIs and queries from databases.

[0885] 2. Preprocessing

[0886] The server cleans and normalizes the collected data, preparing it for analysis. Missing data is appropriately imputed.

[0887] 3. Analysis using predictive AI

[0888] The server uses a trained predictive model to forecast crop yields for the next year. Based on this forecast, it calculates the probability of food shortages occurring and generates several proposed countermeasures (e.g., imports from other regions, promotion of urban agriculture, etc.).

[0889] 4. Simulation using generative AI

[0890] The server uses generative AI to visualize the urban landscape after the introduction of urban agriculture. It also generates a detailed text report of proposed countermeasures.

[0891] 5. User-initiated interactive scenario changes

[0892] The user reviews the simulation results provided on their device and adjusts the scale of urban agriculture and import plans. The modified scenario is then sent to the server.

[0893] 6. Determining and Implementing the Optimal Scenario

[0894] The server re-analyzes the correction scenario and provides the user with the optimal solution. The user then determines the final scenario, develops a specific implementation plan, and executes it as a project.

[0895] This system enables a dynamic and adaptive approach to addressing social challenges by integrating multiple data collection methods and advanced artificial intelligence technologies.

[0896] The following describes the processing flow.

[0897] Step 1: Data Collection

[0898] The server collects various types of data provided by national, local, municipal, and corporate entities using APIs and database queries. This data includes demographic data, disaster risk data, and agricultural production data.

[0899] Step 2: Data Preprocessing

[0900] The server cleans the collected raw data. Specifically, it detects missing or outlier values ​​and performs interpolation or removal as needed.

[0901] The server standardizes and normalizes the format of the cleaned data, ensuring consistency between data points.

[0902] Step 3: Training the AI ​​model

[0903] The server uses normalized data to train predictive artificial intelligence models. For example, it uses regression models for population forecasting and time-series models for disaster risk prediction.

[0904] The server evaluates and optimizes the model's accuracy using techniques such as cross-validation.

[0905] Step 4: Generating Prediction Scenarios

[0906] The server uses a pre-trained predictive artificial intelligence model to forecast future scenarios. For example, it can predict the rate of population decline, the risk of disasters, and food production for the next year.

[0907] The server generates proposed countermeasures based on the prediction results and evaluates the effectiveness and risks of each.

[0908] Step 5: Simulation using generative AI

[0909] The server uses generative artificial intelligence (image generation AI, language generation AI) to represent predictive scenarios visually and as text. For example, it generates simulated images of urban landscapes after the introduction of urban agriculture and detailed reports.

[0910] Step 6: Provide simulation results

[0911] The server provides the user with the generated simulation results. The user views these results on their terminal and evaluates the effectiveness of the scenarios and proposed solutions.

[0912] Step 7: User changes scenario

[0913] Based on the simulation results, users can modify scenarios and proposed countermeasures on their devices. For example, they might expand the scale of urban agriculture or change the focus of disaster countermeasures.

[0914] The scenario modified by the user is sent from the terminal to the server.

[0915] Step 8: Reanalysis and resimulation

[0916] The server then uses predictive and generative AI to perform analysis and simulation again based on the revised scenario submitted by the user.

[0917] The server generates new simulation results and provides them to the user.

[0918] Step 9: Determining the Optimal Scenario

[0919] The user views the regenerated simulation results and determines the optimal scenario.

[0920] The server creates a specific execution plan based on the optimal scenario. This includes task details, schedule, and resource allocation.

[0921] Step 10: Implement the action plan

[0922] Based on the decided execution plan, users take specific actions, such as starting a project or allocating resources.

[0923] (Example 1)

[0924] 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."

[0925] Currently, there is a lack of systems that can quickly and effectively propose optimal solutions to social challenges faced by national and local governments, municipalities, and corporations (e.g., population decline, disaster countermeasures, food crises, etc.). In particular, there is a need for an integrated platform to collect large amounts of data, process outliers and missing values, normalize the data, predict future scenarios based on that data, and perform visual simulations. Furthermore, there is a need for a system that allows users to interactively modify proposed solutions and determine the optimal solution scenario.

[0926] 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.

[0927] In this invention, the server includes data collection means, preprocessing means, means for analyzing data using predictive artificial intelligence, means for generating simulation results using generative artificial intelligence, means for interactively changing scenarios, means for reanalyzing based on user feedback, determining the optimal scenario, and creating an execution plan, and means for displaying simulation results on a terminal. This allows the user to collect various types of data, perform preprocessing, and interactively modify and determine the optimal countermeasures through advanced prediction and simulation.

[0928] "Data collection methods" refer to means for efficiently collecting necessary data from various data sources, such as via APIs or database queries.

[0929] "Preprocessing means" refers to means of cleaning the collected data, detecting and removing outliers, and normalizing the data in order to improve the quality of the collected data.

[0930] A "predictive artificial intelligence method" is a means of using a trained predictive artificial intelligence model to predict future scenarios and generate countermeasures based on pre-processed data.

[0931] A "generative artificial intelligence means" is a means of generating visual simulation results based on prediction results using a generative artificial intelligence model.

[0932] "An interactive means of changing the scenario" refers to a method that allows users to view the provided simulation results on their device and modify the scenario or proposed countermeasures as needed.

[0933] "A means of re-analyzing based on user feedback, determining the optimal scenario, and creating an implementation plan" refers to a method of re-analyzing and generating data based on user modifications, proposing the optimal countermeasures, and creating an implementation plan.

[0934] "Means for displaying simulation results on a terminal" refers to means for displaying the generated simulation results on the user's terminal and providing them to the user.

[0935] The system according to the present invention proposes optimal solutions to social issues through data collection, preprocessing, prediction, generation, and interactive modification of scenarios. This system aims to efficiently and effectively address challenges faced by national and local governments, municipalities, and corporations (e.g., population decline, disaster countermeasures, food crises, etc.).

[0936] Data collection

[0937] The server accesses various data sources provided by national and local governments, municipalities, and corporations to collect necessary big data. This data collection includes using APIs and querying databases. Specifically, high-performance server equipment is used as hardware, and the software utilizes libraries (for example, Python's Requests) that enable access to various API endpoints. Examples of target data include demographic data, disaster risk data, and agricultural production data.

[0938] Pre-treatment

[0939] The server cleans the collected data, imputing outliers and missing values. It also normalizes the data, arranging each data point into a unified format. This transforms the data into a format that is easy to analyze. Specifically, it uses the Python Pandas library for data preprocessing.

[0940] Analysis using predictive artificial intelligence

[0941] The server uses a pre-trained predictive artificial intelligence model to forecast future scenarios based on collected data. For example, it can estimate population decline, predict food production, and assess disaster risk. Furthermore, it generates countermeasures based on these predictions and evaluates their effectiveness. Machine learning libraries such as TensorFlow and Scikit-Learn can be used to implement the predictive AI model.

[0942] Simulation using generative artificial intelligence

[0943] The server uses generative artificial intelligence models to visually represent prediction results. For example, it generates simulated images of future cityscapes and detailed text reports. This allows users to visualize concrete scenarios. The generative models utilize generative AI technologies such as GANs (Generative Adversarial Networks).

[0944] User-interactive scenario changes

[0945] Users view the provided simulation results on their devices and modify the scenarios and proposed solutions as needed. For example, they might expand the scale of urban agriculture or change the focus of disaster countermeasures. The changes made by the user are sent to the server, where the analysis and simulation are performed again. The user's device has a web interface implemented to display the simulation results, using JavaScript, HTML, and CSS.

[0946] Determining and executing the optimal scenario

[0947] Based on user feedback, the server re-analyzes and generates data to propose the optimal solution. Based on this optimal solution, a concrete implementation plan is formulated and provided to the user. This plan includes specific action items, schedules, and resource allocations. The generated implementation plan is provided as a PDF report, and a download link is displayed on the user's device.

[0948] Specific example: Predicting and countermeasures for food crises

[0949] 1. Data Collection

[0950] The server collects agricultural production data, weather data, and transportation infrastructure data. Data collection utilizes various APIs and queries from databases.

[0951] 2. Preprocessing

[0952] The server cleans and normalizes the collected data, preparing it for analysis. Missing data is appropriately imputed.

[0953] 3. Analysis using predictive AI

[0954] The server uses a trained predictive model to forecast crop yields for the next year. Based on this forecast, it calculates the probability of food shortages occurring and generates several proposed countermeasures (e.g., imports from other regions, promotion of urban agriculture, etc.).

[0955] 4. Simulation using generative AI

[0956] The server uses generative AI to visualize the urban landscape after the introduction of urban agriculture. It also generates a detailed text report of proposed countermeasures.

[0957] 5. User-initiated interactive scenario changes

[0958] The user reviews the simulation results provided on their device and adjusts the scale of urban agriculture and import plans. The modified scenario is then sent to the server.

[0959] 6. Determining and Implementing the Optimal Scenario

[0960] The server re-analyzes the corrected scenario and provides the user with the optimal solution. The user then determines the final scenario, develops a specific implementation plan, and executes it as a project.

[0961] Example of a prompt

[0962] "Design a system that predicts food crises and generates optimal countermeasures. This involves collecting, cleaning, and normalizing agricultural production data, weather data, and transportation infrastructure data. The predictive model calculates the probability of food shortages and generates countermeasures. The generative model visualizes the urban landscape after the implementation of urban agriculture, allowing users to interactively change scenarios. The system then generates optimal countermeasures again and proposes concrete action plans."

[0963] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0964] Program processing flow

[0965] Step 1: Prepare for data collection

[0966] The server prepares to access various data sources provided by national and local governments, municipalities, corporations, and other organizations.

[0967] The required inputs are the API endpoint to be accessed and the authentication information.

[0968] Specifically, the process involves preparing the API URL and authentication token, and then using the Python Requests library to prepare for the connection.

[0969] As an output, a connection to the data source to be collected is established.

[0970] Step 2: Obtaining the required information

[0971] The server collects data through an API. It queries the database directly to retrieve the necessary information.

[0972] The input requires request parameters to be sent to the API endpoint.

[0973] Specifically, the Requests library is used to send a GET request and retrieve response data in JSON format.

[0974] As output, the collected raw data is stored in temporary storage.

[0975] Step 3: Data Cleaning

[0976] The server performs data cleaning to improve the quality of the collected data. This includes detecting and removing outliers and imputing missing values.

[0977] Raw data is required as input.

[0978] Specifically, the process involves using the Python Pandas library to impute missing values ​​and remove outliers.

[0979] The output will be cleaned data.

[0980] Step 4: Data Normalization

[0981] The server performs data normalization to unify data in different formats and units.

[0982] Cleaned data is required as input.

[0983] Specifically, the process involves converting all numerical data to a common unit and standardizing the data format.

[0984] The output will be normalized data.

[0985] Step 5: Loading the predictive model

[0986] The server loads a pre-trained predictive artificial intelligence model.

[0987] The input requires the path to the model file.

