system

Abstract

We provide the system. [Solution] A means for receiving data from an observation device and converting that data into a unified format, A means for analyzing the data using an analysis algorithm, Means for managing and visualizing the analyzed data, A means of providing an interface to provide information to users and support decision-making, A means to optimize available resources and automatically readjust their placement, A system that includes means of protecting data using security protocols.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the space business, there is a need to quickly and efficiently analyze and manage a huge amount of data collected from satellites and ground sensors. Conventional methods require time and effort for data collection, analysis, and management, making it difficult to support real-time decision-making and optimize resources. In addition, there were cases where data security was not ensured sufficiently. In response to these problems, a system that can perform integrated operations from data collection to decision-making support is required.

Means for Solving the Problems

[0005] This invention provides a system that receives data from observation equipment in a unified format and processes it using an algorithm for analysis. This system manages the analyzed information and provides a user-facing interface that visualizes the information, supporting rapid user decision-making. Furthermore, it has a function to optimize available resources based on acquired data and automatically readjust their allocation, and incorporates security protocols to protect the data. In this way, it aims to improve the efficiency and security of data processing in the space business.

[0006] An "observation device" is an instrument placed in outer space or on the ground to acquire data on natural phenomena or artificial phenomena.

[0007] "Converting data to a unified format" refers to the process of converting data sent in different formats into a consistent standard format.

[0008] An "analysis algorithm" refers to a computational method and process for extracting useful information from data.

[0009] An "interface that provides users with visualized information" is a tool that displays analyzed data in charts and graphs to help users understand it.

[0010] "Optimizing resources and automatically readjusting their allocation" is a process that includes computing power to rearrange available resources in the most effective way.

[0011] A "security protocol" is a set of rules and procedures established in communications and data management for the purpose of protecting data and preventing unauthorized access. [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] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

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

[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] In an embodiment of this invention, a dedicated software system operates on a server. This system receives data from observation devices, converts it to a unified format, and then processes the data by applying an analysis algorithm. The server displays the results of this processing on a management dashboard in real time and sends relevant information to the user.

[0034] The terminal provides visually analyzed information through a user-accessible interface. For example, it displays weather patterns and terrain changes based on data from space in interactive graphs. This information also supports decision-making, allowing users to develop operational plans and resource management strategies based on the data obtained.

[0035] The server further uses the acquired data to efficiently reallocate resources. This involves using optimization algorithms to propose settings that enhance the utilization efficiency of space equipment and ground infrastructure. Security protocols are employed to guarantee safety during data communication and prevent unauthorized access.

[0036] As a concrete example, consider a case where a user utilizes climate data from space for agricultural purposes. The user's device receives the latest climate analysis data from the server and displays time-series graphs showing fluctuations in precipitation and temperature for a specific region. This information is used to determine the optimal timing for planting and harvesting crops. This makes it possible to achieve more profitable agriculture and helps to significantly reduce resource waste.

[0037] The following describes the processing flow.

[0038] Step 1:

[0039] The server receives data transmitted from the observation equipment in real time. This involves establishing a stable connection with the observation equipment using advanced communication infrastructure and appropriately capturing diverse data formats.

[0040] Step 2:

[0041] The server converts the received data into a predetermined, unified format. This allows the analysis algorithm to process each data item consistently. This conversion process involves metadata extraction and reconstruction based on formatting rules.

[0042] Step 3:

[0043] The server applies an analysis algorithm to the transformed data. This algorithm identifies specific patterns in the image data and extracts significant information about climate and topography. The results are optimized through a feedback loop that further enhances the overall quality of the information obtained through the analysis.

[0044] Step 4:

[0045] The server stores the analysis results in a central database and reflects the information on the user dashboard. In this process, scalable database technology is employed for data management and visualization, ensuring fast query response times.

[0046] Step 5:

[0047] The terminal visualizes the analyzed information through a user-facing interface. Users can use this interface to view climate and topographic data in graphs and maps, and use this information to inform their decision-making. High-resolution joint maps and time-series graphs are used for visualization.

[0048] Step 6:

[0049] The server executes an automated resource allocation algorithm based on analysis data, with the aim of optimizing resources. This algorithm effectively adjusts the allocation of existing space equipment resources and makes suggestions to improve operational efficiency.

[0050] Step 7:

[0051] The server applies security protocols throughout the entire data communication process to protect data confidentiality and integrity. This ensures that only authorized users can access the information and prevents unauthorized manipulation of the data.

[0052] (Example 1)

[0053] 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."

[0054] In today's data collection environment, it is crucial to extract useful information from vast and diverse data formats and to manage resources efficiently and securely based on that information. However, traditional systems have faced challenges such as the time and cost involved in integrating different data formats, performing real-time analysis, providing visual information, and optimizing resources. Furthermore, there has been a lack of data security and an easy-to-use interface for users to utilize generated AI models.

[0055] 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.

[0056] In this invention, the server includes means for receiving data from an observation device and converting the data into a unified format, means for analyzing the data using an analysis algorithm, and means for deploying the analyzed data to a management dashboard in real time. This enables the integration of data formats, streamlining of the analysis process, real-time provision of visual information, secure data communication, and optimal utilization of resources.

[0057] An "observation device" refers to an instrument that collects physical or digital data from the external environment.

[0058] A "unified format" refers to a common data representation format used to convert data in different formats into a consistent format.

[0059] An "analytical algorithm" refers to a mathematical or computational method used to analyze data and extract useful information from it.

[0060] A "management dashboard" refers to a display system that visualizes analyzed data and has an interface for intuitive understanding.

[0061] A "visual interface" refers to an interactive display method for providing information to users graphically.

[0062] An "optimization algorithm" refers to a computational technique used to achieve the efficient allocation of resources.

[0063] A "security protocol" refers to encryption technologies and procedures used to maintain the security of data communications.

[0064] A "generative AI model" refers to an artificial intelligence model that generates information based on specified input conditions.

[0065] A "prompt" refers to a text-based instruction or question that is input into a generative AI model.

[0066] This invention is implemented by running a specific software system on a server. This system receives data in various formats from observation equipment and converts it into a unified format. The server processes the data using data conversion tools, such as ETL tools. The converted data is then analyzed in detail via analytical algorithms. Machine learning libraries such as Python's Scikit-learn and TENSORFLOW® are used for the analysis, including climate pattern analysis and topographic change prediction.

[0067] The analysis results are displayed in real time on a management dashboard. This dashboard is built using visualization software such as Tableau or Grafana as tools for data visualization. Users can interactively view these results through a visual interface provided via their device. For example, it is possible to display interactive graphs of climate data using libraries such as D3.js or Chart.js.

[0068] Users can make decisions based on the analysis data sent from the server, and further obtain detailed analysis results by inputting prompts to the generating AI model. These prompts function as instructions or questions for the generating AI, and are entered in the form of "Please suggest the optimal irrigation schedule for next month."

[0069] Furthermore, the server utilizes optimization algorithms to propose efficient resource allocation. Operations research techniques are used in this process. The server also implements security protocols, such as SSL / TLS, to ensure secure data communication and protect against unauthorized access.

[0070] For example, when users utilize climate data from space for agricultural purposes, this system allows them to view time-series graphs showing fluctuations in precipitation and temperature. Based on this information, it becomes possible to determine the optimal planting time for crops, thereby reducing resource waste.

[0071] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0072] Step 1:

[0073] The server receives data from observation instruments. These instruments transmit, for example, climate data or image data acquired from sensors placed in space. The input is raw sensor data, and the output is data prepared for conversion to a unified format. This process includes data format verification and metadata assignment.

[0074] Step 2:

[0075] The server converts the input data into a unified format. Specifically, it uses ETL (Extract, Transform, Load) tools to reshape data in different formats into a standardized format. The input is raw data, and the output is standardized data. This process prepares the data for subsequent analysis to proceed smoothly.

[0076] Step 3:

[0077] The server analyzes data by applying analytical algorithms to data in a unified format. This includes applying machine learning models using Python's Scikit-learn and TensorFlow. The input is standardized data, and the output is predicted data or classified information resulting from the analysis. Specific operations include predicting temperature changes and detecting anomalies from image data.

[0078] Step 4:

[0079] The server deploys the analysis results to a management dashboard in real time. Tools such as Tableau and Grafana are used for data visualization. The input is analyzed data, and the output is a visual report designed for intuitive user understanding. This phase specifically includes the generation of visual graphs and charts.

[0080] Step 5:

[0081] The terminal provides the user with a visual interface and displays the analysis results. The interface runs on a web browser and uses libraries such as D3.js and Chart.js to interactively visualize the data. The input is visual data sent from the server, and the output is a user-interactive interface.

[0082] Step 6:

[0083] The user makes a decision based on the displayed information and inputs prompts into the generating AI model. As a specific instruction for the analysis the user requests, they send prompts to the generating AI such as, "Please suggest the optimal irrigation schedule for next month." The input is a prompt to the generating AI, and the output is a report containing detailed analysis results and suggestions.

[0084] Step 7:

[0085] The server provides a plan for reallocated resources and proposes an optimal solution using operational research techniques. Inputs are analytical data and current resource allocation information, and output is an optimized resource allocation plan. Ultimately, this leads to improved resource efficiency and reduced operating costs.

[0086] (Application Example 1)

[0087] 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."

[0088] In modern public facilities, security measures are becoming increasingly complex and diverse, making it difficult to effectively analyze information in real time and to quickly detect and respond to anomalies. Furthermore, if resources are not properly allocated and readjusted, the effectiveness of security may decrease, requiring efficient operation. It is necessary to address these challenges and achieve safer and more efficient security management.

[0089] 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.

[0090] In this invention, the server includes means for receiving information from an observation support device and converting that information into a unified format, means for analyzing the information using an analysis method, and means for managing and visualizing the analyzed information and detecting anomalies. This enables real-time anomaly detection and efficient optimization of resource allocation in public facilities.

[0091] An "observation support device" is a device that uses sensors and equipment to collect specific information and provides it to a processing system.

[0092] "Means of converting information into a unified format" refers to a function that performs the process of converting information collected in various formats into a consistent format suitable for analysis and visualization.

[0093] "Means of analysis using analytical methods" refers to means of performing analysis using specific algorithms and techniques based on collected information, in order to extract meaningful data.

[0094] "Means for management, visualization, and anomaly detection" refers to functions that organize and visually present analyzed information to quickly detect unusual behavior or patterns.

[0095] "Real-time anomaly detection" is a process that analyzes collected data sequentially to immediately detect anomalies and enable appropriate responses.

[0096] "Resource allocation optimization" means efficiently planning and coordinating the allocation of available resources to achieve maximum safety and effectiveness with minimum resources.

[0097] To realize this application, the server receives information from the observation support device and converts it into a unified format. The server then applies analytical methods to analyze this information and manages and visualizes the data in real time. The analyzed information is displayed on a management dashboard, and when an anomaly is detected, it is visualized as an alarm.

[0098] The server uses Python and retrieves data from the internet using the requests library. This data is received in JSON format and visualized using the matplotlib library. This allows users to intuitively understand security anomalies and abnormal behavioral patterns based on environmental data collected by sensors installed in a public facility.

[0099] Users utilize an operation screen that provides analysis results and make appropriate decisions based on the obtained data. This operation screen is accessible via a browser and also includes a notification function that enables rapid response when an anomaly is detected.

[0100] For example, if unusual temperature changes or movements are detected during the night, an alert will be displayed on the user's PC or smart device. This approach can enhance security within public facilities and support a rapid response.

[0101] An example of a prompt statement is "Describe a method for detecting sensor anomalies in real time," which is used as an instruction to the generative AI model. This prompt allows the generative AI model to suggest similar analysis methods and code, supporting further system improvements.

[0102] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0103] Step 1:

[0104] The server receives data from the observation support equipment. Raw data from the equipment is provided as input. The server receives this data and converts it into a unified format such as JSON. This process ensures that diverse data obtained from different equipment is formatted for analysis.

[0105] Step 2:

[0106] The server performs analysis on the transformed data using analytical methods. The input is data in a unified format, and the server applies analytical algorithms to detect anomalies and patterns in the data. The output is the anomalies and associated characteristics that may have been detected by the analysis. Statistical methods and machine learning algorithms may be used in this process to distinguish between normal and abnormal data.

[0107] Step 3:

[0108] The analysis results are managed and visualized on the terminal. The terminal receives this analyzed data and presents it to the user through an interactive dashboard. The input is the analyzed data, and the output is graphs and alarm displays that the user can intuitively understand. Based on this visual information, the user can identify anomalies and take immediate action.

[0109] Step 4:

[0110] Users make decisions using information provided by their devices. Inputs are anomaly notifications and graph information displayed on the device, while outputs are specific actions taken by the user. For example, if an anomaly is detected in a specific area, the user can take actions such as conducting an on-site investigation or activating a notification system. This process also leads to the exploration of further improvements and analysis methods by inputting prompts into the generated AI model.

[0111] 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.

[0112] An embodiment of the present invention is centered around a server that receives data from an observation device and converts it into a unified format. The server processes the received data using an analysis algorithm and manages and visualizes the resulting analysis. Furthermore, this system integrates an emotion engine that recognizes and analyzes the user's emotions, and identifies the emotional state by utilizing the user's voice and text input.

[0113] The device features an interface to provide users with information and support their decision-making. It effectively visualizes analysis results while taking into account the user's emotional state. For example, if the emotion engine detects the user's anxiety, the device adjusts how information is presented, adopting a more user-friendly graphical representation.

[0114] The server has the means to readjust resource allocation based on the analyzed data and optimize the overall operational efficiency of the system. In this process, security protocols are applied to ensure the confidentiality and safety of the data. All communications, including user sentiment data, are encrypted and protected from unauthorized access.

[0115] As a concrete example, consider the case of a user who uses climate change information. The user receives real-time weather data through their device and makes strategic decisions based on the insights gained from it. In this process, the emotion engine detects the user's anxiety and provides support to deepen the user's understanding by changing the presentation style and simplifying the explanation. Through these adjustments, the user can make decisions confidently without feeling stressed.

[0116] The following describes the processing flow.

[0117] Step 1:

[0118] The server receives data transmitted from observation instruments placed in space. This process involves establishing a communication channel and verifying that the data is received in a consistent manner.

[0119] Step 2:

[0120] The server converts the received data into a unified format. This includes cross-checking the data and resolving inconsistencies, preparing it for efficient operation of the analysis algorithms.

[0121] Step 3:

[0122] The server activates an analysis algorithm to analyze data in a unified format. If image data is included, this algorithm identifies specific patterns or changes and extracts meaningful information.

[0123] Step 4:

[0124] The terminal reflects the analyzed data on a management dashboard, visualizing it in a user-accessible format. Here, information is presented using easy-to-understand graphs and maps, allowing users to easily grasp the data.

[0125] Step 5:

[0126] The emotion engine analyzes the user's emotional state based on voice or text input. This allows it to understand the user's psychological state and adjust the interface's responsiveness and appearance as needed.

[0127] Step 6:

[0128] Users make decisions based on information provided through their devices. For example, when making strategic decisions based on climate data, if the emotion engine determines that the user is in a state of anxiety, it simplifies the presentation of information and provides support to encourage calm decision-making.

[0129] Step 7:

[0130] The server optimizes the allocation of available resources based on the analyzed data. This includes efficient resource reallocation and suggests settings that improve the operational efficiency of the associated infrastructure.

[0131] Step 8:

[0132] The server applies security protocols to all data processing and communication to protect the confidentiality and integrity of information. It prevents unauthorized access and securely manages data related to user sentiment.

[0133] (Example 2)

[0134] 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".

[0135] Conventional data processing systems have struggled to manage data in different formats in a unified manner and to provide decision-making support while understanding the user's psychological state. Furthermore, insufficient resource optimization and security enhancements have hindered efficient data utilization. To address these issues, a system is needed that comprehensively handles everything from data reception and analysis to user interface provision and efficient resource utilization.

[0136] 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.

[0137] In this invention, the server includes means for receiving data from an observation device and converting the data into a unified format, means for analyzing the data using an analysis algorithm and storing the results in a recording device, and means for recognizing the user's emotional state and providing an interface to support decision-making by providing information tailored to that state. This enables the efficient integration of data in different formats, the provision of information that takes the user's emotions into consideration, and achieves resource optimization and improved security.

[0138] An "observation device" is a device used to collect data in an external environment or under specific circumstances, and is a device that has the function of acquiring and recording data.

[0139] A "unified format" is a data format that facilitates data processing and analysis by converting data obtained in different formats and structures into a consistent format.

[0140] An "analysis algorithm" is a set of computational procedures designed to evaluate data or perform pattern recognition, and is a method used to obtain specific results or insights.

[0141] A "recording device" is a device for storing data on a storage medium, and is a device that enables the retention of information and its access at a later date.

[0142] A "visualization device" is a device or software used to visually represent data, generating charts and graphs to facilitate understanding of the data.

[0143] "Emotional state" refers to a user's psychological or emotional condition, and is a subjective state analyzed using an emotion engine.

[0144] "Interface provision means" refers to means that enable the exchange of information between the user and the system, and includes user interfaces that function as a window for operation and information provision.

[0145] "Resource optimization" refers to techniques for efficiently allocating available resources to maximize performance.

[0146] "Encryption technology" is a technology used to protect data from unauthorized external access by encrypting it, thereby ensuring the confidentiality and security of the data.

[0147] A "generative AI model" is a model trained to generate new insights or outputs from data using artificial intelligence technology, and it processes prompts and other text as input.

[0148] A "prompt statement" is an input statement given to a generative AI model, and it is text that provides instructions or information for the model to process.

[0149] This invention relates to a system that receives data from observation devices, converts it into a unified format, analyzes it, and manages it. The server first receives data from the observation devices. The received data is converted into a data frame using the Python library Pandas and managed in a consistent format.

[0150] Next, the server analyzes the data trends using an analysis algorithm based on NumPy. The analysis results are stored in a database and managed visually using visualization tools. An SQL database is used for storing and retrieving results, while tools such as Chart.js are used for visualization.

[0151] Furthermore, the server utilizes an emotion engine to analyze user input. This engine employs natural language processing (NLP) technology, with the Google® Cloud Natural Language API analyzing the user's voice and text input to identify their emotional state. This enables the provision of information that takes the user's psychological state into consideration.

[0152] The device provides analysis results to the user through a user-friendly interface. For example, if the emotion engine detects anxiety, the device presents the information in a more user-friendly design. This allows the user to receive the information with confidence and make informed decisions.

[0153] Furthermore, the server uses AWS® Lambda to automatically optimize and efficiently reallocate available resources. The server applies the SSL / TLS protocol to all communications and maintains data confidentiality using AES encryption technology.

[0154] For example, if a user wants to create a presentation based on next week's weather forecast using climate change information, they can input a prompt message into the AI ​​model saying, "I want to prepare the data for the next meeting." This prompt message allows the system to automatically perform appropriate data processing and support the user's decision-making.

[0155] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0156] Step 1:

[0157] The server receives data from the observation device. The observation device collects and transmits various environmental data. The server receives this data and first checks its format. The input is raw data sent from the observation device, which is converted into a unified format suitable for processing within the server.

[0158] Step 2:

[0159] The server processes the data, which has been converted to a unified format, using an analysis algorithm. This analysis uses the numerical computation library NumPy to evaluate trends across the entire dataset. It also runs an anomaly detection algorithm to identify anomalies within the data. The input is data in a unified format, and the output is the analyzed information and an anomaly report.

[0160] Step 3:

[0161] The server stores the analysis results in a recording device and manages and visualizes them using a visualization device. Chart.js is used for visualization, graphing the data in a user-friendly format. The input is the analyzed data, and the output is a graphical report that the user can view.

[0162] Step 4:

[0163] The server uses an emotion engine to analyze the user's voice and text input. Natural language processing techniques are used to identify the user's emotional state from their speech and text. Google Cloud Natural Language API is utilized here. Input is user voice and text data, and output is the identified emotional state.

[0164] Step 5:

[0165] The terminal provides the user with a visualized version of the analysis results through an interface. The display method is adjusted to reflect the user's emotional state. For example, if anxiety is detected, a user-friendly design is adopted to reduce complexity. The input consists of the analysis results and emotional data, while the output is the adjusted interface.

[0166] Step 6:

[0167] The server optimizes resources and automatically reallocates them. It utilizes cloud features such as AWS Lambda to dynamically allocate computing resources according to the load. This process improves the overall system efficiency. The input is the system load, and the output is the optimized resource allocation.

[0168] Step 7:

[0169] The server protects all communications using encryption technology. By applying the SSL / TLS protocol and encrypting all data transmission and reception, data confidentiality is ensured. Input is the data being transmitted, and output is the encrypted transmitted data.

[0170] Step 8:

[0171] The user inputs prompts into a generative AI model, and the system performs appropriate data processing based on those instructions. The model receives user requests as input, and results are obtained based on those requests. Through this process, user support and decision-making are effectively facilitated. The input is prompts, and the output is the information and insights the user seeks.

[0172] (Application Example 2)

[0173] 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".

[0174] In recent years, with the spread of e-commerce and online payments, there has been a growing need to streamline user decision-making and improve the user experience. However, conventional systems often provide information without considering user emotions, which can cause stress and lead to decreased purchasing intent and a decline in the quality of the purchasing experience. There are also challenges in providing personalized product suggestions tailored to purchasing trends and in speeding up payments.

[0175] 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.

[0176] In this invention, the server includes means for receiving data from an observation device and converting that data into a unified format, means for an emotion engine that recognizes and analyzes the user's emotions and adjusts the method of providing information based on the user's emotional state, and means for analyzing the user's purchase data and providing product recommendations based on purchasing trends. This makes it possible to provide information that matches the user's emotional state, reducing stress and enabling optimized content and payment procedures.

[0177] An "observation device" is a device used to collect and detect data, and it has the role of transmitting necessary information to a server.

[0178] A "unified format" is a standard for converting data in different formats into a common format, ensuring data consistency and compatibility.

[0179] An "analysis algorithm" is a procedure for analyzing received data and extracting useful information; it is a means of improving the efficiency and accuracy of data analysis.

[0180] "Visualization" is the process of displaying analyzed data in a way that is easy for humans to understand, representing information in graphs, charts, and other visual formats.

[0181] An "emotion engine" is a technology that recognizes and analyzes emotions from a user's voice or text, and is a system for understanding the user's emotional state in real time.

[0182] An "interface provisioning method" is a method for exchanging information between the user and the system to support decision-making and improve the user experience.