[0988] Specifically, the process involves loading the model from disk into memory using TensorFlow or Scikit-Learn.

[0989] As an output, the predictive model is loaded into memory.

[0990] Step 6: Run the forecast

[0991] The server inputs pre-processed data into a predictive model to forecast future scenarios.

[0992] The input requires normalized data and a loaded predictive model.

[0993] In terms of specific operations, the model's predict function is called to obtain the prediction result.

[0994] The output will be the prediction results.

[0995] Step 7: Loading the Generative Model

[0996] The server loads a generative artificial intelligence model for visualization.

[0997] The path to the generated model file is required as input.

[0998] Specifically, the process involves loading the GAN model from disk into memory.

[0999] As output, the generative model is loaded into memory.

[1000] Step 8: Run the simulation

[1001] The server generates a visual simulation based on the prediction results.

[1002] The input requires the prediction results and the loaded generative model.

[1003] In terms of specific operations, the prediction results are input into a generative model, and simulated images and text reports are generated.

[1004] The output is a visualized simulation result.

[1005] Step 9: Displaying the simulation results

[1006] Users can view the simulation results through their device.

[1007] The generated simulation results are required as input.

[1008] Specifically, a user interface (UI) is provided to display the simulation results in a web browser.

[1009] The user will see the simulation results as output.

[1010] Step 10: Scenario Revision

[1011] Users can interactively modify proposed solutions and scenarios.

[1012] The displayed simulation results are required as input.

[1013] In terms of operation, parameters are adjusted via a web interface, and those changes are sent to the server.

[1014] The revised scenario is generated as output.

[1015] Step 11: Feedback Analysis

[1016] The server will perform another analysis based on the user's feedback.

[1017] A modified scenario is required as input.

[1018] Specifically, the process involves running the prediction and simulation again using the new parameter set.

[1019] The output will include re-analyzed prediction results and simulation results.

[1020] Step 12: Provide an execution plan

[1021] The server develops a concrete implementation plan based on the optimal countermeasure and provides it to the user.

[1022] The input requires re-analyzed prediction results and simulation results.

[1023] Specifically, the system generates an execution plan as a PDF report and provides a download link to the user's device.

[1024] As output, a specific execution plan is provided to the user.

[1025] (Application Example 1)

[1026] 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."

[1027] Traditional logistics center operations management is fraught with numerous variables and uncertainties, making efficient inventory management and optimal picking route planning difficult. Furthermore, real-time data analysis and immediate feedback are challenging, resulting in limited operational flexibility. This can lead to human error, wasted resources, and decreased productivity. A system is needed to address these challenges and improve the efficiency of logistics operations.

[1028] 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.

[1029] In this invention, the server includes means for collecting data, means for preprocessing data, means for analyzing data using predictive artificial intelligence, means for generating simulation results using generative artificial intelligence, means for interactively changing scenarios, means for determining the optimal scenario and creating an execution plan, means for supporting the optimization of logistics operations, means for displaying inventory data and transportation information in real time, and means for modifying scenarios via smart glasses. This makes it possible to analyze data in real time and realize optimal logistics operations.

[1030] "Data collection means" refers to devices or systems used to collect necessary data via APIs or database queries.

[1031] "Preprocessing means" refers to devices or systems that clean, correct for outliers, and normalize collected data, and convert it into a format suitable for analysis.

[1032] A "predictive artificial intelligence tool" is a device or system that uses a pre-trained predictive model to predict future scenarios and countermeasures based on collected data.

[1033] A "generative artificial intelligence means" is a device or system that visually generates simulation results based on prediction results and provides them to the user.

[1034] "Means of interactively modifying scenarios" refer to devices or systems that allow users to view simulation results and modify scenarios or countermeasures as needed.

[1035] "Means for determining the optimal scenario and creating an implementation plan" refers to devices or systems that formulate optimal countermeasures and specific implementation plans based on user feedback.

[1036] "Means to support the optimization of logistics operations" refer to devices and systems that streamline operations within a logistics center and propose optimal inventory management and picking routes.

[1037] "Means for displaying inventory data and transportation information in real time" refers to devices and systems that display inventory data and transportation information collected within a logistics center in real time, thereby visualizing the situation.

[1038] "Means of modifying scenarios via smart glasses" refers to devices or systems that enable the use of smart glasses during logistics operations to modify scenarios and countermeasures on the spot through visual information and voice instructions.

[1039] This invention relates to a system that streamlines operations within a logistics center and proposes optimal inventory management and picking routes. The following describes specific embodiments for carrying out the invention.

[1040] Hardware and software to use

[1041] This system utilizes cloud servers, smart glasses, AI models, and a real-time database.

[1042] Cloud servers: Common cloud infrastructure such as Amazon Web Services and Google Cloud.

[1043] Smart glasses: For example, a typical smart glasses device.

[1044] Software: Libraries and tools used for data processing and analysis, such as Python, pandas, scikit-learn, and Plotly.

[1045] Data processing and data calculation

[1046] 1. Data collection methods:

[1047] The server retrieves inventory and transportation information from the distribution center by querying it via API or from the database. This data includes product IDs, inventory quantities, and inbound / outbound timestamps.

[1048] 2. Pre-treatment means:

[1049] The server cleans the collected data, imputing missing values ​​and correcting outliers. It also normalizes the data and converts it into a format suitable for analysis.

[1050] 3. Predictive artificial intelligence methods:

[1051] The server uses trained predictive AI models (such as Linear Regression or Deep Learning models) to forecast demand from collected data. This enables optimal inventory management and picking route planning.

[1052] 4. Generative artificial intelligence methods:

[1053] Based on the prediction results, an AI model is used to generate visual simulation results. For example, a visual display showing changes in shelf placement or new picking routes is created.

[1054] 5. Means of interactively changing the scenario:

[1055] Users can review the simulation results provided through smart glasses and modify the scenario via voice prompts and visual information. For example, they might use a prompt such as, "Show me next week's inventory forecast and suggest the optimal picking order."

[1056] 6. Means for determining the optimal scenario and creating an execution plan:

[1057] The server re-analyzes the user feedback and develops optimal countermeasures and a specific implementation plan. This plan includes specific action items, schedules, and resource allocations.

[1058] Specific example

[1059] For example, a newly implemented system in a logistics center uses prompt messages like the following:

[1060] "Please display the inventory forecast for next week and suggest the optimal picking order."

[1061] Following this prompt, the server analyzes the data in real time and presents the optimal scenario to the user through smart glasses. The user reviews the view and modifies the scenario as needed. This allows for efficient management of operations within the logistics center.

[1062] This system can optimize complex logistics operations in real time, significantly improving employee work efficiency.

[1063] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1064] Step 1:

[1065] The server collects inventory and transportation information from the logistics center via APIs and databases. Specific actions performed by the server include sending requests to API endpoints and executing SQL queries. This inputs raw data such as product IDs, inventory quantities, and inbound / outbound timestamps, which are then stored in the database.

[1066] Step 2:

[1067] The server preprocesses the collected data. Specifically, it cleans the data (imputing missing values, correcting outliers) and normalizes it. The input is the raw data collected in step 1, which is then converted to a unified format. The output is cleaned data suitable for analysis.

[1068] Step 3:

[1069] The server analyzes pre-processed data and performs demand forecasting using a trained predictive artificial intelligence model. The input is the cleaned data from step 2, and the output is future demand forecast data. Specifically, this involves inputting data into a predictive model (e.g., Linear Regression or Deep Learning model) and retrieving results.

[1070] Step 4:

[1071] The server uses a generative artificial intelligence model to generate visual simulation results based on the prediction results. The input is the demand forecast data from step 3, and the output is a visual representation of the simulation (e.g., rearranged shelves or new picking routes). The specific operations include inputting data into the generative model and generating visual content.

[1072] Step 5:

[1073] The user reviews the simulation results provided through smart glasses and makes necessary changes interactively using prompts. The input is the simulation results from step 4, and the user's modification feedback is taken into consideration. Specific actions include scenario changes based on voice prompts and visual information.

[1074] Step 6:

[1075] The server reanalyzes the data based on user feedback and develops optimal countermeasures and specific implementation plans. The input is the user correction feedback from step 5, and the output is the final countermeasures and implementation plan. Specific actions include reanalysis incorporating the feedback and creation of an implementation plan.

[1076] Step 7:

[1077] The server deploys the finalized execution plan to each piece of equipment in the logistics operation, optimizing the entire system. The input is the execution plan from step 6, and the output is the optimized state of the logistics operation. Specific actions include sending instructions to transport robots and picking robots.

[1078] 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.

[1079] The system according to the present invention provides optimized solutions to social issues by combining data collection, preprocessing, prediction, generation, and interactive scenario modification with an emotion engine that recognizes user emotions. This system aims to respond in real time and adaptively to complex challenges faced by national and local governments, municipalities, and corporations (e.g., population decline, disaster countermeasures, food crises, etc.).

[1080] System Overview

[1081] 1. Data Collection

[1082] The server collects various types of data provided by national, local, municipal, and corporate entities via APIs and database queries. This data includes demographic data, disaster risk data, and agricultural production data.

[1083] 2. Preprocessing

[1084] The server cleans the collected data, detects outliers and missing values, and performs interpolation or removal as needed.

[1085] The server normalizes the data and prepares it for analysis.

[1086] 3. Analysis using predictive AI

[1087] The server uses a pre-trained predictive artificial intelligence model to forecast future scenarios. For example, it can predict the rate of population decline, the risk of disasters, and food production for the next year.

[1088] The server generates proposed countermeasures based on the prediction results and evaluates the effectiveness and risks of each.

[1089] 4. Simulation using generative AI

[1090] The server uses generative artificial intelligence (e.g., image generation AI, language generation AI) to represent predictive scenarios visually and in text. For example, it can generate simulated images of urban landscapes after the introduction of urban agriculture and detailed reports.

[1091] 5. User-initiated interactive scenario changes

[1092] Users view the provided simulation results on their devices and modify scenarios and proposed countermeasures based on their feedback. Examples include scenarios that expand the scale of urban agriculture or changes in the focus of disaster countermeasures.

[1093] User modifications are sent from the terminal to the server, where they are analyzed and simulated again.

[1094] 6. Emotion recognition by an emotion engine

[1095] The server uses an emotion engine to collect and analyze emotional information during user feedback and interactions. It uses speech recognition and natural language processing technologies to evaluate the user's emotional state.

[1096] 7. Emotion-based scenario adjustment

[1097] The server adjusts scenarios and proposed solutions based on emotional information obtained from the emotion engine. If the user is experiencing stress, the system takes action such as providing more concise and easy-to-understand information.

[1098] 8. Determining and Implementing the Optimal Scenario

[1099] The user determines the optimal scenario based on the regenerated scenarios.

[1100] The server creates and provides the user with a specific execution plan based on the optimal scenario. This includes detailed tasks, schedules, and resource allocations.

[1101] The user takes specific actions based on the decided execution plan.

[1102] Specific example

[1103] Predicting and countermeasures for food crises

[1104] 1. Data Collection

[1105] The server collects agricultural production data, weather data, and transportation infrastructure data. Data collection utilizes various APIs and queries from databases.

[1106] 2. Preprocessing

[1107] The server cleans and normalizes the collected data and appropriately imputes missing data.

[1108] 3. Analysis using predictive AI

[1109] The server uses a predictive AI model to forecast crop production for the next year, assess the risk of food shortages, and generate multiple countermeasures.

[1110] 4. Simulation using generative AI

[1111] The server uses generative AI to visualize the urban landscape after the introduction of urban agriculture and generates a detailed text report of proposed countermeasures.

[1112] 5. User-initiated scenario changes

[1113] Users review the simulation results on their terminal and adjust the scale of urban agriculture and import plans. The revised scenario is then sent to the server.

[1114] 6. Emotion recognition by an emotion engine

[1115] The server collects and analyzes emotional information using speech recognition and natural language processing during user reviews. For example, if a user is feeling anxious, the server uses that information to soften the explanation of the suggested solutions.

[1116] 7. Emotion-based scenario adjustment

[1117] The server fine-tunes the scenario based on information obtained from the emotion engine, presenting solutions in a way that is more convincing to the user.

[1118] 8. Determining and Implementing the Optimal Scenario

[1119] The user selects the optimal countermeasure based on the regenerated scenario, and the server creates an execution plan based on that scenario.

[1120] The user takes specific actions (e.g., starting a project, allocating resources) based on the determined execution plan.

[1121] By combining this system with an emotion engine, it can perform interactive scenario changes and optimizations that take into account the user's emotional state, enabling it to provide more effective and user-friendly solutions.

[1122] The following describes the processing flow.

[1123] Step 1: Data Collection

[1124] The server collects various types of data provided by national, local, municipal, and corporate entities via APIs or database queries. For example, it retrieves demographic data, disaster risk data, and agricultural production data.

[1125] Step 2: Data Preprocessing

[1126] The server cleans the collected data, detects missing or outlier values, and imputes or removes them if necessary.

[1127] The server normalizes the cleaned data and prepares it for analysis.

[1128] Step 3: Training the AI ​​model

[1129] The server uses normalized data to train predictive artificial intelligence models. For example, it uses regression models for population forecasting and time-series models for disaster risk prediction.

[1130] The server evaluates and optimizes the model's accuracy using cross-validation techniques.

[1131] Step 4: Generating Prediction Scenarios

[1132] The server uses a trained predictive artificial intelligence model to forecast future scenarios. For example, it can predict the rate of population decline, the risk of disasters, and food production for the next year.

[1133] The server generates multiple countermeasures based on the prediction results and evaluates the effectiveness and risks of each.

[1134] Step 5: Simulation using generative AI

[1135] The server uses generative artificial intelligence (image generation AI and language generation AI) to represent predictive scenarios visually and in text. For example, it generates simulated images of urban landscapes after the introduction of urban agriculture and detailed reports.

[1136] Step 6: Provide simulation results

[1137] The server provides the user with the generated simulation results. The user views these results on their terminal and evaluates the effectiveness of the scenarios and proposed solutions.

[1138] Step 7: Emotion recognition by the emotion engine

[1139] The server uses an emotion engine to detect user feedback and emotions during interactions. It analyzes the user's emotional state using speech recognition and natural language processing technologies.

[1140] Step 8: Emotion-based scenario adjustments

[1141] The server uses emotional information obtained from the emotion engine to adjust scenarios and suggested solutions. For example, if the user is feeling stressed, it will provide more concise and easy-to-understand information.

[1142] Step 9: User changes scenario

[1143] Users modify scenarios and proposed solutions on their devices based on simulation results and their emotions. For example, they might expand the scale of urban agriculture or change the focus of disaster preparedness.

[1144] The scenario modified by the user is sent from the terminal to the server.

[1145] Step 10: Reanalysis and resimulation

[1146] The server then uses predictive and generative AI to perform analysis and simulation again based on the revised scenario submitted by the user.

[1147] The server generates new simulation results and provides them to the user.

[1148] Step 11: Determining the Optimal Scenario

[1149] The user determines the optimal scenario based on the regenerated simulation results.

[1150] The server creates a specific execution plan based on the optimal scenario. This includes detailed tasks, schedules, and resource allocations.

[1151] Step 12: Implement the Action Plan

[1152] Based on the decided execution plan, the user takes specific actions (such as starting a project or allocating resources).

[1153] (Example 2)

[1154] 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."

[1155] Traditional solutions have struggled to provide effective solutions to the complex and diverse challenges facing society (e.g., population decline, disaster preparedness, food crises) due to their lack of real-time response capabilities and adaptability. Furthermore, the difficulty in incorporating user emotions and feedback into scenario adjustments has resulted in a lack of user-friendly solutions.

[1156] 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.

[1157] In this invention, the server includes means for collecting data, means for preprocessing data, means for analyzing data using predictive artificial intelligence, means for generating simulation results using generative artificial intelligence, means for interactively changing scenarios, means for collecting and analyzing feedback using an emotion recognition engine, means for adjusting scenarios based on emotion information, and means for determining the optimal scenario and creating an execution plan. This enables real-time analysis and simulation of collected data, realizes interactive scenario adjustments that take user emotions into consideration, and enables the provision of more effective and user-friendly solutions.

[1158] "Means of collecting data" refers to the system's function of obtaining necessary data from external sources using APIs or database queries.

[1159] "Means of preprocessing" refers to the system's function of cleaning collected data, imputing or removing outliers and missing values, and normalizing the data to prepare it for analysis.

[1160] "Methods for analyzing data using predictive artificial intelligence" refers to the functionality of a system that uses a trained AI model to predict future scenarios and risks from collected and pre-processed data.

[1161] "Means for generating simulation results using generative artificial intelligence" refers to the functionality of a system that uses image generation AI and language generation AI to represent predicted scenarios visually and as text.

[1162] "An interactive means of modifying the scenario" refers to a system function that allows users to view simulation results via a terminal, modify the scenario and proposed countermeasures based on feedback, and send those modifications to the server.

[1163] "Means for collecting and analyzing feedback using an emotion recognition engine" refers to a system function that uses speech recognition and natural language processing technologies to evaluate the user's emotional state and analyze the feedback.

[1164] "Means of adjusting scenarios based on emotional information" refers to a system function that adjusts scenarios and proposed solutions in a user-friendly manner based on user emotional information obtained from an emotional recognition engine.

[1165] "Means for determining the optimal scenario and creating an execution plan" refers to a system function that determines the optimal scenario based on the regenerated scenarios, creates a specific execution plan (tasks, schedule, resource allocation) based on that scenario, and provides it to the user.

[1166] This invention provides a system that consistently performs data acquisition, preprocessing, prediction, simulation, scenario modification, sentiment recognition, and optimal scenario determination. This system operates through the cooperation of a server, terminals, and users.

[1167] System configuration and operation

[1168] Data collection

[1169] The server collects necessary data from multiple data sources using APIs and database queries. This data includes demographics, crop production, and disaster risk information. Specifically, the server sends requests to API endpoints and retrieves the data returned as responses in JSON format.

[1170] Pre-treatment

[1171] The server cleans the collected data, detecting and imputing outliers and missing values. Each dataset is normalized and prepared for analysis. Specifically, missing values ​​are imputed with the data's mean, and the data range is scaled from 0 to 1.

[1172] Analysis using predictive AI

[1173] The server uses a pre-trained predictive AI model to forecast future scenarios. This includes forecasts of the next year's population decline rate, disaster risk, and food production volume. The server inputs data into the AI ​​model and outputs the prediction results. The pre-trained model may include a deep learning-based model.

[1174] Simulation using generative AI

[1175] The server uses a generative AI model to represent predictive scenarios in visual and textual formats. For example, it can generate simulated images of urban landscapes after the introduction of urban agriculture, or generate detailed action plan reports. At this time, the server will input prompts such as the following:

[1176] Prompt: "Based on forecasts of next year's crop production and an assessment of the resulting risk of food shortages, conduct a simulation of what happens after the introduction of urban agriculture, and generate the results as a visualization and a detailed text report."

[1177] User-interactive scenario changes

[1178] Users view the simulation results provided on their devices and modify the scenarios and proposed countermeasures. For example, they might adjust the scale of urban agriculture or change the focus of disaster response. The modified scenarios are sent from the device to the server for reanalysis and resimulation.

[1179] Emotion recognition by an emotion engine

[1180] The server uses an emotion engine to collect and analyze emotional information during user feedback and interactions. It evaluates the user's emotional state using speech recognition and natural language processing technologies, and makes adjustments based on the collected emotional information to reduce the user's anxiety and stress.

[1181] Emotion-based scenario adjustment

[1182] The server adjusts the scenario based on emotional information obtained from the emotion engine. For example, if the user is feeling stressed, the system will provide more concise and easy-to-understand information.

[1183] Determining and executing the optimal scenario

[1184] The user determines the optimal scenario based on the regenerated scenarios. The server creates a specific execution plan (tasks, schedule, resource allocation) based on that scenario. The user then takes specific actions according to the execution plan.

[1185] Thus, this system can respond in real time, taking user emotions into consideration, from data collection to the execution of optimal scenarios. Through close collaboration between the server, terminal, and user at each processing step, effective solutions to complex social issues are provided.

[1186] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1187] Step 1: Data Collection

[1188] The server uses APIs and database queries to collect the necessary data from multiple data sources.

[1189] Input: API endpoint or database query

[1190] Specific operation: The server sends a request to the API endpoint "https: / / api.example.com / population_stats" and retrieves demographic data in JSON format as a response.

[1191] Output: Collected data

[1192] Step 2: Pretreatment

[1193] The server cleans the collected data, detecting and imputing or removing outliers and missing values.

[1194] Input: Collected data

[1195] Specific operation: The server performs a data scan and imputes missing data with the mean value. It normalizes the data and prepares it for analysis.

[1196] Output: Preprocessed data

[1197] Step 3: Analysis using predictive AI

[1198] The server inputs the pre-processed data into a trained predictive AI model to predict future scenarios.

[1199] Input: Preprocessed data

[1200] Specific operation: The server inputs data into a predictive model and forecasts the population decline rate, disaster risk, and food production volume for the next year. Based on the prediction results, it generates proposed countermeasures and evaluates their effectiveness.

[1201] Output: Prediction results and proposed countermeasures

[1202] Step 4: Simulation using generative AI

[1203] The server uses a generative AI model based on the prediction results to represent the simulation results in both visual and textual formats.

[1204] Input: Prediction results and proposed countermeasures

[1205] Specific operation: The server inputs prompt text into the generative AI, which then generates simulated images of urban agriculture after its implementation and a detailed report of proposed countermeasures.

[1206] Prompt: "Based on forecasts of next year's crop production and an assessment of the resulting risk of food shortages, conduct a simulation of what happens after the introduction of urban agriculture, and generate the results as a visualization and a detailed text report."