[0183] "Resource optimization" is the process of efficiently using available resources to maximize their effectiveness, thereby improving the operational efficiency of a system.

[0184] A "security protocol" is a set of rules and procedures designed to ensure the confidentiality and integrity of data and protect it from unauthorized access.

[0185] "Product recommendation" is a method of suggesting appropriate products based on a user's purchasing tendencies and past purchase history, and is a means of providing a personalized shopping experience.

[0186] "Fast purchase procedures" refer to processes that simplify the user's purchasing process, reducing effort and time, and improving user convenience.

[0187] The system that realizes this application example works in conjunction with various devices to provide optimal information and process payments based on the user's emotions and purchasing behavior. Specifically, a server plays a central role, converting data received from observation devices into a unified format. This converted data is then analyzed in detail and visualized by an analysis algorithm. The server is equipped with an emotion engine that recognizes and analyzes the user's emotions, and grasps the user's emotional state through voice and text input.

[0188] The server provides information to the user's terminal based on analysis results and the user's emotional state. This supports the user's decision-making and improves the purchasing experience. This information provision includes product recommendations that take purchasing trends into account, presenting suitable options for the user. Furthermore, if the user is experiencing stress, the interface and operating procedures are simplified and adjusted to enable faster purchase processing.

[0189] Data security is a top priority in the operation of this system, and the servers use security protocols to protect all data communications. This ensures the confidentiality of user sentiment data and purchase information.

[0190] As a concrete example, consider the case of a user purchasing food online. When the user is choosing tonight's dinner, if the emotion engine detects the user's impatience, a "Buy Now" button will appear on the screen, allowing the user to complete the purchase with minimal input. Furthermore, based on past purchase history, food recommendations reflecting the user's preferences will be provided.

[0191] An example of a prompt for a generative AI model would be: "Create an AI response that recognizes the user's emotional state from their voice input and suggests a simplified purchase process if they are feeling stressed." This serves as input for the AI ​​to generate interactions that respond to the user's emotions.

[0192] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0193] Step 1:

[0194] The server receives data from the observation instrument and converts it into a unified format. By receiving raw data sent from the observation instrument and converting it into a unified format, it enables subsequent analysis algorithms to operate efficiently. The input consists of various data streams from the observation instrument, and the output is data formatted into a unified format.

[0195] Step 2:

[0196] The server analyzes the data, which has been converted to a unified format, using an analysis algorithm. In this step, specific trends and patterns are automatically detected using numerical parameters of the data. The input is formatted data, and the output is the analysis result. Specifically, data mining is performed using machine learning algorithms.

[0197] Step 3:

[0198] The server uses the user's voice and text input to recognize the user's emotional state using an emotion engine. The input consists of voice and text data provided by the user, and emotion analysis is performed to identify the emotional state as output. Specifically, it estimates emotions by combining a natural language processing model and voice analysis software.

[0199] Step 4:

[0200] The server provides information optimized for the user's terminal based on the analyzed data and the user's emotional state. The input is the analysis results and the user's emotional state, while the output is the customized information presented to the user. In this process, the user interface is dynamically adjusted to clearly and visually represent the information.

[0201] Step 5:

[0202] The device analyzes the user's purchasing patterns and generates recommended products. It takes past purchase history and current purchase candidates as input and outputs personalized product recommendations. In practice, a collaborative filtering algorithm is used to display the most relevant products to the user.

[0203] Step 6:

[0204] The terminal displays an interface tailored to the user's emotional state and provides a simplified procedure. Inputs are the user's emotional state and current purchase status, while output is a simple interface to facilitate the user's smooth purchase completion. Here, the user interface elements are dynamically adjusted to display appropriate buttons and navigation.

[0205] Step 7:

[0206] The server protects all data through security protocols and ensures user privacy. Inputs are processed analytical data and sentiment data, and outputs are encrypted data. This operation includes data encryption using the SSL / TLS protocol, and all communications are secure.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] [Second Embodiment]

[0211] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0212] 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.

[0213] 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).

[0214] 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.

[0215] 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.

[0216] 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).

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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".

[0223] In an embodiment of this invention, a dedicated software system operates on a server. This system receives data from observation devices, converts it to a unified format, and then processes the data by applying an analysis algorithm. The server displays the results of this processing on a management dashboard in real time and sends relevant information to the user.

[0224] The terminal provides visually analyzed information through a user-accessible interface. For example, it displays weather patterns and terrain changes based on data from space in interactive graphs. This information also supports decision-making, allowing users to develop operational plans and resource management strategies based on the data obtained.

[0225] The server further uses the acquired data to efficiently reallocate resources. This involves using optimization algorithms to propose settings that enhance the utilization efficiency of space equipment and ground infrastructure. Security protocols are employed to guarantee safety during data communication and prevent unauthorized access.

[0226] As a concrete example, consider a case where a user utilizes climate data from space for agricultural purposes. The user's device receives the latest climate analysis data from the server and displays time-series graphs showing fluctuations in precipitation and temperature for a specific region. This information is used to determine the optimal timing for planting and harvesting crops. This makes it possible to achieve more profitable agriculture and helps to significantly reduce resource waste.

[0227] The following describes the processing flow.

[0228] Step 1:

[0229] The server receives data transmitted from the observation equipment in real time. This involves establishing a stable connection with the observation equipment using advanced communication infrastructure and appropriately capturing diverse data formats.

[0230] Step 2:

[0231] The server converts the received data into a predetermined, unified format. This allows the analysis algorithm to process each data item consistently. This conversion process involves metadata extraction and reconstruction based on formatting rules.

[0232] Step 3:

[0233] The server applies an analysis algorithm to the transformed data. This algorithm identifies specific patterns in the image data and extracts significant information about climate and topography. The results are optimized through a feedback loop that further enhances the overall quality of the information obtained through the analysis.

[0234] Step 4:

[0235] The server stores the analysis results in a central database and reflects the information on the user dashboard. In this process, scalable database technology is employed for data management and visualization, ensuring fast query response times.

[0236] Step 5:

[0237] The terminal visualizes the analyzed information through a user-facing interface. Users can use this interface to view climate and topographic data in graphs and maps, and use this information to inform their decision-making. High-resolution joint maps and time-series graphs are used for visualization.

[0238] Step 6:

[0239] The server executes an automated resource allocation algorithm based on analysis data, with the aim of optimizing resources. This algorithm effectively adjusts the allocation of existing space equipment resources and makes suggestions to improve operational efficiency.

[0240] Step 7:

[0241] The server applies security protocols throughout the entire data communication process to protect data confidentiality and integrity. This ensures that only authorized users can access the information and prevents unauthorized manipulation of the data.

[0242] (Example 1)

[0243] 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."

[0244] In today's data collection environment, it is crucial to extract useful information from vast and diverse data formats and to manage resources efficiently and securely based on that information. However, traditional systems have faced challenges such as the time and cost involved in integrating different data formats, performing real-time analysis, providing visual information, and optimizing resources. Furthermore, there has been a lack of data security and an easy-to-use interface for users to utilize generated AI models.

[0245] 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.

[0246] In this invention, the server includes means for receiving data from an observation device and converting the data into a unified format, means for analyzing the data using an analysis algorithm, and means for deploying the analyzed data to a management dashboard in real time. This enables the integration of data formats, streamlining of the analysis process, real-time provision of visual information, secure data communication, and optimal utilization of resources.

[0247] An "observation device" refers to an instrument that collects physical or digital data from the external environment.

[0248] A "unified format" refers to a common data representation format used to convert data in different formats into a consistent format.

[0249] An "analytical algorithm" refers to a mathematical or computational method used to analyze data and extract useful information from it.

[0250] A "management dashboard" refers to a display system that visualizes analyzed data and has an interface for intuitive understanding.

[0251] A "visual interface" refers to an interactive display method for providing information to users graphically.

[0252] An "optimization algorithm" refers to a computational technique used to achieve the efficient allocation of resources.

[0253] A "security protocol" refers to encryption technologies and procedures used to maintain the security of data communications.

[0254] A "generative AI model" refers to an artificial intelligence model that generates information based on specified input conditions.

[0255] A "prompt" refers to a text-based instruction or question that is input into a generative AI model.

[0256] This invention is implemented by running a specific software system on a server. This system receives data in various formats from observation equipment and converts it into a unified format. The server processes the data using data transformation tools, such as ETL tools. The converted data is then analyzed in detail via analytical algorithms. Machine learning libraries such as Python's Scikit-learn and TensorFlow are used for the analysis, including climate pattern analysis and topographic change prediction.

[0257] The analysis results are displayed in real time on a management dashboard. This dashboard is built using visualization software such as Tableau or Grafana as tools for data visualization. Users can interactively view these results through a visual interface provided via their device. For example, it is possible to display interactive graphs of climate data using libraries such as D3.js or Chart.js.

[0258] Users can make decisions based on the analysis data sent from the server, and further obtain detailed analysis results by inputting prompts to the generating AI model. These prompts function as instructions or questions for the generating AI, and are entered in the form of "Please suggest the optimal irrigation schedule for next month."

[0259] Furthermore, the server utilizes optimization algorithms to propose efficient resource allocation. Operations research techniques are used in this process. The server also implements security protocols, such as SSL / TLS, to ensure secure data communication and protect against unauthorized access.

[0260] For example, when users utilize climate data from space for agricultural purposes, this system allows them to view time-series graphs showing fluctuations in precipitation and temperature. Based on this information, it becomes possible to determine the optimal planting time for crops, thereby reducing resource waste.

[0261] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0262] Step 1:

[0263] The server receives data from observation instruments. These instruments transmit, for example, climate data or image data acquired from sensors placed in space. The input is raw sensor data, and the output is data prepared for conversion to a unified format. This process includes data format verification and metadata assignment.

[0264] Step 2:

[0265] The server converts the input data into a unified format. Specifically, it uses ETL (Extract, Transform, Load) tools to reshape data in different formats into a standardized format. The input is raw data, and the output is standardized data. This process prepares the data for subsequent analysis to proceed smoothly.

[0266] Step 3:

[0267] The server analyzes data by applying analytical algorithms to data in a unified format. This includes applying machine learning models using Python's Scikit-learn and TensorFlow. The input is standardized data, and the output is predicted data or classified information resulting from the analysis. Specific operations include predicting temperature changes and detecting anomalies from image data.

[0268] Step 4:

[0269] The server deploys the analysis results to a management dashboard in real time. Tools such as Tableau and Grafana are used for data visualization. The input is analyzed data, and the output is a visual report designed for intuitive user understanding. This phase specifically includes the generation of visual graphs and charts.

[0270] Step 5:

[0271] The terminal provides the user with a visual interface and displays the analysis results. The interface runs on a web browser and uses libraries such as D3.js and Chart.js to interactively visualize the data. The input is visual data sent from the server, and the output is a user-interactive interface.

[0272] Step 6:

[0273] The user makes a decision based on the displayed information and inputs prompts into the generating AI model. As a specific instruction for the analysis the user requests, they send prompts to the generating AI such as, "Please suggest the optimal irrigation schedule for next month." The input is a prompt to the generating AI, and the output is a report containing detailed analysis results and suggestions.

[0274] Step 7:

[0275] The server provides a plan for reallocated resources and proposes an optimal solution using operational research techniques. Inputs are analytical data and current resource allocation information, and output is an optimized resource allocation plan. Ultimately, this leads to improved resource efficiency and reduced operating costs.

[0276] (Application Example 1)

[0277] 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."

[0278] In modern public facilities, security measures are becoming increasingly complex and diverse, making it difficult to effectively analyze information in real time and to quickly detect and respond to anomalies. Furthermore, if resources are not properly allocated and readjusted, the effectiveness of security may decrease, requiring efficient operation. It is necessary to address these challenges and achieve safer and more efficient security management.

[0279] The specific processing by the specific processing unit 290 of the data processing apparatus 12 in Application Example 1 is realized by the following means.

[0280] In this invention, the server includes means for receiving information from the observation support device and converting the information into a unified format, means for analyzing the information by an analysis method, and means for managing and visualizing the analyzed information and detecting abnormalities. Thereby, it becomes possible to perform real-time abnormality detection in public facilities and optimize efficient resource allocation.

[0281] The "observation support device" is a device for collecting specific information using sensors and devices and providing it to a processing system.

[0282] The "means for converting information into a unified format" is a function that performs a process of converting information collected in various formats into a consistent format suitable for analysis and visualization.

[0283] The "means for analyzing by an analysis method" is means for performing analysis using a specific algorithm or method based on the collected information and extracting meaningful data.

[0284] The "means for managing, visualizing, and detecting abnormalities" is a function for quickly detecting unusual behaviors and patterns by organizing the analyzed information and presenting it visually.

[0285] "Real-time abnormality detection" is a process of sequentially analyzing the collected data, immediately detecting abnormalities, and enabling appropriate responses.

[0286] "Optimization of resource allocation" is to efficiently plan and adjust the allocation of available resources to achieve maximum safety and effect with minimal resources.

[0287] To realize this application, the server receives information from the observation support device and converts it into a unified format. The server then applies analytical methods to analyze this information and manages and visualizes the data in real time. The analyzed information is displayed on a management dashboard, and when an anomaly is detected, it is visualized as an alarm.

[0288] The server uses Python and retrieves data from the internet using the requests library. This data is received in JSON format and visualized using the matplotlib library. This allows users to intuitively understand security anomalies and abnormal behavioral patterns based on environmental data collected by sensors installed in a public facility.

[0289] Users utilize an operation screen that provides analysis results and make appropriate decisions based on the obtained data. This operation screen is accessible via a browser and also includes a notification function that enables rapid response when an anomaly is detected.

[0290] For example, if unusual temperature changes or movements are detected during the night, an alert will be displayed on the user's PC or smart device. This approach can enhance security within public facilities and support a rapid response.

[0291] An example of a prompt statement is "Describe a method for detecting sensor anomalies in real time," which is used as an instruction to the generative AI model. This prompt allows the generative AI model to suggest similar analysis methods and code, supporting further system improvements.

[0292] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0293] Step 1:

[0294] The server receives data from the observation support equipment. Raw data from the equipment is provided as input. The server receives this data and converts it into a unified format such as JSON. This process ensures that diverse data obtained from different equipment is formatted for analysis.

[0295] Step 2:

[0296] The server performs analysis on the transformed data using analytical methods. The input is data in a unified format, and the server applies analytical algorithms to detect anomalies and patterns in the data. The output is the anomalies and associated characteristics that may have been detected by the analysis. Statistical methods and machine learning algorithms may be used in this process to distinguish between normal and abnormal data.

[0297] Step 3:

[0298] The analysis results are managed and visualized on the terminal. The terminal receives this analyzed data and presents it to the user through an interactive dashboard. The input is the analyzed data, and the output is graphs and alarm displays that the user can intuitively understand. Based on this visual information, the user can identify anomalies and take immediate action.

[0299] Step 4:

[0300] Users make decisions using information provided by their devices. Inputs are anomaly notifications and graph information displayed on the device, while outputs are specific actions taken by the user. For example, if an anomaly is detected in a specific area, the user can take actions such as conducting an on-site investigation or activating a notification system. This process also leads to the exploration of further improvements and analysis methods by inputting prompts into the generated AI model.

[0301] 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.

[0302] An embodiment of the present invention is centered around a server that receives data from an observation device and converts it into a unified format. The server processes the received data using an analysis algorithm and manages and visualizes the resulting analysis. Furthermore, this system integrates an emotion engine that recognizes and analyzes the user's emotions, and identifies the emotional state by utilizing the user's voice and text input.

[0303] The device features an interface to provide users with information and support their decision-making. It effectively visualizes analysis results while taking into account the user's emotional state. For example, if the emotion engine detects the user's anxiety, the device adjusts how information is presented, adopting a more user-friendly graphical representation.

[0304] The server has the means to readjust resource allocation based on the analyzed data and optimize the overall operational efficiency of the system. In this process, security protocols are applied to ensure the confidentiality and safety of the data. All communications, including user sentiment data, are encrypted and protected from unauthorized access.

[0305] As a concrete example, consider the case of a user who uses climate change information. The user receives real-time weather data through their device and makes strategic decisions based on the insights gained from it. In this process, the emotion engine detects the user's anxiety and provides support to deepen the user's understanding by changing the presentation style and simplifying the explanation. Through these adjustments, the user can make decisions confidently without feeling stressed.

[0306] The following describes the processing flow.

[0307] Step 1:

[0308] The server receives data transmitted from observation devices deployed in space. This process includes establishing a communication channel and verifying that the data is received in a consistent form.

[0309] Step 2:

[0310] The server converts the received data into a unified format. This includes cross-checking the data and resolving inconsistencies, and serves to prepare the data so that the analysis algorithm can operate efficiently.

[0311] Step 3:

[0312] The server activates the analysis algorithm and analyzes the data in the unified format. When the algorithm includes image data, it identifies specific patterns and changes and extracts meaningful information.

[0313] Step 4:

[0314] The terminal reflects the analyzed data on the management dashboard and visualizes it in a form accessible to the user. Here, information is presented using easy-to-understand graphs and maps so that the user can easily grasp the data.

[0315] Step 5:

[0316] The emotion engine analyzes the emotional state based on voice or text input from the user. This enables understanding of the user's psychological state and adjustment of the responsiveness and appearance of the interface as needed.

[0317] Step 6:

[0318] Users make decisions based on information provided through their devices. For example, when making strategic decisions based on climate data, if the emotion engine determines that the user is in a state of anxiety, it simplifies the presentation of information and provides support to encourage calm decision-making.

[0319] Step 7:

[0320] The server optimizes the allocation of available resources based on the analyzed data. This includes efficient resource reallocation and suggests settings that improve the operational efficiency of the associated infrastructure.

[0321] Step 8:

[0322] The server applies security protocols to all data processing and communication to protect the confidentiality and integrity of information. It prevents unauthorized access and securely manages data related to user sentiment.

[0323] (Example 2)

[0324] 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".

[0325] Conventional data processing systems have struggled to manage data in different formats in a unified manner and to provide decision-making support while understanding the user's psychological state. Furthermore, insufficient resource optimization and security enhancements have hindered efficient data utilization. To address these issues, a system is needed that comprehensively handles everything from data reception and analysis to user interface provision and efficient resource utilization.

[0326] 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.

[0327] In this invention, the server includes means for receiving data from an observation device and converting the data into a unified format, means for analyzing the data using an analysis algorithm and storing the results in a recording device, and means for recognizing the user's emotional state and providing an interface to support decision-making by providing information tailored to that state. This enables the efficient integration of data in different formats, the provision of information that takes the user's emotions into consideration, and achieves resource optimization and improved security.

[0328] An "observation device" is a device used to collect data in an external environment or under specific circumstances, and is a device that has the function of acquiring and recording data.

[0329] A "unified format" is a data format that facilitates data processing and analysis by converting data obtained in different formats and structures into a consistent format.

[0330] An "analysis algorithm" is a set of computational procedures designed to evaluate data or perform pattern recognition, and is a method used to obtain specific results or insights.

[0331] A "recording device" is a device for storing data on a storage medium, and is a device that enables the retention of information and its access at a later date.

[0332] A "visualization device" is a device or software used to visually represent data, generating charts and graphs to facilitate understanding of the data.

[0333] "Emotional state" refers to a user's psychological or emotional condition, and is a subjective state analyzed using an emotion engine.

[0334] "Interface provision means" refers to means that enable the exchange of information between the user and the system, and includes user interfaces that function as a window for operation and information provision.

[0335] "Resource optimization" refers to techniques for efficiently allocating available resources to maximize performance.

[0336] "Encryption technology" is a technology used to protect data from unauthorized external access by encrypting it, thereby ensuring the confidentiality and security of the data.

[0337] A "generative AI model" is a model trained to generate new insights or outputs from data using artificial intelligence technology, and it processes prompts and other text as input.

[0338] A "prompt statement" is an input statement given to a generative AI model, and it is text that provides instructions or information for the model to process.

[0339] This invention relates to a system that receives data from observation devices, converts it into a unified format, analyzes it, and manages it. The server first receives data from the observation devices. The received data is converted into a data frame using the Python library Pandas and managed in a consistent format.

[0340] Next, the server analyzes the data trends using an analysis algorithm based on NumPy. The analysis results are stored in a database and managed visually using visualization tools. An SQL database is used for storing and retrieving results, while tools such as Chart.js are used for visualization.

[0341] Furthermore, the server utilizes an emotion engine to analyze user input. This engine employs natural language processing (NLP) technology, with the Google Cloud Natural Language API analyzing the user's voice and text input to identify their emotional state. This enables the provision of information that takes the user's psychological state into consideration.

[0342] The device provides analysis results to the user through a user-friendly interface. For example, if the emotion engine detects anxiety, the device presents the information in a more user-friendly design. This allows the user to receive the information with confidence and make informed decisions.

[0343] Furthermore, the servers use AWS Lambda to automatically optimize and efficiently reallocate available resources. The servers apply the SSL / TLS protocol to all communications and maintain data confidentiality using AES encryption technology.

[0344] For example, if a user wants to create a presentation based on next week's weather forecast using climate change information, they can input a prompt message into the AI ​​model saying, "I want to prepare the data for the next meeting." This prompt message allows the system to automatically perform appropriate data processing and support the user's decision-making.

[0345] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0346] Step 1:

[0347] The server receives data from the observation device. The observation device collects and transmits various environmental data. The server receives this data and first checks its format. The input is raw data sent from the observation device, which is converted into a unified format suitable for processing within the server.

[0348] Step 2:

[0349] The server processes the data, which has been converted to a unified format, using an analysis algorithm. This analysis uses the numerical computation library NumPy to evaluate trends across the entire dataset. It also runs an anomaly detection algorithm to identify anomalies within the data. The input is data in a unified format, and the output is the analyzed information and an anomaly report.

[0350] Step 3:

[0351] The server stores the analysis results in a recording device and manages and visualizes them using a visualization device. Chart.js is used for visualization, graphing the data in a user-friendly format. The input is the analyzed data, and the output is a graphical report that the user can view.

[0352] Step 4:

[0353] The server uses an emotion engine to analyze the user's voice and text input. Natural language processing techniques are used to identify the user's emotional state from their speech and text. Google Cloud Natural Language API is utilized here. Input is user voice and text data, and output is the identified emotional state.