[1207] Output: Simulation results, simulated images, and proposed countermeasures report.

[1208] Step 5: User-initiated interactive scenario changes

[1209] Users can view the simulation results provided through their device and modify the scenarios and proposed solutions as needed.

[1210] Input: Simulation results

[1211] Specific operation: The user checks the simulation results on their device and makes adjustments, such as adjusting the scale of urban agriculture. These adjustments are then sent from the device to the server.

[1212] Output: Modified scenario

[1213] Step 6: Emotion recognition by the emotion engine

[1214] The server uses an emotion engine to collect and analyze emotional information during user feedback and interactions.

[1215] Input: User feedback

[1216] Specific operation: The server uses speech recognition and natural language processing technologies to evaluate the user's emotional state and stores the analysis results as emotional information.

[1217] Output: Analyzed sentiment information

[1218] Step 7: Scenario adjustment based on emotional information

[1219] The server adjusts the scenario based on emotional information.

[1220] Input: Analyzed emotional information

[1221] Specific operation: Based on the results of the emotion recognition engine, the server adjusts the scenario and proposed solutions to be user-friendly and provides concise and easy-to-understand information.

[1222] Output: Adjusted scenario

[1223] Step 8: Determine and execute the optimal scenario

[1224] The user determines the optimal scenario based on the regenerated scenarios. The server then creates a specific execution plan based on that scenario and provides it to the user.

[1225] Input: Adjusted scenario

[1226] Specific operation: The user selects the optimal scenario, and the server creates an execution plan (tasks, schedule, resource allocation). The user then performs specific actions according to that plan.

[1227] Output: Optimal execution plan and specific actions

[1228] In this way, the system operates in close coordination at each processing step, from data collection to the execution of the optimal scenario.

[1229] (Application Example 2)

[1230] 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."

[1231] Fluctuations and shortages in food supply necessitate increased efficiency in food delivery services. Furthermore, flexible responses tailored to user needs and emotions are also required. This invention aims to address these challenges by improving the quality and efficiency of food delivery services through real-time prediction and optimization, as well as feedback based on user emotions.

[1232] 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.

[1233] In this invention, the server includes means for collecting data, means for preprocessing data, means for analyzing data using predictive artificial intelligence, means for generating simulation results using generative artificial intelligence, means for recognizing user emotions, means for adjusting scenarios based on user emotions, means for interactively changing scenarios, and means for determining the optimal scenario and creating an execution plan. This enables real-time supply and demand forecasting and supply planning for food delivery services, as well as flexible service provision that responds to user emotions.

[1234] "Means of data collection" refers to methods for collecting necessary data from various sources, including methods via APIs and queries from databases.

[1235] "Means of preprocessing" refers to means of cleaning and normalizing collected data and preparing it into an analyzable format.

[1236] "Methods for analyzing data using predictive artificial intelligence" refer to methods for analyzing collected data and predicting future scenarios using trained artificial intelligence models.

[1237] "Means for generating simulation results using generative artificial intelligence" refers to means for generating simulation results in visual and textual formats based on predicted scenarios.

[1238] "Means of recognizing user emotions" refers to means of collecting and analyzing emotional information during user feedback and interactions, and includes speech recognition and natural language processing technologies.

[1239] "Means of adjusting scenarios based on user emotions" refers to methods for flexibly adjusting scenarios and countermeasures using recognized user emotion information.

[1240] "An interactive means of changing the scenario" refers to a method by which users can view simulation results through their devices and modify the scenario or proposed countermeasures based on their feedback.

[1241] "Means for determining the optimal scenario and creating an execution plan" refers to the means for creating a specific execution plan based on the optimal scenario determined by the user.

[1242] The system according to the present invention aims to optimize food delivery services and improve the user experience. This system includes data collection, preprocessing, data analysis using predictive artificial intelligence, generation of simulation results using generative artificial intelligence, user emotion recognition and scenario adjustment, interactive scenario modification, determination of the optimal scenario, and creation of an execution plan.

[1243] The system will be implemented as follows:

[1244] First, the server collects necessary data, such as crop production data and weather data, via APIs or database queries. This data is then preprocessed, undergoing cleaning and normalization to prepare it for analysis.

[1245] Next, the server analyzes the collected data using a trained predictive artificial intelligence model. For example, it can predict the supply of agricultural products for the next year and evaluate the supply-demand balance in food delivery services. It also uses generative artificial intelligence to generate simulation results based on these predictions in visual and text formats. This could include, for example, optimizing delivery routes and suggesting delivery schedules.

[1246] Users view simulation results through their devices and provide feedback. The server collects and analyzes user sentiment information using speech recognition and natural language processing technologies. The sentiment engine flexibly adjusts the scenario based on user feedback and suggests the optimal course of action.

[1247] For example, a user might provide the following feedback:

[1248] "Today's delivery was delayed, but the quality was good. I wish it could be delivered a little faster."

[1249] In response to this feedback, the server performs sentiment recognition and analyzes whether the delivery delay is causing dissatisfaction with the user. Based on this, the delivery plan is optimized again to improve service to the user.

[1250] This enables real-time supply and demand forecasting and supply planning for food delivery services, as well as flexible service delivery tailored to user preferences. The system aims to improve the efficiency of food delivery and enhance user satisfaction.

[1251] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1252] Step 1:

[1253] The server collects necessary data, such as crop production data and weather data, via APIs or database queries. This provides the production and weather data to be used as input data. This data is then used in subsequent preprocessing steps.

[1254] Step 2:

[1255] The server preprocesses the collected data. Specifically, it cleans and normalizes the data to prepare it for analysis. The input to this step is the data collected in step 1, and the output is the cleaned data. This includes imputing missing values ​​and removing outliers.

[1256] Step 3:

[1257] The server uses a trained predictive artificial intelligence model to analyze preprocessed data and predict future scenarios. The input for this step is cleaned data, and the output is a predicted supply quantity. Examples include predicting crop supply quantities or evaluating supply-demand balance in food delivery services.

[1258] Step 4:

[1259] The server uses generative artificial intelligence to generate simulation results based on predicted scenarios in both visual and textual formats. The input for this step is the predicted scenario, and the output is visualized simulation results and a text report. Examples include optimizing delivery routes and suggesting delivery schedules.

[1260] Step 5:

[1261] The user reviews the simulation results generated by the server through their terminal and provides feedback. A concrete example of feedback might be, "Today's delivery was delayed, but the quality was good. I wish it could be delivered a little earlier." The input for this step is the visualized simulation results, and the output is user feedback.

[1262] Step 6:

[1263] The server uses speech recognition and natural language processing technologies to collect and analyze emotional information from user feedback. The input for this step is user feedback, and the output is user emotional information. The emotional recognition engine analyzes user stress and dissatisfaction to help adjust the system.

[1264] Step 7:

[1265] The server adjusts the scenario based on the user's sentiment information and suggests the optimal course of action. The input for this step is the user's sentiment information, and the output is the adjusted simulation results and new proposed solutions. For example, it might suggest re-optimizing the delivery plan or improving the service.

[1266] Step 8:

[1267] Ultimately, after the optimal scenario is determined by the user, the server creates a specific execution plan based on that scenario. The input to this step is the adjusted scenario, and the output is the execution plan. The execution plan includes detailed tasks, schedules, resource allocations, and more.

[1268] 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.

[1269] 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.

[1270] 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.

[1271] [Fourth Embodiment]

[1272] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1273] 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.

[1274] 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).

[1275] 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.

[1276] 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.

[1277] 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).

[1278] 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.

[1279] 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.

[1280] 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.

[1281] 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.

[1282] 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.

[1283] 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.

[1284] 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".

[1285] The system according to the present invention proposes optimal solutions to social issues through data collection, preprocessing, prediction, generation, and interactive modification of scenarios. This system aims to efficiently and effectively address challenges faced by national and local governments, municipalities, and corporations (e.g., population decline, disaster countermeasures, food crises, etc.).

[1286] System Overview

[1287] 1. Data Collection

[1288] The server accesses various data sources provided by national and local governments, municipalities, and corporations to collect the necessary big data. This data collection includes using APIs and querying databases. Examples of data that may be collected include demographic data, disaster risk data, and agricultural production data.

[1289] 2. Preprocessing

[1290] The server cleans the collected data, imputing outliers and missing values. It also normalizes the data, converting each data point into a unified format that is easy to analyze.

[1291] 3. Analysis using predictive AI

[1292] The server uses a trained predictive artificial intelligence model to forecast future scenarios based on collected data. For example, it estimates population decline, predicts food production, and assesses disaster risk. Furthermore, it generates proposed countermeasures based on these predictions and evaluates their effectiveness.

[1293] 4. Simulation using generative AI

[1294] The server uses a generative artificial intelligence model to visually represent prediction results. For example, it can generate simulated images of future cityscapes and detailed text reports. This allows users to visualize concrete scenarios.

[1295] 5. User-initiated interactive scenario changes

[1296] Users can view the provided simulation results on their devices and modify the scenarios and proposed solutions as needed. For example, they might expand the scale of urban agriculture or change the focus of disaster countermeasures. The changes made by the user are sent to the server, where the analysis and simulation are performed again.

[1297] 6. Determining and Implementing the Optimal Scenario

[1298] Based on user feedback, the server re-analyzes and generates data to propose the optimal solution. Based on this optimal solution, a concrete implementation plan is formulated and provided to the user. This implementation plan includes specific action items, schedules, resource allocation, etc.

[1299] Specific example

[1300] Predicting and countermeasures for food crises

[1301] 1. Data Collection

[1302] The server collects agricultural production data, weather data, and transportation infrastructure data. Data collection utilizes various APIs and queries from databases.

[1303] 2. Preprocessing

[1304] The server cleans and normalizes the collected data, preparing it for analysis. Missing data is appropriately imputed.

[1305] 3. Analysis using predictive AI

[1306] The server uses a trained predictive model to forecast crop yields for the next year. Based on this forecast, it calculates the probability of food shortages occurring and generates several proposed countermeasures (e.g., imports from other regions, promotion of urban agriculture, etc.).

[1307] 4. Simulation using generative AI

[1308] The server uses generative AI to visualize the urban landscape after the introduction of urban agriculture. It also generates a detailed text report of proposed countermeasures.

[1309] 5. User-initiated interactive scenario changes

[1310] The user reviews the simulation results provided on their device and adjusts the scale of urban agriculture and import plans. The modified scenario is then sent to the server.

[1311] 6. Determining and Implementing the Optimal Scenario

[1312] The server re-analyzes the correction scenario and provides the user with the optimal solution. The user then determines the final scenario, develops a specific implementation plan, and executes it as a project.

[1313] This system enables a dynamic and adaptive approach to addressing social challenges by integrating multiple data collection methods and advanced artificial intelligence technologies.

[1314] The following describes the processing flow.

[1315] Step 1: Data Collection

[1316] The server collects various types of data provided by national, local, municipal, and corporate entities using APIs and database queries. This data includes demographic data, disaster risk data, and agricultural production data.

[1317] Step 2: Data Preprocessing

[1318] The server cleans the collected raw data. Specifically, it detects missing or outlier values ​​and performs interpolation or removal as needed.

[1319] The server standardizes and normalizes the format of the cleaned data, ensuring consistency between data points.

[1320] Step 3: Training the AI ​​model

[1321] The server uses normalized data to train predictive artificial intelligence models. For example, it uses regression models for population forecasting and time-series models for disaster risk prediction.

[1322] The server evaluates and optimizes the model's accuracy using techniques such as cross-validation.

[1323] Step 4: Generating Prediction Scenarios

[1324] The server uses a pre-trained predictive artificial intelligence model to forecast future scenarios. For example, it can predict the rate of population decline, the risk of disasters, and food production for the next year.

[1325] The server generates proposed countermeasures based on the prediction results and evaluates the effectiveness and risks of each.

[1326] Step 5: Simulation using generative AI

[1327] The server uses generative artificial intelligence (image generation AI, language generation AI) to represent predictive scenarios visually and as text. For example, it generates simulated images of urban landscapes after the introduction of urban agriculture and detailed reports.

[1328] Step 6: Provide simulation results

[1329] The server provides the user with the generated simulation results. The user views these results on their terminal and evaluates the effectiveness of the scenarios and proposed solutions.

[1330] Step 7: User changes scenario

[1331] Based on the simulation results, users can modify scenarios and proposed countermeasures on their devices. For example, they might expand the scale of urban agriculture or change the focus of disaster countermeasures.

[1332] The scenario modified by the user is sent from the terminal to the server.

[1333] Step 8: Reanalysis and resimulation

[1334] The server then uses predictive and generative AI to perform analysis and simulation again based on the revised scenario submitted by the user.

[1335] The server generates new simulation results and provides them to the user.

[1336] Step 9: Determining the Optimal Scenario

[1337] The user views the regenerated simulation results and determines the optimal scenario.

[1338] The server creates a specific execution plan based on the optimal scenario. This includes task details, schedule, and resource allocation.

[1339] Step 10: Implement the action plan

[1340] Based on the decided execution plan, users take specific actions, such as starting a project or allocating resources.

[1341] (Example 1)

[1342] 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".

[1343] Currently, there is a lack of systems that can quickly and effectively propose optimal solutions to social challenges faced by national and local governments, municipalities, and corporations (e.g., population decline, disaster countermeasures, food crises, etc.). In particular, there is a need for an integrated platform to collect large amounts of data, process outliers and missing values, normalize the data, predict future scenarios based on that data, and perform visual simulations. Furthermore, there is a need for a system that allows users to interactively modify proposed solutions and determine the optimal solution scenario.

[1344] 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.

[1345] In this invention, the server includes data collection means, preprocessing means, means for analyzing data using predictive artificial intelligence, means for generating simulation results using generative artificial intelligence, means for interactively changing scenarios, means for reanalyzing based on user feedback, determining the optimal scenario, and creating an execution plan, and means for displaying simulation results on a terminal. This allows the user to collect various types of data, perform preprocessing, and interactively modify and determine the optimal countermeasures through advanced prediction and simulation.

[1346] "Data collection methods" refer to means for efficiently collecting necessary data from various data sources, such as via APIs or database queries.

[1347] "Preprocessing means" refers to means of cleaning the collected data, detecting and removing outliers, and normalizing the data in order to improve the quality of the collected data.

[1348] A "predictive artificial intelligence method" is a means of using a trained predictive artificial intelligence model to predict future scenarios and generate countermeasures based on pre-processed data.

[1349] A "generative artificial intelligence means" is a means of generating visual simulation results based on prediction results using a generative artificial intelligence model.

[1350] "An interactive means of changing the scenario" refers to a method that allows users to view the provided simulation results on their device and modify the scenario or proposed countermeasures as needed.

[1351] "A means of re-analyzing based on user feedback, determining the optimal scenario, and creating an implementation plan" refers to a method of re-analyzing and generating data based on user modifications, proposing the optimal countermeasures, and creating an implementation plan.

[1352] "Means for displaying simulation results on a terminal" refers to means for displaying the generated simulation results on the user's terminal and providing them to the user.

[1353] The system according to the present invention proposes optimal solutions to social issues through data collection, preprocessing, prediction, generation, and interactive modification of scenarios. This system aims to efficiently and effectively address challenges faced by national and local governments, municipalities, and corporations (e.g., population decline, disaster countermeasures, food crises, etc.).

[1354] Data collection

[1355] The server accesses various data sources provided by national and local governments, municipalities, and corporations to collect necessary big data. This data collection includes using APIs and querying databases. Specifically, high-performance server equipment is used as hardware, and the software utilizes libraries (for example, Python's Requests) that enable access to various API endpoints. Examples of target data include demographic data, disaster risk data, and agricultural production data.

[1356] Pre-treatment

[1357] The server cleans the collected data, imputing outliers and missing values. It also normalizes the data, arranging each data point into a unified format. This transforms the data into a format that is easy to analyze. Specifically, it uses the Python Pandas library for data preprocessing.

[1358] Analysis using predictive artificial intelligence

[1359] The server uses a pre-trained predictive artificial intelligence model to forecast future scenarios based on collected data. For example, it can estimate population decline, predict food production, and assess disaster risk. Furthermore, it generates countermeasures based on these predictions and evaluates their effectiveness. Machine learning libraries such as TensorFlow and Scikit-Learn can be used to implement the predictive AI model.

[1360] Simulation using generative artificial intelligence

[1361] The server uses generative artificial intelligence models to visually represent prediction results. For example, it generates simulated images of future cityscapes and detailed text reports. This allows users to visualize concrete scenarios. The generative models utilize generative AI technologies such as GANs (Generative Adversarial Networks).

[1362] User-interactive scenario changes

[1363] Users view the provided simulation results on their devices and modify the scenarios and proposed solutions as needed. For example, they might expand the scale of urban agriculture or change the focus of disaster countermeasures. The changes made by the user are sent to the server, where the analysis and simulation are performed again. The user's device has a web interface implemented to display the simulation results, using JavaScript, HTML, and CSS.

[1364] Determining and executing the optimal scenario

[1365] Based on user feedback, the server re-analyzes and generates data to propose the optimal solution. Based on this optimal solution, a concrete implementation plan is formulated and provided to the user. This plan includes specific action items, schedules, and resource allocations. The generated implementation plan is provided as a PDF report, and a download link is displayed on the user's device.

[1366] Specific example: Predicting and countermeasures for food crises

[1367] 1. Data Collection

[1368] The server collects agricultural production data, weather data, and transportation infrastructure data. Data collection utilizes various APIs and queries from databases.

[1369] 2. Preprocessing

[1370] The server cleans and normalizes the collected data, preparing it for analysis. Missing data is appropriately imputed.

[1371] 3. Analysis using predictive AI

[1372] The server uses a trained predictive model to forecast crop yields for the next year. Based on this forecast, it calculates the probability of food shortages occurring and generates several proposed countermeasures (e.g., imports from other regions, promotion of urban agriculture, etc.).

[1373] 4. Simulation using generative AI

[1374] The server uses generative AI to visualize the urban landscape after the introduction of urban agriculture. It also generates a detailed text report of proposed countermeasures.

[1375] 5. User-initiated interactive scenario changes

[1376] The user reviews the simulation results provided on their device and adjusts the scale of urban agriculture and import plans. The modified scenario is then sent to the server.

[1377] 6. Determining and Implementing the Optimal Scenario

[1378] The server re-analyzes the corrected scenario and provides the user with the optimal solution. The user then determines the final scenario, develops a specific implementation plan, and executes it as a project.

[1379] Example of a prompt

[1380] "Design a system that predicts food crises and generates optimal countermeasures. This involves collecting, cleaning, and normalizing agricultural production data, weather data, and transportation infrastructure data. The predictive model calculates the probability of food shortages and generates countermeasures. The generative model visualizes the urban landscape after the implementation of urban agriculture, allowing users to interactively change scenarios. The system then generates optimal countermeasures again and proposes concrete action plans."

[1381] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1382] Program processing flow

[1383] Step 1: Prepare for data collection

[1384] The server prepares to access various data sources provided by national and local governments, municipalities, corporations, and other organizations.

[1385] The required inputs are the API endpoint to be accessed and the authentication information.

[1386] Specifically, the process involves preparing the API URL and authentication token, and then using the Python Requests library to prepare for the connection.

[1387] As an output, a connection to the data source to be collected is established.

[1388] Step 2: Obtaining the required information

[1389] The server collects data through an API. It queries the database directly to retrieve the necessary information.

[1390] The input requires request parameters to be sent to the API endpoint.

[1391] Specifically, the Requests library is used to send a GET request and retrieve response data in JSON format.

[1392] As output, the collected raw data is stored in temporary storage.

[1393] Step 3: Data Cleaning

[1394] The server performs data cleaning to improve the quality of the collected data. This includes detecting and removing outliers and imputing missing values.

[1395] Raw data is required as input.

[1396] Specifically, the process involves using the Python Pandas library to impute missing values ​​and remove outliers.

[1397] The output will be cleaned data.

[1398] Step 4: Data Normalization

[1399] The server performs data normalization to unify data in different formats and units.

[1400] Cleaned data is required as input.

[1401] Specifically, the process involves converting all numerical data to a common unit and standardizing the data format.

[1402] The output will be normalized data.

[1403] Step 5: Loading the predictive model

[1404] The server loads a pre-trained predictive artificial intelligence model.

[1405] The input requires the path to the model file.

[1406] Specifically, the process involves loading the model from disk into memory using TensorFlow or Scikit-Learn.

[1407] As an output, the predictive model is loaded into memory.

[1408] Step 6: Run the forecast

[1409] The server inputs pre-processed data into a predictive model to forecast future scenarios.

[1410] The input requires normalized data and a loaded predictive model.

[1411] In terms of specific operations, the model's predict function is called to obtain the prediction result.

[1412] The output will be the prediction results.

[1413] Step 7: Loading the Generative Model

[1414] The server loads a generative artificial intelligence model for visualization.

[1415] The path to the generated model file is required as input.

[1416] Specifically, the process involves loading the GAN model from disk into memory.

[1417] As output, the generative model is loaded into memory.

[1418] Step 8: Run the simulation

[1419] The server generates a visual simulation based on the prediction results.

[1420] The input requires the prediction results and the loaded generative model.

[1421] In terms of specific operations, the prediction results are input into a generative model, and simulated images and text reports are generated.

[1422] The output is a visualized simulation result.

[1423] Step 9: Displaying the simulation results

[1424] Users can view the simulation results through their device.

[1425] The generated simulation results are required as input.

[1426] Specifically, a user interface (UI) is provided to display the simulation results in a web browser.

[1427] The user will see the simulation results as output.