[0354] Step 5:

[0355] The terminal provides the user with a visualized version of the analysis results through an interface. The display method is adjusted to reflect the user's emotional state. For example, if anxiety is detected, a user-friendly design is adopted to reduce complexity. The input consists of the analysis results and emotional data, while the output is the adjusted interface.

[0356] Step 6:

[0357] The server optimizes resources and automatically reallocates them. It utilizes cloud features such as AWS Lambda to dynamically allocate computing resources according to the load. This process improves the overall system efficiency. The input is the system load, and the output is the optimized resource allocation.

[0358] Step 7:

[0359] The server protects all communications using encryption technology. By applying the SSL / TLS protocol and encrypting all data transmission and reception, data confidentiality is ensured. Input is the data being transmitted, and output is the encrypted transmitted data.

[0360] Step 8:

[0361] The user inputs prompts into a generative AI model, and the system performs appropriate data processing based on those instructions. The model receives user requests as input, and results are obtained based on those requests. Through this process, user support and decision-making are effectively facilitated. The input is prompts, and the output is the information and insights the user seeks.

[0362] (Application Example 2)

[0363] 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."

[0364] In recent years, with the spread of e-commerce and online payments, there has been a growing need to streamline user decision-making and improve the user experience. However, conventional systems often provide information without considering user emotions, which can cause stress and lead to decreased purchasing intent and a decline in the quality of the purchasing experience. There are also challenges in providing personalized product suggestions tailored to purchasing trends and in speeding up payments.

[0365] 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.

[0366] In this invention, the server includes means for receiving data from an observation device and converting that data into a unified format, means for an emotion engine that recognizes and analyzes the user's emotions and adjusts the method of providing information based on the user's emotional state, and means for analyzing the user's purchase data and providing product recommendations based on purchasing trends. This makes it possible to provide information that matches the user's emotional state, reducing stress and enabling optimized content and payment procedures.

[0367] An "observation device" is a device used to collect and detect data, and it has the role of transmitting necessary information to a server.

[0368] A "unified format" is a standard for converting data in different formats into a common format, ensuring data consistency and compatibility.

[0369] An "analysis algorithm" is a procedure for analyzing received data and extracting useful information; it is a means of improving the efficiency and accuracy of data analysis.

[0370] "Visualization" is the process of displaying analyzed data in a way that is easy for humans to understand, representing information in graphs, charts, and other visual formats.

[0371] An "emotion engine" is a technology that recognizes and analyzes emotions from a user's voice or text, and is a system for understanding the user's emotional state in real time.

[0372] An "interface provisioning method" is a method for exchanging information between the user and the system to support decision-making and improve the user experience.

[0373] "Resource optimization" is the process of efficiently using available resources to maximize their effectiveness, thereby improving the operational efficiency of a system.

[0374] A "security protocol" is a set of rules and procedures designed to ensure the confidentiality and integrity of data and protect it from unauthorized access.

[0375] "Product recommendation" is a method of suggesting appropriate products based on a user's purchasing tendencies and past purchase history, and is a means of providing a personalized shopping experience.

[0376] "Fast purchase procedures" refer to processes that simplify the user's purchasing process, reducing effort and time, and improving user convenience.

[0377] The system that realizes this application example works in conjunction with various devices to provide optimal information and process payments based on the user's emotions and purchasing behavior. Specifically, a server plays a central role, converting data received from observation devices into a unified format. This converted data is then analyzed in detail and visualized by an analysis algorithm. The server is equipped with an emotion engine that recognizes and analyzes the user's emotions, and grasps the user's emotional state through voice and text input.

[0378] The server provides information to the user's terminal based on analysis results and the user's emotional state. This supports the user's decision-making and improves the purchasing experience. This information provision includes product recommendations that take purchasing trends into account, presenting suitable options for the user. Furthermore, if the user is experiencing stress, the interface and operating procedures are simplified and adjusted to enable faster purchase processing.

[0379] Data security is a top priority in the operation of this system, and the servers use security protocols to protect all data communications. This ensures the confidentiality of user sentiment data and purchase information.

[0380] As a concrete example, consider the case of a user purchasing food online. When the user is choosing tonight's dinner, if the emotion engine detects the user's impatience, a "Buy Now" button will appear on the screen, allowing the user to complete the purchase with minimal input. Furthermore, based on past purchase history, food recommendations reflecting the user's preferences will be provided.

[0381] An example of a prompt for a generative AI model would be: "Create an AI response that recognizes the user's emotional state from their voice input and suggests a simplified purchase process if they are feeling stressed." This serves as input for the AI ​​to generate interactions that respond to the user's emotions.

[0382] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0383] Step 1:

[0384] The server receives data from the observation instrument and converts it into a unified format. By receiving raw data sent from the observation instrument and converting it into a unified format, it enables subsequent analysis algorithms to operate efficiently. The input consists of various data streams from the observation instrument, and the output is data formatted into a unified format.

[0385] Step 2:

[0386] The server analyzes the data, which has been converted to a unified format, using an analysis algorithm. In this step, specific trends and patterns are automatically detected using numerical parameters of the data. The input is formatted data, and the output is the analysis result. Specifically, data mining is performed using machine learning algorithms.

[0387] Step 3:

[0388] The server uses the user's voice and text input to recognize the user's emotional state using an emotion engine. The input consists of voice and text data provided by the user, and emotion analysis is performed to identify the emotional state as output. Specifically, it estimates emotions by combining a natural language processing model and voice analysis software.

[0389] Step 4:

[0390] The server provides information optimized for the user's terminal based on the analyzed data and the user's emotional state. The input is the analysis results and the user's emotional state, while the output is the customized information presented to the user. In this process, the user interface is dynamically adjusted to clearly and visually represent the information.

[0391] Step 5:

[0392] The device analyzes the user's purchasing patterns and generates recommended products. It takes past purchase history and current purchase candidates as input and outputs personalized product recommendations. In practice, a collaborative filtering algorithm is used to display the most relevant products to the user.

[0393] Step 6:

[0394] The terminal displays an interface tailored to the user's emotional state and provides a simplified procedure. Inputs are the user's emotional state and current purchase status, while output is a simple interface to facilitate the user's smooth purchase completion. Here, the user interface elements are dynamically adjusted to display appropriate buttons and navigation.

[0395] Step 7:

[0396] The server protects all data through security protocols and ensures user privacy. Inputs are processed analytical data and sentiment data, and outputs are encrypted data. This operation includes data encryption using the SSL / TLS protocol, and all communications are secure.

[0397] 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.

[0398] 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.

[0399] 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.

[0400] [Third Embodiment]

[0401] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0402] 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.

[0403] 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).

[0404] 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.

[0405] 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.

[0406] 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).

[0407] 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.

[0408] 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.

[0409] 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.

[0410] 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.

[0411] 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.

[0412] 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".

[0413] In an embodiment of this invention, a dedicated software system operates on a server. This system receives data from observation devices, converts it to a unified format, and then processes the data by applying an analysis algorithm. The server displays the results of this processing on a management dashboard in real time and sends relevant information to the user.

[0414] The terminal provides visually analyzed information through a user-accessible interface. For example, it displays weather patterns and terrain changes based on data from space in interactive graphs. This information also supports decision-making, allowing users to develop operational plans and resource management strategies based on the data obtained.

[0415] The server further uses the acquired data to efficiently reallocate resources. This involves using optimization algorithms to propose settings that enhance the utilization efficiency of space equipment and ground infrastructure. Security protocols are employed to guarantee safety during data communication and prevent unauthorized access.

[0416] As a concrete example, consider a case where a user utilizes climate data from space for agricultural purposes. The user's device receives the latest climate analysis data from the server and displays time-series graphs showing fluctuations in precipitation and temperature for a specific region. This information is used to determine the optimal timing for planting and harvesting crops. This makes it possible to achieve more profitable agriculture and helps to significantly reduce resource waste.

[0417] The following describes the processing flow.

[0418] Step 1:

[0419] The server receives data transmitted from the observation equipment in real time. This involves establishing a stable connection with the observation equipment using advanced communication infrastructure and appropriately capturing diverse data formats.

[0420] Step 2:

[0421] The server converts the received data into a predetermined, unified format. This allows the analysis algorithm to process each data item consistently. This conversion process involves metadata extraction and reconstruction based on formatting rules.

[0422] Step 3:

[0423] The server applies an analysis algorithm to the transformed data. This algorithm identifies specific patterns in the image data and extracts significant information about climate and topography. The results are optimized through a feedback loop that further enhances the overall quality of the information obtained through the analysis.

[0424] Step 4:

[0425] The server stores the analysis results in a central database and reflects the information on the user dashboard. In this process, scalable database technology is employed for data management and visualization, ensuring fast query response times.

[0426] Step 5:

[0427] The terminal visualizes the analyzed information through a user-facing interface. Users can use this interface to view climate and topographic data in graphs and maps, and use this information to inform their decision-making. High-resolution joint maps and time-series graphs are used for visualization.

[0428] Step 6:

[0429] The server executes an automated resource allocation algorithm based on analysis data, with the aim of optimizing resources. This algorithm effectively adjusts the allocation of existing space equipment resources and makes suggestions to improve operational efficiency.

[0430] Step 7:

[0431] The server applies security protocols throughout the entire data communication process to protect data confidentiality and integrity. This ensures that only authorized users can access the information and prevents unauthorized manipulation of the data.

[0432] (Example 1)

[0433] 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."

[0434] In today's data collection environment, it is crucial to extract useful information from vast and diverse data formats and to manage resources efficiently and securely based on that information. However, traditional systems have faced challenges such as the time and cost involved in integrating different data formats, performing real-time analysis, providing visual information, and optimizing resources. Furthermore, there has been a lack of data security and an easy-to-use interface for users to utilize generated AI models.

[0435] 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.

[0436] In this invention, the server includes means for receiving data from an observation device and converting the data into a unified format, means for analyzing the data using an analysis algorithm, and means for deploying the analyzed data to a management dashboard in real time. This enables the integration of data formats, streamlining of the analysis process, real-time provision of visual information, secure data communication, and optimal utilization of resources.

[0437] An "observation device" refers to an instrument that collects physical or digital data from the external environment.

[0438] A "unified format" refers to a common data representation format used to convert data in different formats into a consistent format.

[0439] An "analytical algorithm" refers to a mathematical or computational method used to analyze data and extract useful information from it.

[0440] A "management dashboard" refers to a display system that visualizes analyzed data and has an interface for intuitive understanding.

[0441] A "visual interface" refers to an interactive display method for providing information to users graphically.

[0442] An "optimization algorithm" refers to a computational technique used to achieve the efficient allocation of resources.

[0443] A "security protocol" refers to encryption technologies and procedures used to maintain the security of data communications.

[0444] A "generative AI model" refers to an artificial intelligence model that generates information based on specified input conditions.

[0445] A "prompt" refers to a text-based instruction or question that is input into a generative AI model.

[0446] This invention is implemented by running a specific software system on a server. This system receives data in various formats from observation equipment and converts it into a unified format. The server processes the data using data transformation tools, such as ETL tools. The converted data is then analyzed in detail via analytical algorithms. Machine learning libraries such as Python's Scikit-learn and TensorFlow are used for the analysis, including climate pattern analysis and topographic change prediction.

[0447] The analysis results are displayed in real time on a management dashboard. This dashboard is built using visualization software such as Tableau or Grafana as tools for data visualization. Users can interactively view these results through a visual interface provided via their device. For example, it is possible to display interactive graphs of climate data using libraries such as D3.js or Chart.js.