[1428] Step 10: Scenario Revision

[1429] Users can interactively modify proposed solutions and scenarios.

[1430] The displayed simulation results are required as input.

[1431] In terms of operation, parameters are adjusted via a web interface, and those changes are sent to the server.

[1432] The revised scenario is generated as output.

[1433] Step 11: Feedback Analysis

[1434] The server will perform another analysis based on the user's feedback.

[1435] A modified scenario is required as input.

[1436] Specifically, the process involves running the prediction and simulation again using the new parameter set.

[1437] The output will include re-analyzed prediction results and simulation results.

[1438] Step 12: Provide an execution plan

[1439] The server develops a concrete implementation plan based on the optimal countermeasure and provides it to the user.

[1440] The input requires re-analyzed prediction results and simulation results.

[1441] Specifically, the system generates an execution plan as a PDF report and provides a download link to the user's device.

[1442] As output, a specific execution plan is provided to the user.

[1443] (Application Example 1)

[1444] 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".

[1445] Traditional logistics center operations management is fraught with numerous variables and uncertainties, making efficient inventory management and optimal picking route planning difficult. Furthermore, real-time data analysis and immediate feedback are challenging, resulting in limited operational flexibility. This can lead to human error, wasted resources, and decreased productivity. A system is needed to address these challenges and improve the efficiency of logistics operations.

[1446] 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.

[1447] In this invention, the server includes means for collecting data, means for preprocessing data, means for analyzing data using predictive artificial intelligence, means for generating simulation results using generative artificial intelligence, means for interactively changing scenarios, means for determining the optimal scenario and creating an execution plan, means for supporting the optimization of logistics operations, means for displaying inventory data and transportation information in real time, and means for modifying scenarios via smart glasses. This makes it possible to analyze data in real time and realize optimal logistics operations.

[1448] "Data collection means" refers to devices or systems used to collect necessary data via APIs or database queries.

[1449] "Preprocessing means" refers to devices or systems that clean, correct for outliers, and normalize collected data, and convert it into a format suitable for analysis.

[1450] A "predictive artificial intelligence tool" is a device or system that uses a pre-trained predictive model to predict future scenarios and countermeasures based on collected data.

[1451] A "generative artificial intelligence means" is a device or system that visually generates simulation results based on prediction results and provides them to the user.

[1452] "Means of interactively modifying scenarios" refer to devices or systems that allow users to view simulation results and modify scenarios or countermeasures as needed.

[1453] "Means for determining the optimal scenario and creating an implementation plan" refers to devices or systems that formulate optimal countermeasures and specific implementation plans based on user feedback.

[1454] "Means to support the optimization of logistics operations" refer to devices and systems that streamline operations within a logistics center and propose optimal inventory management and picking routes.

[1455] "Means for displaying inventory data and transportation information in real time" refers to devices and systems that display inventory data and transportation information collected within a logistics center in real time, thereby visualizing the situation.

[1456] "Means of modifying scenarios via smart glasses" refers to devices or systems that enable the use of smart glasses during logistics operations to modify scenarios and countermeasures on the spot through visual information and voice instructions.

[1457] This invention relates to a system that streamlines operations within a logistics center and proposes optimal inventory management and picking routes. The following describes specific embodiments for carrying out the invention.

[1458] Hardware and software to use

[1459] This system utilizes cloud servers, smart glasses, AI models, and a real-time database.

[1460] Cloud servers: Common cloud infrastructure such as Amazon Web Services and Google Cloud.

[1461] Smart glasses: For example, a typical smart glasses device.

[1462] Software: Libraries and tools used for data processing and analysis, such as Python, pandas, scikit-learn, and Plotly.

[1463] Data processing and data calculation

[1464] 1. Data collection methods:

[1465] The server retrieves inventory and transportation information from the distribution center by querying it via API or from the database. This data includes product IDs, inventory quantities, and inbound / outbound timestamps.

[1466] 2. Pre-treatment means:

[1467] The server cleans the collected data, imputing missing values ​​and correcting outliers. It also normalizes the data and converts it into a format suitable for analysis.

[1468] 3. Predictive artificial intelligence methods:

[1469] The server uses trained predictive AI models (such as Linear Regression or Deep Learning models) to forecast demand from collected data. This enables optimal inventory management and picking route planning.

[1470] 4. Generative artificial intelligence methods:

[1471] Based on the prediction results, an AI model is used to generate visual simulation results. For example, a visual display showing changes in shelf placement or new picking routes is created.

[1472] 5. Means of interactively changing the scenario:

[1473] Users can review the simulation results provided through smart glasses and modify the scenario via voice prompts and visual information. For example, they might use a prompt such as, "Show me next week's inventory forecast and suggest the optimal picking order."

[1474] 6. Means for determining the optimal scenario and creating an execution plan:

[1475] The server re-analyzes the user feedback and develops optimal countermeasures and a specific implementation plan. This plan includes specific action items, schedules, and resource allocations.

[1476] Specific example

[1477] For example, a newly implemented system in a logistics center uses prompt messages like the following:

[1478] "Please display the inventory forecast for next week and suggest the optimal picking order."

[1479] Following this prompt, the server analyzes the data in real time and presents the optimal scenario to the user through smart glasses. The user reviews the view and modifies the scenario as needed. This allows for efficient management of operations within the logistics center.

[1480] This system can optimize complex logistics operations in real time, significantly improving employee work efficiency.

[1481] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1482] Step 1:

[1483] The server collects inventory and transportation information from the logistics center via APIs and databases. Specific actions performed by the server include sending requests to API endpoints and executing SQL queries. This inputs raw data such as product IDs, inventory quantities, and inbound / outbound timestamps, which are then stored in the database.

[1484] Step 2:

[1485] The server preprocesses the collected data. Specifically, it cleans the data (imputing missing values, correcting outliers) and normalizes it. The input is the raw data collected in step 1, which is then converted to a unified format. The output is cleaned data suitable for analysis.

[1486] Step 3:

[1487] The server analyzes pre-processed data and performs demand forecasting using a trained predictive artificial intelligence model. The input is the cleaned data from step 2, and the output is future demand forecast data. Specifically, this involves inputting data into a predictive model (e.g., Linear Regression or Deep Learning model) and retrieving results.

[1488] Step 4:

[1489] The server uses a generative artificial intelligence model to generate visual simulation results based on the prediction results. The input is the demand forecast data from step 3, and the output is a visual representation of the simulation (e.g., rearranged shelves or new picking routes). The specific operations include inputting data into the generative model and generating visual content.

[1490] Step 5:

[1491] The user reviews the simulation results provided through smart glasses and makes necessary changes interactively using prompts. The input is the simulation results from step 4, and the user's modification feedback is taken into consideration. Specific actions include scenario changes based on voice prompts and visual information.

[1492] Step 6:

[1493] The server reanalyzes the data based on user feedback and develops optimal countermeasures and specific implementation plans. The input is the user correction feedback from step 5, and the output is the final countermeasures and implementation plan. Specific actions include reanalysis incorporating the feedback and creation of an implementation plan.

[1494] Step 7:

[1495] The server deploys the finalized execution plan to each piece of equipment in the logistics operation, optimizing the entire system. The input is the execution plan from step 6, and the output is the optimized state of the logistics operation. Specific actions include sending instructions to transport robots and picking robots.

[1496] 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.

[1497] The system according to the present invention provides optimized solutions to social issues by combining data collection, preprocessing, prediction, generation, and interactive scenario modification with an emotion engine that recognizes user emotions. This system aims to respond in real time and adaptively to complex challenges faced by national and local governments, municipalities, and corporations (e.g., population decline, disaster countermeasures, food crises, etc.).

[1498] System Overview

[1499] 1. Data Collection

[1500] The server collects various types of data provided by national, local, municipal, and corporate entities via APIs and database queries. This data includes demographic data, disaster risk data, and agricultural production data.

[1501] 2. Preprocessing

[1502] The server cleans the collected data, detects outliers and missing values, and performs interpolation or removal as needed.

[1503] The server normalizes the data and prepares it for analysis.

[1504] 3. Analysis using predictive AI

[1505] The server uses a pre-trained predictive artificial intelligence model to forecast future scenarios. For example, it can predict the rate of population decline, the risk of disasters, and food production for the next year.

[1506] The server generates proposed countermeasures based on the prediction results and evaluates the effectiveness and risks of each.

[1507] 4. Simulation using generative AI

[1508] The server uses generative artificial intelligence (e.g., image generation AI, language generation AI) to represent predictive scenarios visually and in text. For example, it can generate simulated images of urban landscapes after the introduction of urban agriculture and detailed reports.

[1509] 5. User-initiated interactive scenario changes

[1510] Users view the provided simulation results on their devices and modify scenarios and proposed countermeasures based on their feedback. Examples include scenarios that expand the scale of urban agriculture or changes in the focus of disaster countermeasures.

[1511] User modifications are sent from the terminal to the server, where they are analyzed and simulated again.

[1512] 6. Emotion recognition by an emotion engine

[1513] The server uses an emotion engine to collect and analyze emotional information during user feedback and interactions. It uses speech recognition and natural language processing technologies to evaluate the user's emotional state.

[1514] 7. Emotion-based scenario adjustment

[1515] The server adjusts scenarios and proposed solutions based on emotional information obtained from the emotion engine. If the user is experiencing stress, the system takes action such as providing more concise and easy-to-understand information.

[1516] 8. Determining and Implementing the Optimal Scenario

[1517] The user determines the optimal scenario based on the regenerated scenarios.

[1518] The server creates and provides the user with a specific execution plan based on the optimal scenario. This includes detailed tasks, schedules, and resource allocations.

[1519] The user takes specific actions based on the decided execution plan.

[1520] Specific example

[1521] Predicting and countermeasures for food crises

[1522] 1. Data Collection

[1523] The server collects agricultural production data, weather data, and transportation infrastructure data. Data collection utilizes various APIs and queries from databases.

[1524] 2. Preprocessing

[1525] The server cleans and normalizes the collected data and appropriately imputes missing data.

[1526] 3. Analysis using predictive AI

[1527] The server uses a predictive AI model to forecast crop production for the next year, assess the risk of food shortages, and generate multiple countermeasures.

[1528] 4. Simulation using generative AI

[1529] The server uses generative AI to visualize the urban landscape after the introduction of urban agriculture and generates a detailed text report of proposed countermeasures.

[1530] 5. User-initiated scenario changes

[1531] Users review the simulation results on their terminal and adjust the scale of urban agriculture and import plans. The revised scenario is then sent to the server.

[1532] 6. Emotion recognition by an emotion engine

[1533] The server collects and analyzes emotional information using speech recognition and natural language processing during user reviews. For example, if a user is feeling anxious, the server uses that information to soften the explanation of the suggested solutions.

[1534] 7. Emotion-based scenario adjustment

[1535] The server fine-tunes the scenario based on information obtained from the emotion engine, presenting solutions in a way that is more convincing to the user.

[1536] 8. Determining and Implementing the Optimal Scenario

[1537] The user selects the optimal countermeasure based on the regenerated scenario, and the server creates an execution plan based on that scenario.

[1538] The user takes specific actions (e.g., starting a project, allocating resources) based on the determined execution plan.

[1539] By combining this system with an emotion engine, it can perform interactive scenario changes and optimizations that take into account the user's emotional state, enabling it to provide more effective and user-friendly solutions.

[1540] The following describes the processing flow.

[1541] Step 1: Data Collection

[1542] The server collects various types of data provided by national, local, municipal, and corporate entities via APIs or database queries. For example, it retrieves demographic data, disaster risk data, and agricultural production data.

[1543] Step 2: Data Preprocessing

[1544] The server cleans the collected data, detects missing or outlier values, and imputes or removes them if necessary.

[1545] The server normalizes the cleaned data and prepares it for analysis.

[1546] Step 3: Training the AI ​​model

[1547] The server uses normalized data to train predictive artificial intelligence models. For example, it uses regression models for population forecasting and time-series models for disaster risk prediction.

[1548] The server evaluates and optimizes the model's accuracy using cross-validation techniques.

[1549] Step 4: Generating Prediction Scenarios

[1550] The server uses a trained predictive artificial intelligence model to forecast future scenarios. For example, it can predict the rate of population decline, the risk of disasters, and food production for the next year.

[1551] The server generates multiple countermeasures based on the prediction results and evaluates the effectiveness and risks of each.

[1552] Step 5: Simulation using generative AI

[1553] The server uses generative artificial intelligence (image generation AI and language generation AI) to represent predictive scenarios visually and in text. For example, it generates simulated images of urban landscapes after the introduction of urban agriculture and detailed reports.

[1554] Step 6: Provide simulation results

[1555] The server provides the user with the generated simulation results. The user views these results on their terminal and evaluates the effectiveness of the scenarios and proposed solutions.

[1556] Step 7: Emotion recognition by the emotion engine

[1557] The server uses an emotion engine to detect user feedback and emotions during interactions. It analyzes the user's emotional state using speech recognition and natural language processing technologies.

[1558] Step 8: Emotion-based scenario adjustments

[1559] The server uses emotional information obtained from the emotion engine to adjust scenarios and suggested solutions. For example, if the user is feeling stressed, it will provide more concise and easy-to-understand information.

[1560] Step 9: User changes scenario

[1561] Users modify scenarios and proposed solutions on their devices based on simulation results and their emotions. For example, they might expand the scale of urban agriculture or change the focus of disaster preparedness.

[1562] The scenario modified by the user is sent from the terminal to the server.

[1563] Step 10: Reanalysis and resimulation

[1564] The server then uses predictive and generative AI to perform analysis and simulation again based on the revised scenario submitted by the user.

[1565] The server generates new simulation results and provides them to the user.

[1566] Step 11: Determining the Optimal Scenario

[1567] The user determines the optimal scenario based on the regenerated simulation results.

[1568] The server creates a specific execution plan based on the optimal scenario. This includes detailed tasks, schedules, and resource allocations.

[1569] Step 12: Implement the Action Plan

[1570] Based on the decided execution plan, the user takes specific actions (such as starting a project or allocating resources).

[1571] (Example 2)

[1572] 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".

[1573] Traditional solutions have struggled to provide effective solutions to the complex and diverse challenges facing society (e.g., population decline, disaster preparedness, food crises) due to their lack of real-time response capabilities and adaptability. Furthermore, the difficulty in incorporating user emotions and feedback into scenario adjustments has resulted in a lack of user-friendly solutions.

[1574] 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.

[1575] In this invention, the server includes means for collecting data, means for preprocessing data, means for analyzing data using predictive artificial intelligence, means for generating simulation results using generative artificial intelligence, means for interactively changing scenarios, means for collecting and analyzing feedback using an emotion recognition engine, means for adjusting scenarios based on emotion information, and means for determining the optimal scenario and creating an execution plan. This enables real-time analysis and simulation of collected data, realizes interactive scenario adjustments that take user emotions into consideration, and enables the provision of more effective and user-friendly solutions.

[1576] "Means of collecting data" refers to the system's function of obtaining necessary data from external sources using APIs or database queries.

[1577] "Means of preprocessing" refers to the system's function of cleaning collected data, imputing or removing outliers and missing values, and normalizing the data to prepare it for analysis.

[1578] "Methods for analyzing data using predictive artificial intelligence" refers to the functionality of a system that uses a trained AI model to predict future scenarios and risks from collected and pre-processed data.

[1579] "Means for generating simulation results using generative artificial intelligence" refers to the functionality of a system that uses image generation AI and language generation AI to represent predicted scenarios visually and as text.

[1580] "An interactive means of modifying the scenario" refers to a system function that allows users to view simulation results via a terminal, modify the scenario and proposed countermeasures based on feedback, and send those modifications to the server.

[1581] "Means for collecting and analyzing feedback using an emotion recognition engine" refers to a system function that uses speech recognition and natural language processing technologies to evaluate the user's emotional state and analyze the feedback.

[1582] "Means of adjusting scenarios based on emotional information" refers to a system function that adjusts scenarios and proposed solutions in a user-friendly manner based on user emotional information obtained from an emotional recognition engine.

[1583] "Means for determining the optimal scenario and creating an execution plan" refers to a system function that determines the optimal scenario based on the regenerated scenarios, creates a specific execution plan (tasks, schedule, resource allocation) based on that scenario, and provides it to the user.

[1584] This invention provides a system that consistently performs data acquisition, preprocessing, prediction, simulation, scenario modification, sentiment recognition, and optimal scenario determination. This system operates through the cooperation of a server, terminals, and users.

[1585] System configuration and operation

[1586] Data collection

[1587] The server collects necessary data from multiple data sources using APIs and database queries. This data includes demographics, crop production, and disaster risk information. Specifically, the server sends requests to API endpoints and retrieves the data returned as responses in JSON format.

[1588] Pre-treatment

[1589] The server cleans the collected data, detecting and imputing outliers and missing values. Each dataset is normalized and prepared for analysis. Specifically, missing values ​​are imputed with the data's mean, and the data range is scaled from 0 to 1.

[1590] Analysis using predictive AI

[1591] The server uses a pre-trained predictive AI model to forecast future scenarios. This includes forecasts of the next year's population decline rate, disaster risk, and food production volume. The server inputs data into the AI ​​model and outputs the prediction results. The pre-trained model may include a deep learning-based model.

[1592] Simulation using generative AI

[1593] The server uses a generative AI model to represent predictive scenarios in visual and textual formats. For example, it can generate simulated images of urban landscapes after the introduction of urban agriculture, or generate detailed action plan reports. At this time, the server will input prompts such as the following:

[1594] Prompt: "Based on forecasts of next year's crop production and an assessment of the resulting risk of food shortages, conduct a simulation of what happens after the introduction of urban agriculture, and generate the results as a visualization and a detailed text report."

[1595] User-interactive scenario changes

[1596] Users view the simulation results provided on their devices and modify the scenarios and proposed countermeasures. For example, they might adjust the scale of urban agriculture or change the focus of disaster response. The modified scenarios are sent from the device to the server for reanalysis and resimulation.

[1597] Emotion recognition by an emotion engine

[1598] The server uses an emotion engine to collect and analyze emotional information during user feedback and interactions. It evaluates the user's emotional state using speech recognition and natural language processing technologies, and makes adjustments based on the collected emotional information to reduce the user's anxiety and stress.

[1599] Emotion-based scenario adjustment

[1600] The server adjusts the scenario based on emotional information obtained from the emotion engine. For example, if the user is feeling stressed, the system will provide more concise and easy-to-understand information.

[1601] Determining and executing the optimal scenario

[1602] The user determines the optimal scenario based on the regenerated scenarios. The server creates a specific execution plan (tasks, schedule, resource allocation) based on that scenario. The user then takes specific actions according to the execution plan.

[1603] Thus, this system can respond in real time, taking user emotions into consideration, from data collection to the execution of optimal scenarios. Through close collaboration between the server, terminal, and user at each processing step, effective solutions to complex social issues are provided.

[1604] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1605] Step 1: Data Collection

[1606] The server uses APIs and database queries to collect the necessary data from multiple data sources.

[1607] Input: API endpoint or database query

[1608] Specific operation: The server sends a request to the API endpoint "https: / / api.example.com / population_stats" and retrieves demographic data in JSON format as a response.

[1609] Output: Collected data

[1610] Step 2: Pretreatment

[1611] The server cleans the collected data, detecting and imputing or removing outliers and missing values.

[1612] Input: Collected data

[1613] Specific operation: The server performs a data scan and imputes missing data with the mean value. It normalizes the data and prepares it for analysis.

[1614] Output: Preprocessed data

[1615] Step 3: Analysis using predictive AI

[1616] The server inputs the pre-processed data into a trained predictive AI model to predict future scenarios.

[1617] Input: Preprocessed data

[1618] Specific operation: The server inputs data into a predictive model and forecasts the population decline rate, disaster risk, and food production volume for the next year. Based on the prediction results, it generates proposed countermeasures and evaluates their effectiveness.

[1619] Output: Prediction results and proposed countermeasures

[1620] Step 4: Simulation using generative AI

[1621] The server uses a generative AI model based on the prediction results to represent the simulation results in both visual and textual formats.

[1622] Input: Prediction results and proposed countermeasures

[1623] Specific operation: The server inputs prompt text into the generative AI, which then generates simulated images of urban agriculture after its implementation and a detailed report of proposed countermeasures.

[1624] Prompt: "Based on forecasts of next year's crop production and an assessment of the resulting risk of food shortages, conduct a simulation of what happens after the introduction of urban agriculture, and generate the results as a visualization and a detailed text report."

[1625] Output: Simulation results, simulated images, and proposed countermeasures report.

[1626] Step 5: User-initiated interactive scenario changes

[1627] Users can view the simulation results provided through their device and modify the scenarios and proposed solutions as needed.

[1628] Input: Simulation results

[1629] Specific operation: The user checks the simulation results on their device and makes adjustments, such as adjusting the scale of urban agriculture. These adjustments are then sent from the device to the server.

[1630] Output: Modified scenario

[1631] Step 6: Emotion recognition by the emotion engine

[1632] The server uses an emotion engine to collect and analyze emotional information during user feedback and interactions.

[1633] Input: User feedback

[1634] Specific operation: The server uses speech recognition and natural language processing technologies to evaluate the user's emotional state and stores the analysis results as emotional information.

[1635] Output: Analyzed sentiment information

[1636] Step 7: Scenario adjustment based on emotional information

[1637] The server adjusts the scenario based on emotional information.

[1638] Input: Analyzed emotional information

[1639] Specific operation: Based on the results of the emotion recognition engine, the server adjusts the scenario and proposed solutions to be user-friendly and provides concise and easy-to-understand information.