[0448] Users can make decisions based on the analysis data sent from the server, and further obtain detailed analysis results by inputting prompts to the generating AI model. These prompts function as instructions or questions for the generating AI, and are entered in the form of "Please suggest the optimal irrigation schedule for next month."

[0449] Furthermore, the server utilizes optimization algorithms to propose efficient resource allocation. Operations research techniques are used in this process. The server also implements security protocols, such as SSL / TLS, to ensure secure data communication and protect against unauthorized access.

[0450] For example, when users utilize climate data from space for agricultural purposes, this system allows them to view time-series graphs showing fluctuations in precipitation and temperature. Based on this information, it becomes possible to determine the optimal planting time for crops, thereby reducing resource waste.

[0451] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0452] Step 1:

[0453] The server receives data from observation instruments. These instruments transmit, for example, climate data or image data acquired from sensors placed in space. The input is raw sensor data, and the output is data prepared for conversion to a unified format. This process includes data format verification and metadata assignment.

[0454] Step 2:

[0455] The server converts the input data into a unified format. Specifically, it uses ETL (Extract, Transform, Load) tools to reshape data in different formats into a standardized format. The input is raw data, and the output is standardized data. This process prepares the data for subsequent analysis to proceed smoothly.

[0456] Step 3:

[0457] The server analyzes data by applying analytical algorithms to data in a unified format. This includes applying machine learning models using Python's Scikit-learn and TensorFlow. The input is standardized data, and the output is predicted data or classified information resulting from the analysis. Specific operations include predicting temperature changes and detecting anomalies from image data.

[0458] Step 4:

[0459] The server deploys the analysis results to a management dashboard in real time. Tools such as Tableau and Grafana are used for data visualization. The input is analyzed data, and the output is a visual report designed for intuitive user understanding. This phase specifically includes the generation of visual graphs and charts.

[0460] Step 5:

[0461] The terminal provides the user with a visual interface and displays the analysis results. The interface runs on a web browser and uses libraries such as D3.js and Chart.js to interactively visualize the data. The input is visual data sent from the server, and the output is a user-interactive interface.

[0462] Step 6:

[0463] The user makes a decision based on the displayed information and inputs prompts into the generating AI model. As a specific instruction for the analysis the user requests, they send prompts to the generating AI such as, "Please suggest the optimal irrigation schedule for next month." The input is a prompt to the generating AI, and the output is a report containing detailed analysis results and suggestions.

[0464] Step 7:

[0465] The server provides a plan for reallocated resources and proposes an optimal solution using operational research techniques. Inputs are analytical data and current resource allocation information, and output is an optimized resource allocation plan. Ultimately, this leads to improved resource efficiency and reduced operating costs.

[0466] (Application Example 1)

[0467] 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."

[0468] In modern public facilities, security measures are becoming increasingly complex and diverse, making it difficult to effectively analyze information in real time and to quickly detect and respond to anomalies. Furthermore, if resources are not properly allocated and readjusted, the effectiveness of security may decrease, requiring efficient operation. It is necessary to address these challenges and achieve safer and more efficient security management.

[0469] 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.

[0470] In this invention, the server includes means for receiving information from an observation support device and converting that information into a unified format, means for analyzing the information using an analysis method, and means for managing and visualizing the analyzed information and detecting anomalies. This enables real-time anomaly detection and efficient optimization of resource allocation in public facilities.

[0471] An "observation support device" is a device that uses sensors and equipment to collect specific information and provides it to a processing system.

[0472] "Means of converting information into a unified format" refers to a function that performs the process of converting information collected in various formats into a consistent format suitable for analysis and visualization.

[0473] "Means of analysis using analytical methods" refers to means of performing analysis using specific algorithms and techniques based on collected information, in order to extract meaningful data.

[0474] "Means for management, visualization, and anomaly detection" refers to functions that organize and visually present analyzed information to quickly detect unusual behavior or patterns.

[0475] "Real-time anomaly detection" is a process that analyzes collected data sequentially to immediately detect anomalies and enable appropriate responses.

[0476] "Resource allocation optimization" means efficiently planning and coordinating the allocation of available resources to achieve maximum safety and effectiveness with minimum resources.

[0477] To realize this application, the server receives information from the observation support device and converts it into a unified format. The server then applies analytical methods to analyze this information and manages and visualizes the data in real time. The analyzed information is displayed on a management dashboard, and when an anomaly is detected, it is visualized as an alarm.

[0478] The server uses Python and retrieves data from the internet using the requests library. This data is received in JSON format and visualized using the matplotlib library. This allows users to intuitively understand security anomalies and abnormal behavioral patterns based on environmental data collected by sensors installed in a public facility.

[0479] Users utilize an operation screen that provides analysis results and make appropriate decisions based on the obtained data. This operation screen is accessible via a browser and also includes a notification function that enables rapid response when an anomaly is detected.

[0480] For example, if unusual temperature changes or movements are detected during the night, an alert will be displayed on the user's PC or smart device. This approach can enhance security within public facilities and support a rapid response.

[0481] An example of a prompt statement is "Describe a method for detecting sensor anomalies in real time," which is used as an instruction to the generative AI model. This prompt allows the generative AI model to suggest similar analysis methods and code, supporting further system improvements.

[0482] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0483] Step 1:

[0484] The server receives data from the observation support equipment. Raw data from the equipment is provided as input. The server receives this data and converts it into a unified format such as JSON. This process ensures that diverse data obtained from different equipment is formatted for analysis.

[0485] Step 2:

[0486] The server performs analysis on the transformed data using analytical methods. The input is data in a unified format, and the server applies analytical algorithms to detect anomalies and patterns in the data. The output is the anomalies and associated characteristics that may have been detected by the analysis. Statistical methods and machine learning algorithms may be used in this process to distinguish between normal and abnormal data.

[0487] Step 3:

[0488] The analysis results are managed and visualized on the terminal. The terminal receives this analyzed data and presents it to the user through an interactive dashboard. The input is the analyzed data, and the output is graphs and alarm displays that the user can intuitively understand. Based on this visual information, the user can identify anomalies and take immediate action.

[0489] Step 4:

[0490] Users make decisions using information provided by their devices. Inputs are anomaly notifications and graph information displayed on the device, while outputs are specific actions taken by the user. For example, if an anomaly is detected in a specific area, the user can take actions such as conducting an on-site investigation or activating a notification system. This process also leads to the exploration of further improvements and analysis methods by inputting prompts into the generated AI model.

[0491] 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.

[0492] An embodiment of the present invention is centered around a server that receives data from an observation device and converts it into a unified format. The server processes the received data using an analysis algorithm and manages and visualizes the resulting analysis. Furthermore, this system integrates an emotion engine that recognizes and analyzes the user's emotions, and identifies the emotional state by utilizing the user's voice and text input.

[0493] The device features an interface to provide users with information and support their decision-making. It effectively visualizes analysis results while taking into account the user's emotional state. For example, if the emotion engine detects the user's anxiety, the device adjusts how information is presented, adopting a more user-friendly graphical representation.

[0494] The server has the means to readjust resource allocation based on the analyzed data and optimize the overall operational efficiency of the system. In this process, security protocols are applied to ensure the confidentiality and safety of the data. All communications, including user sentiment data, are encrypted and protected from unauthorized access.

[0495] As a concrete example, consider the case of a user who uses climate change information. The user receives real-time weather data through their device and makes strategic decisions based on the insights gained from it. In this process, the emotion engine detects the user's anxiety and provides support to deepen the user's understanding by changing the presentation style and simplifying the explanation. Through these adjustments, the user can make decisions confidently without feeling stressed.

[0496] The following describes the processing flow.

[0497] Step 1:

[0498] The server receives data transmitted from observation instruments placed in space. This process involves establishing a communication channel and verifying that the data is received in a consistent manner.

[0499] Step 2:

[0500] The server converts the received data into a unified format. This includes cross-checking the data and resolving inconsistencies, preparing it for efficient operation of the analysis algorithms.

[0501] Step 3:

[0502] The server activates an analysis algorithm to analyze data in a unified format. If image data is included, this algorithm identifies specific patterns or changes and extracts meaningful information.

[0503] Step 4:

[0504] The terminal reflects the analyzed data on a management dashboard, visualizing it in a user-accessible format. Here, information is presented using easy-to-understand graphs and maps, allowing users to easily grasp the data.

[0505] Step 5:

[0506] The emotion engine analyzes the user's emotional state based on voice or text input. This allows it to understand the user's psychological state and adjust the interface's responsiveness and appearance as needed.

[0507] Step 6:

[0508] Users make decisions based on information provided through their devices. For example, when making strategic decisions based on climate data, if the emotion engine determines that the user is in a state of anxiety, it simplifies the presentation of information and provides support to encourage calm decision-making.

[0509] Step 7:

[0510] The server optimizes the allocation of available resources based on the analyzed data. This includes efficient resource reallocation and suggests settings that improve the operational efficiency of the associated infrastructure.

[0511] Step 8:

[0512] The server applies security protocols to all data processing and communication to protect the confidentiality and integrity of information. It prevents unauthorized access and securely manages data related to user sentiment.

[0513] (Example 2)

[0514] 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."

[0515] Conventional data processing systems have struggled to manage data in different formats in a unified manner and to provide decision-making support while understanding the user's psychological state. Furthermore, insufficient resource optimization and security enhancements have hindered efficient data utilization. To address these issues, a system is needed that comprehensively handles everything from data reception and analysis to user interface provision and efficient resource utilization.

[0516] 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.

[0517] In this invention, the server includes means for receiving data from an observation device and converting the data into a unified format, means for analyzing the data using an analysis algorithm and storing the results in a recording device, and means for recognizing the user's emotional state and providing an interface to support decision-making by providing information tailored to that state. This enables the efficient integration of data in different formats, the provision of information that takes the user's emotions into consideration, and achieves resource optimization and improved security.

[0518] An "observation device" is a device used to collect data in an external environment or under specific circumstances, and is a device that has the function of acquiring and recording data.

[0519] A "unified format" is a data format that facilitates data processing and analysis by converting data obtained in different formats and structures into a consistent format.

[0520] An "analysis algorithm" is a set of computational procedures designed to evaluate data or perform pattern recognition, and is a method used to obtain specific results or insights.

[0521] A "recording device" is a device for storing data on a storage medium, and is a device that enables the retention of information and its access at a later date.

[0522] A "visualization device" is a device or software used to visually represent data, generating charts and graphs to facilitate understanding of the data.

[0523] "Emotional state" refers to a user's psychological or emotional condition, and is a subjective state analyzed using an emotion engine.

[0524] "Interface provision means" refers to means that enable the exchange of information between the user and the system, and includes user interfaces that function as a window for operation and information provision.

[0525] "Resource optimization" refers to techniques for efficiently allocating available resources to maximize performance.

[0526] "Encryption technology" is a technology used to protect data from unauthorized external access by encrypting it, thereby ensuring the confidentiality and security of the data.

[0527] A "generative AI model" is a model trained to generate new insights or outputs from data using artificial intelligence technology, and it processes prompts and other text as input.

[0528] A "prompt statement" is an input statement given to a generative AI model, and it is text that provides instructions or information for the model to process.

[0529] This invention relates to a system that receives data from observation devices, converts it into a unified format, analyzes it, and manages it. The server first receives data from the observation devices. The received data is converted into a data frame using the Python library Pandas and managed in a consistent format.