[1640] Output: Adjusted scenario

[1641] Step 8: Determine and execute the optimal scenario

[1642] The user determines the optimal scenario based on the regenerated scenarios. The server then creates a specific execution plan based on that scenario and provides it to the user.

[1643] Input: Adjusted scenario

[1644] Specific operation: The user selects the optimal scenario, and the server creates an execution plan (tasks, schedule, resource allocation). The user then performs specific actions according to that plan.

[1645] Output: Optimal execution plan and specific actions

[1646] In this way, the system operates in close coordination at each processing step, from data collection to the execution of the optimal scenario.

[1647] (Application Example 2)

[1648] 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".

[1649] Fluctuations and shortages in food supply necessitate increased efficiency in food delivery services. Furthermore, flexible responses tailored to user needs and emotions are also required. This invention aims to address these challenges by improving the quality and efficiency of food delivery services through real-time prediction and optimization, as well as feedback based on user emotions.

[1650] 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.

[1651] In this invention, the server includes means for collecting data, means for preprocessing data, means for analyzing data using predictive artificial intelligence, means for generating simulation results using generative artificial intelligence, means for recognizing user emotions, means for adjusting scenarios based on user emotions, means for interactively changing scenarios, and means for determining the optimal scenario and creating an execution plan. This enables real-time supply and demand forecasting and supply planning for food delivery services, as well as flexible service provision that responds to user emotions.

[1652] "Means of data collection" refers to methods for collecting necessary data from various sources, including methods via APIs and queries from databases.

[1653] "Means of preprocessing" refers to means of cleaning and normalizing collected data and preparing it into an analyzable format.

[1654] "Methods for analyzing data using predictive artificial intelligence" refer to methods for analyzing collected data and predicting future scenarios using trained artificial intelligence models.

[1655] "Means for generating simulation results using generative artificial intelligence" refers to means for generating simulation results in visual and textual formats based on predicted scenarios.

[1656] "Means of recognizing user emotions" refers to means of collecting and analyzing emotional information during user feedback and interactions, and includes speech recognition and natural language processing technologies.

[1657] "Means of adjusting scenarios based on user emotions" refers to methods for flexibly adjusting scenarios and countermeasures using recognized user emotion information.

[1658] "An interactive means of changing the scenario" refers to a method by which users can view simulation results through their devices and modify the scenario or proposed countermeasures based on their feedback.

[1659] "Means for determining the optimal scenario and creating an execution plan" refers to the means for creating a specific execution plan based on the optimal scenario determined by the user.

[1660] The system according to the present invention aims to optimize food delivery services and improve the user experience. This system includes data collection, preprocessing, data analysis using predictive artificial intelligence, generation of simulation results using generative artificial intelligence, user emotion recognition and scenario adjustment, interactive scenario modification, determination of the optimal scenario, and creation of an execution plan.

[1661] The system will be implemented as follows:

[1662] First, the server collects necessary data, such as crop production data and weather data, via APIs or database queries. This data is then preprocessed, undergoing cleaning and normalization to prepare it for analysis.

[1663] Next, the server analyzes the collected data using a trained predictive artificial intelligence model. For example, it can predict the supply of agricultural products for the next year and evaluate the supply-demand balance in food delivery services. It also uses generative artificial intelligence to generate simulation results based on these predictions in visual and text formats. This could include, for example, optimizing delivery routes and suggesting delivery schedules.

[1664] Users view simulation results through their devices and provide feedback. The server collects and analyzes user sentiment information using speech recognition and natural language processing technologies. The sentiment engine flexibly adjusts the scenario based on user feedback and suggests the optimal course of action.

[1665] For example, a user might provide the following feedback:

[1666] "Today's delivery was delayed, but the quality was good. I wish it could be delivered a little faster."

[1667] In response to this feedback, the server performs sentiment recognition and analyzes whether the delivery delay is causing dissatisfaction with the user. Based on this, the delivery plan is optimized again to improve service to the user.

[1668] This enables real-time supply and demand forecasting and supply planning for food delivery services, as well as flexible service delivery tailored to user preferences. The system aims to improve the efficiency of food delivery and enhance user satisfaction.

[1669] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1670] Step 1:

[1671] The server collects necessary data, such as crop production data and weather data, via APIs or database queries. This provides the production and weather data to be used as input data. This data is then used in subsequent preprocessing steps.

[1672] Step 2:

[1673] The server preprocesses the collected data. Specifically, it cleans and normalizes the data to prepare it for analysis. The input to this step is the data collected in step 1, and the output is the cleaned data. This includes imputing missing values ​​and removing outliers.

[1674] Step 3:

[1675] The server uses a trained predictive artificial intelligence model to analyze preprocessed data and predict future scenarios. The input for this step is cleaned data, and the output is a predicted supply quantity. Examples include predicting crop supply quantities or evaluating supply-demand balance in food delivery services.

[1676] Step 4:

[1677] The server uses generative artificial intelligence to generate simulation results based on predicted scenarios in both visual and textual formats. The input for this step is the predicted scenario, and the output is visualized simulation results and a text report. Examples include optimizing delivery routes and suggesting delivery schedules.

[1678] Step 5:

[1679] The user reviews the simulation results generated by the server through their terminal and provides feedback. A concrete example of feedback might be, "Today's delivery was delayed, but the quality was good. I wish it could be delivered a little earlier." The input for this step is the visualized simulation results, and the output is user feedback.

[1680] Step 6:

[1681] The server uses speech recognition and natural language processing technologies to collect and analyze emotional information from user feedback. The input for this step is user feedback, and the output is user emotional information. The emotional recognition engine analyzes user stress and dissatisfaction to help adjust the system.

[1682] Step 7:

[1683] The server adjusts the scenario based on the user's sentiment information and suggests the optimal course of action. The input for this step is the user's sentiment information, and the output is the adjusted simulation results and new proposed solutions. For example, it might suggest re-optimizing the delivery plan or improving the service.

[1684] Step 8:

[1685] Ultimately, after the optimal scenario is determined by the user, the server creates a specific execution plan based on that scenario. The input to this step is the adjusted scenario, and the output is the execution plan. The execution plan includes detailed tasks, schedules, resource allocations, and more.

[1686] 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.

[1687] 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.

[1688] 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.

[1689] 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.

[1690] 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.

[1691] 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.

[1692] 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.

[1693] 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.

[1694] 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."

[1695] 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.

[1696] 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.

[1697] 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.

[1698] 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.

[1699] 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.

[1700] 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.

[1701] 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.

[1702] 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.

[1703] 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.

[1704] 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.

[1705] 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.

[1706] 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.

[1707] The following is further disclosed regarding the embodiments described above.

[1708] (Claim 1)

[1709] Means of collecting data,

[1710] Means for performing pre-processing,

[1711] Methods for analyzing data using predictive artificial intelligence,

[1712] A means of generating simulation results using generative artificial intelligence,

[1713] A means of interactively changing the scenario,

[1714] A means of determining the optimal scenario and creating an execution plan,

[1715] A system that includes this.

[1716] (Claim 2)

[1717] The system according to claim 1, characterized in that the data collection means uses an API or queries from a database.

[1718] (Claim 3)

[1719] The system according to claim 1, characterized in that the predictive artificial intelligence means predicts data using multiple models and generates multiple proposed countermeasures.

[1720] "Example 1"

[1721] (Claim 1)

[1722] Data collection means,

[1723] Pre-treatment means,

[1724] Methods for analyzing data using predictive artificial intelligence,

[1725] A means of generating simulation results using generative artificial intelligence,

[1726] A means of interactively changing the scenario,

[1727] A means of re-analyzing based on user feedback, determining the optimal scenario, and creating an execution plan,

[1728] A means of displaying the simulation results on a terminal,

[1729] A system that includes this.

[1730] (Claim 2)

[1731] The system according to claim 1, characterized in that the data collection means uses an API or queries from a database.

[1732] (Claim 3)

[1733] The system according to claim 1, characterized in that the predictive artificial intelligence means predicts data using multiple models and generates multiple proposed countermeasures.

[1734] "Application Example 1"

[1735] (Claim 1)

[1736] Means of collecting data,

[1737] Means for performing pre-processing,

[1738] Methods for analyzing data using predictive artificial intelligence,

[1739] A means of generating simulation results using generative artificial intelligence,

[1740] A means of interactively changing the scenario,

[1741] A means of determining the optimal scenario and creating an execution plan,

[1742] Means to support the optimization of logistics operations,

[1743] A means of displaying inventory data and transportation information in real time,

[1744] A means of modifying the scenario through smart glasses,

[1745] A system that includes this.

[1746] (Claim 2)

[1747] The system according to claim 1, characterized in that the data collection means uses an API or queries from a database.

[1748] (Claim 3)

[1749] The system according to claim 1, characterized in that the predictive artificial intelligence means predicts data using multiple models and generates multiple proposed countermeasures.

[1750] "Example 2 of combining an emotion engine"

[1751] (Claim 1)

[1752] Means of collecting data,

[1753] Means for performing pre-processing,

[1754] Methods for analyzing data using predictive artificial intelligence,

[1755] A means of generating simulation results using generative artificial intelligence,

[1756] A means of interactively changing the scenario,

[1757] A means of collecting and analyzing feedback using an emotion recognition engine,

[1758] A means of adjusting the scenario based on emotional information,

[1759] A means of determining the optimal scenario and creating an execution plan,

[1760] A system that includes this.

[1761] (Claim 2)

[1762] The system according to claim 1, characterized in that the data collection means uses an API or queries from a database.

[1763] (Claim 3)

[1764] The system according to claim 1, characterized in that the predictive artificial intelligence means predicts data using multiple models and generates multiple proposed countermeasures.

[1765] "Application example 2 when combining with an emotional engine"

[1766] (Claim 1)

[1767] Means of collecting data,

[1768] Means for performing pre-processing,

[1769] Methods for analyzing data using predictive artificial intelligence,

[1770] A means of generating simulation results using generative artificial intelligence,

[1771] Means of recognizing user emotions,

[1772] A means of adjusting the scenario based on user emotions,

[1773] A means of interactively changing the scenario,

[1774] A means of determining the optimal scenario and creating an execution plan,

[1775] A system that includes this.

[1776] (Claim 2)

[1777] The system according to claim 1, characterized in that the data collection means uses an API or queries from a database.

[1778] (Claim 3)

[1779] The system according to claim 1, characterized in that the predictive artificial intelligence means predicts data using multiple models and generates multiple proposed countermeasures. [Explanation of symbols]

[1780] 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. Means of collecting data, Means for performing pre-processing, Methods for analyzing data using predictive artificial intelligence, A means of generating simulation results using generative artificial intelligence, A means of interactively changing the scenario, A means of determining the optimal scenario and creating an execution plan, A system that includes this.

2. The system according to claim 1, characterized in that the data collection means uses an API or queries from a database.

3. The system according to claim 1, characterized in that the predictive artificial intelligence means predicts data using multiple models and generates multiple proposed countermeasures.

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