[0530] Next, the server analyzes the data trends using an analysis algorithm based on NumPy. The analysis results are stored in a database and managed visually using visualization tools. An SQL database is used for storing and retrieving results, while tools such as Chart.js are used for visualization.

[0531] Furthermore, the server utilizes an emotion engine to analyze user input. This engine employs natural language processing (NLP) technology, with the Google Cloud Natural Language API analyzing the user's voice and text input to identify their emotional state. This enables the provision of information that takes the user's psychological state into consideration.

[0532] The device provides analysis results to the user through a user-friendly interface. For example, if the emotion engine detects anxiety, the device presents the information in a more user-friendly design. This allows the user to receive the information with confidence and make informed decisions.

[0533] Furthermore, the servers use AWS Lambda to automatically optimize and efficiently reallocate available resources. The servers apply the SSL / TLS protocol to all communications and maintain data confidentiality using AES encryption technology.

[0534] For example, if a user wants to create a presentation based on next week's weather forecast using climate change information, they can input a prompt message into the AI ​​model saying, "I want to prepare the data for the next meeting." This prompt message allows the system to automatically perform appropriate data processing and support the user's decision-making.

[0535] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0536] Step 1:

[0537] The server receives data from the observation device. The observation device collects and transmits various environmental data. The server receives this data and first checks its format. The input is raw data sent from the observation device, which is converted into a unified format suitable for processing within the server.

[0538] Step 2:

[0539] The server processes the data, which has been converted to a unified format, using an analysis algorithm. This analysis uses the numerical computation library NumPy to evaluate trends across the entire dataset. It also runs an anomaly detection algorithm to identify anomalies within the data. The input is data in a unified format, and the output is the analyzed information and an anomaly report.

[0540] Step 3:

[0541] The server stores the analysis results in a recording device and manages and visualizes them using a visualization device. Chart.js is used for visualization, graphing the data in a user-friendly format. The input is the analyzed data, and the output is a graphical report that the user can view.

[0542] Step 4:

[0543] The server uses an emotion engine to analyze the user's voice and text input. Natural language processing techniques are used to identify the user's emotional state from their speech and text. Google Cloud Natural Language API is utilized here. Input is user voice and text data, and output is the identified emotional state.

[0544] Step 5:

[0545] The terminal provides the user with a visualized version of the analysis results through an interface. The display method is adjusted to reflect the user's emotional state. For example, if anxiety is detected, a user-friendly design is adopted to reduce complexity. The input consists of the analysis results and emotional data, while the output is the adjusted interface.

[0546] Step 6:

[0547] The server optimizes resources and automatically reallocates them. It utilizes cloud features such as AWS Lambda to dynamically allocate computing resources according to the load. This process improves the overall system efficiency. The input is the system load, and the output is the optimized resource allocation.

[0548] Step 7:

[0549] The server protects all communications using encryption technology. By applying the SSL / TLS protocol and encrypting all data transmission and reception, data confidentiality is ensured. Input is the data being transmitted, and output is the encrypted transmitted data.

[0550] Step 8:

[0551] The user inputs prompts into a generative AI model, and the system performs appropriate data processing based on those instructions. The model receives user requests as input, and results are obtained based on those requests. Through this process, user support and decision-making are effectively facilitated. The input is prompts, and the output is the information and insights the user seeks.

[0552] (Application Example 2)

[0553] 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."

[0554] In recent years, with the spread of e-commerce and online payments, there has been a growing need to streamline user decision-making and improve the user experience. However, conventional systems often provide information without considering user emotions, which can cause stress and lead to decreased purchasing intent and a decline in the quality of the purchasing experience. There are also challenges in providing personalized product suggestions tailored to purchasing trends and in speeding up payments.

[0555] 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.

[0556] In this invention, the server includes means for receiving data from an observation device and converting that data into a unified format, means for an emotion engine that recognizes and analyzes the user's emotions and adjusts the method of providing information based on the user's emotional state, and means for analyzing the user's purchase data and providing product recommendations based on purchasing trends. This makes it possible to provide information that matches the user's emotional state, reducing stress and enabling optimized content and payment procedures.

[0557] An "observation device" is a device used to collect and detect data, and it has the role of transmitting necessary information to a server.

[0558] A "unified format" is a standard for converting data in different formats into a common format, ensuring data consistency and compatibility.

[0559] An "analysis algorithm" is a procedure for analyzing received data and extracting useful information; it is a means of improving the efficiency and accuracy of data analysis.

[0560] "Visualization" is the process of displaying analyzed data in a way that is easy for humans to understand, representing information in graphs, charts, and other visual formats.

[0561] An "emotion engine" is a technology that recognizes and analyzes emotions from a user's voice or text, and is a system for understanding the user's emotional state in real time.

[0562] An "interface provisioning method" is a method for exchanging information between the user and the system to support decision-making and improve the user experience.

[0563] "Resource optimization" is the process of efficiently using available resources to maximize their effectiveness, thereby improving the operational efficiency of a system.

[0564] A "security protocol" is a set of rules and procedures designed to ensure the confidentiality and integrity of data and protect it from unauthorized access.

[0565] "Product recommendation" is a method of suggesting appropriate products based on a user's purchasing tendencies and past purchase history, and is a means of providing a personalized shopping experience.

[0566] "Fast purchase procedures" refer to processes that simplify the user's purchasing process, reducing effort and time, and improving user convenience.

[0567] The system that realizes this application example works in conjunction with various devices to provide optimal information and process payments based on the user's emotions and purchasing behavior. Specifically, a server plays a central role, converting data received from observation devices into a unified format. This converted data is then analyzed in detail and visualized by an analysis algorithm. The server is equipped with an emotion engine that recognizes and analyzes the user's emotions, and grasps the user's emotional state through voice and text input.

[0568] The server provides information to the user's terminal based on analysis results and the user's emotional state. This supports the user's decision-making and improves the purchasing experience. This information provision includes product recommendations that take purchasing trends into account, presenting suitable options for the user. Furthermore, if the user is experiencing stress, the interface and operating procedures are simplified and adjusted to enable faster purchase processing.

[0569] Data security is a top priority in the operation of this system, and the servers use security protocols to protect all data communications. This ensures the confidentiality of user sentiment data and purchase information.

[0570] As a concrete example, consider the case of a user purchasing food online. When the user is choosing tonight's dinner, if the emotion engine detects the user's impatience, a "Buy Now" button will appear on the screen, allowing the user to complete the purchase with minimal input. Furthermore, based on past purchase history, food recommendations reflecting the user's preferences will be provided.

[0571] An example of a prompt for a generative AI model would be: "Create an AI response that recognizes the user's emotional state from their voice input and suggests a simplified purchase process if they are feeling stressed." This serves as input for the AI ​​to generate interactions that respond to the user's emotions.

[0572] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0573] Step 1:

[0574] The server receives data from the observation instrument and converts it into a unified format. By receiving raw data sent from the observation instrument and converting it into a unified format, it enables subsequent analysis algorithms to operate efficiently. The input consists of various data streams from the observation instrument, and the output is data formatted into a unified format.

[0575] Step 2:

[0576] The server analyzes the data, which has been converted to a unified format, using an analysis algorithm. In this step, specific trends and patterns are automatically detected using numerical parameters of the data. The input is formatted data, and the output is the analysis result. Specifically, data mining is performed using machine learning algorithms.

[0577] Step 3:

[0578] The server uses the user's voice and text input to recognize the user's emotional state using an emotion engine. The input consists of voice and text data provided by the user, and emotion analysis is performed to identify the emotional state as output. Specifically, it estimates emotions by combining a natural language processing model and voice analysis software.

[0579] Step 4:

[0580] The server provides information optimized for the user's terminal based on the analyzed data and the user's emotional state. The input is the analysis results and the user's emotional state, while the output is the customized information presented to the user. In this process, the user interface is dynamically adjusted to clearly and visually represent the information.

[0581] Step 5:

[0582] The device analyzes the user's purchasing patterns and generates recommended products. It takes past purchase history and current purchase candidates as input and outputs personalized product recommendations. In practice, a collaborative filtering algorithm is used to display the most relevant products to the user.

[0583] Step 6:

[0584] The terminal displays an interface tailored to the user's emotional state and provides a simplified procedure. Inputs are the user's emotional state and current purchase status, while output is a simple interface to facilitate the user's smooth purchase completion. Here, the user interface elements are dynamically adjusted to display appropriate buttons and navigation.

[0585] Step 7:

[0586] The server protects all data through security protocols and ensures user privacy. Inputs are processed analytical data and sentiment data, and outputs are encrypted data. This operation includes data encryption using the SSL / TLS protocol, and all communications are secure.

[0587] 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.

[0588] 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.

[0589] 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.

[0590] [Fourth Embodiment]

[0591] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0592] 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.

[0593] 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).

[0594] 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.

[0595] 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.

[0596] 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).

[0597] 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.

[0598] 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.

[0599] 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.

[0600] 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.

[0601] 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.

[0602] 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.

[0603] 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".

[0604] In an embodiment of this invention, a dedicated software system operates on a server. This system receives data from observation devices, converts it to a unified format, and then processes the data by applying an analysis algorithm. The server displays the results of this processing on a management dashboard in real time and sends relevant information to the user.

[0605] The terminal provides visually analyzed information through a user-accessible interface. For example, it displays weather patterns and terrain changes based on data from space in interactive graphs. This information also supports decision-making, allowing users to develop operational plans and resource management strategies based on the data obtained.

[0606] The server further uses the acquired data to efficiently reallocate resources. This involves using optimization algorithms to propose settings that enhance the utilization efficiency of space equipment and ground infrastructure. Security protocols are employed to guarantee safety during data communication and prevent unauthorized access.

[0607] As a concrete example, consider a case where a user utilizes climate data from space for agricultural purposes. The user's device receives the latest climate analysis data from the server and displays time-series graphs showing fluctuations in precipitation and temperature for a specific region. This information is used to determine the optimal timing for planting and harvesting crops. This makes it possible to achieve more profitable agriculture and helps to significantly reduce resource waste.

[0608] The following describes the processing flow.

[0609] Step 1:

[0610] The server receives data transmitted from the observation equipment in real time. This involves establishing a stable connection with the observation equipment using advanced communication infrastructure and appropriately capturing diverse data formats.

[0611] Step 2:

[0612] The server converts the received data into a predetermined, unified format. This allows the analysis algorithm to process each data item consistently. This conversion process involves metadata extraction and reconstruction based on formatting rules.

[0613] Step 3:

[0614] The server applies an analysis algorithm to the transformed data. This algorithm identifies specific patterns in the image data and extracts significant information about climate and topography. The results are optimized through a feedback loop that further enhances the overall quality of the information obtained through the analysis.

[0615] Step 4:

[0616] The server stores the analysis results in a central database and reflects the information on the user dashboard. In this process, scalable database technology is employed for data management and visualization, ensuring fast query response times.

[0617] Step 5:

[0618] The terminal visualizes the analyzed information through a user-facing interface. Users can use this interface to view climate and topographic data in graphs and maps, and use this information to inform their decision-making. High-resolution joint maps and time-series graphs are used for visualization.

[0619] Step 6:

[0620] The server executes an automated resource allocation algorithm based on analysis data, with the aim of optimizing resources. This algorithm effectively adjusts the allocation of existing space equipment resources and makes suggestions to improve operational efficiency.

[0621] Step 7:

[0622] The server applies security protocols throughout the entire data communication process to protect data confidentiality and integrity. This ensures that only authorized users can access the information and prevents unauthorized manipulation of the data.

[0623] (Example 1)

[0624] 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".

[0625] In today's data collection environment, it is crucial to extract useful information from vast and diverse data formats and to manage resources efficiently and securely based on that information. However, traditional systems have faced challenges such as the time and cost involved in integrating different data formats, performing real-time analysis, providing visual information, and optimizing resources. Furthermore, there has been a lack of data security and an easy-to-use interface for users to utilize generated AI models.

[0626] 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.

[0627] In this invention, the server includes means for receiving data from an observation device and converting the data into a unified format, means for analyzing the data using an analysis algorithm, and means for deploying the analyzed data to a management dashboard in real time. This enables the integration of data formats, streamlining of the analysis process, real-time provision of visual information, secure data communication, and optimal utilization of resources.

[0628] An "observation device" refers to an instrument that collects physical or digital data from the external environment.

[0629] A "unified format" refers to a common data representation format used to convert data in different formats into a consistent format.

[0630] An "analytical algorithm" refers to a mathematical or computational method used to analyze data and extract useful information from it.

[0631] A "management dashboard" refers to a display system that visualizes analyzed data and has an interface for intuitive understanding.

[0632] A "visual interface" refers to an interactive display method for providing information to users graphically.

[0633] An "optimization algorithm" refers to a computational technique used to achieve the efficient allocation of resources.

[0634] A "security protocol" refers to encryption technologies and procedures used to maintain the security of data communications.

[0635] A "generative AI model" refers to an artificial intelligence model that generates information based on specified input conditions.

[0636] A "prompt" refers to a text-based instruction or question that is input into a generative AI model.

[0637] This invention is implemented by running a specific software system on a server. This system receives data in various formats from observation equipment and converts it into a unified format. The server processes the data using data transformation tools, such as ETL tools. The converted data is then analyzed in detail via analytical algorithms. Machine learning libraries such as Python's Scikit-learn and TensorFlow are used for the analysis, including climate pattern analysis and topographic change prediction.

[0638] The analysis results are displayed in real time on a management dashboard. This dashboard is built using visualization software such as Tableau or Grafana as tools for data visualization. Users can interactively view these results through a visual interface provided via their device. For example, it is possible to display interactive graphs of climate data using libraries such as D3.js or Chart.js.

[0639] Users can make decisions based on the analysis data sent from the server, and further obtain detailed analysis results by inputting prompts to the generating AI model. These prompts function as instructions or questions for the generating AI, and are entered in the form of "Please suggest the optimal irrigation schedule for next month."

[0640] Furthermore, the server utilizes optimization algorithms to propose efficient resource allocation. Operations research techniques are used in this process. The server also implements security protocols, such as SSL / TLS, to ensure secure data communication and protect against unauthorized access.

[0641] For example, when users utilize climate data from space for agricultural purposes, this system allows them to view time-series graphs showing fluctuations in precipitation and temperature. Based on this information, it becomes possible to determine the optimal planting time for crops, thereby reducing resource waste.

[0642] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0643] Step 1:

[0644] The server receives data from observation instruments. These instruments transmit, for example, climate data or image data acquired from sensors placed in space. The input is raw sensor data, and the output is data prepared for conversion to a unified format. This process includes data format verification and metadata assignment.

[0645] Step 2:

[0646] The server converts the input data into a unified format. Specifically, it uses ETL (Extract, Transform, Load) tools to reshape data in different formats into a standardized format. The input is raw data, and the output is standardized data. This process prepares the data for subsequent analysis to proceed smoothly.

[0647] Step 3:

[0648] The server analyzes data by applying analytical algorithms to data in a unified format. This includes applying machine learning models using Python's Scikit-learn and TensorFlow. The input is standardized data, and the output is predicted data or classified information resulting from the analysis. Specific operations include predicting temperature changes and detecting anomalies from image data.

[0649] Step 4:

[0650] The server deploys the analysis results to a management dashboard in real time. Tools such as Tableau and Grafana are used for data visualization. The input is analyzed data, and the output is a visual report designed for intuitive user understanding. This phase specifically includes the generation of visual graphs and charts.

[0651] Step 5:

[0652] The terminal provides the user with a visual interface and displays the analysis results. The interface runs on a web browser and uses libraries such as D3.js and Chart.js to interactively visualize the data. The input is visual data sent from the server, and the output is a user-interactive interface.

[0653] Step 6:

[0654] The user makes a decision based on the displayed information and inputs prompts into the generating AI model. As a specific instruction for the analysis the user requests, they send prompts to the generating AI such as, "Please suggest the optimal irrigation schedule for next month." The input is a prompt to the generating AI, and the output is a report containing detailed analysis results and suggestions.

[0655] Step 7:

[0656] The server provides a plan for reallocated resources and proposes an optimal solution using operational research techniques. Inputs are analytical data and current resource allocation information, and output is an optimized resource allocation plan. Ultimately, this leads to improved resource efficiency and reduced operating costs.

[0657] (Application Example 1)

[0658] 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".

[0659] In modern public facilities, security measures are becoming increasingly complex and diverse, making it difficult to effectively analyze information in real time and to quickly detect and respond to anomalies. Furthermore, if resources are not properly allocated and readjusted, the effectiveness of security may decrease, requiring efficient operation. It is necessary to address these challenges and achieve safer and more efficient security management.

[0660] 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.

[0661] In this invention, the server includes means for receiving information from an observation support device and converting that information into a unified format, means for analyzing the information using an analysis method, and means for managing and visualizing the analyzed information and detecting anomalies. This enables real-time anomaly detection and efficient optimization of resource allocation in public facilities.

[0662] An "observation support device" is a device that uses sensors and equipment to collect specific information and provides it to a processing system.

[0663] "Means of converting information into a unified format" refers to a function that performs the process of converting information collected in various formats into a consistent format suitable for analysis and visualization.

[0664] "Means of analysis using analytical methods" refers to means of performing analysis using specific algorithms and techniques based on collected information, in order to extract meaningful data.

[0665] "Means for management, visualization, and anomaly detection" refers to functions that organize and visually present analyzed information to quickly detect unusual behavior or patterns.

[0666] "Real-time anomaly detection" is a process that analyzes collected data sequentially to immediately detect anomalies and enable appropriate responses.

[0667] "Resource allocation optimization" means efficiently planning and coordinating the allocation of available resources to achieve maximum safety and effectiveness with minimum resources.

[0668] To realize this application, the server receives information from the observation support device and converts it into a unified format. The server then applies analytical methods to analyze this information and manages and visualizes the data in real time. The analyzed information is displayed on a management dashboard, and when an anomaly is detected, it is visualized as an alarm.

[0669] The server uses Python and retrieves data from the internet using the requests library. This data is received in JSON format and visualized using the matplotlib library. This allows users to intuitively understand security anomalies and abnormal behavioral patterns based on environmental data collected by sensors installed in a public facility.

[0670] Users utilize an operation screen that provides analysis results and make appropriate decisions based on the obtained data. This operation screen is accessible via a browser and also includes a notification function that enables rapid response when an anomaly is detected.

[0671] For example, if unusual temperature changes or movements are detected during the night, an alert will be displayed on the user's PC or smart device. This approach can enhance security within public facilities and support a rapid response.

[0672] An example of a prompt statement is "Describe a method for detecting sensor anomalies in real time," which is used as an instruction to the generative AI model. This prompt allows the generative AI model to suggest similar analysis methods and code, supporting further system improvements.

[0673] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0674] Step 1:

[0675] The server receives data from the observation support equipment. Raw data from the equipment is provided as input. The server receives this data and converts it into a unified format such as JSON. This process ensures that diverse data obtained from different equipment is formatted for analysis.

[0676] Step 2:

[0677] The server performs analysis on the transformed data using analytical methods. The input is data in a unified format, and the server applies analytical algorithms to detect anomalies and patterns in the data. The output is the anomalies and associated characteristics that may have been detected by the analysis. Statistical methods and machine learning algorithms may be used in this process to distinguish between normal and abnormal data.

[0678] Step 3:

[0679] The analysis results are managed and visualized on the terminal. The terminal receives this analyzed data and presents it to the user through an interactive dashboard. The input is the analyzed data, and the output is graphs and alarm displays that the user can intuitively understand. Based on this visual information, the user can identify anomalies and take immediate action.

[0680] Step 4:

[0681] Users make decisions using information provided by their devices. Inputs are anomaly notifications and graph information displayed on the device, while outputs are specific actions taken by the user. For example, if an anomaly is detected in a specific area, the user can take actions such as conducting an on-site investigation or activating a notification system. This process also leads to the exploration of further improvements and analysis methods by inputting prompts into the generated AI model.

[0682] 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.

[0683] An embodiment of the present invention is centered around a server that receives data from an observation device and converts it into a unified format. The server processes the received data using an analysis algorithm and manages and visualizes the resulting analysis. Furthermore, this system integrates an emotion engine that recognizes and analyzes the user's emotions, and identifies the emotional state by utilizing the user's voice and text input.

[0684] The device features an interface to provide users with information and support their decision-making. It effectively visualizes analysis results while taking into account the user's emotional state. For example, if the emotion engine detects the user's anxiety, the device adjusts how information is presented, adopting a more user-friendly graphical representation.

[0685] The server has the means to readjust resource allocation based on the analyzed data and optimize the overall operational efficiency of the system. In this process, security protocols are applied to ensure the confidentiality and safety of the data. All communications, including user sentiment data, are encrypted and protected from unauthorized access.

[0686] As a concrete example, consider the case of a user who uses climate change information. The user receives real-time weather data through their device and makes strategic decisions based on the insights gained from it. In this process, the emotion engine detects the user's anxiety and provides support to deepen the user's understanding by changing the presentation style and simplifying the explanation. Through these adjustments, the user can make decisions confidently without feeling stressed.

[0687] The following describes the processing flow.

[0688] Step 1:

[0689] The server receives data transmitted from observation instruments placed in space. This process involves establishing a communication channel and verifying that the data is received in a consistent manner.

[0690] Step 2:

[0691] The server converts the received data into a unified format. This includes cross-checking the data and resolving inconsistencies, preparing it for efficient operation of the analysis algorithms.

[0692] Step 3:

[0693] The server activates an analysis algorithm to analyze data in a unified format. If image data is included, this algorithm identifies specific patterns or changes and extracts meaningful information.

[0694] Step 4:

[0695] The terminal reflects the analyzed data on a management dashboard, visualizing it in a user-accessible format. Here, information is presented using easy-to-understand graphs and maps, allowing users to easily grasp the data.

[0696] Step 5:

[0697] The emotion engine analyzes the user's emotional state based on voice or text input. This allows it to understand the user's psychological state and adjust the interface's responsiveness and appearance as needed.

[0698] Step 6:

[0699] Users make decisions based on information provided through their devices. For example, when making strategic decisions based on climate data, if the emotion engine determines that the user is in a state of anxiety, it simplifies the presentation of information and provides support to encourage calm decision-making.

[0700] Step 7:

[0701] The server optimizes the allocation of available resources based on the analyzed data. This includes efficient resource reallocation and suggests settings that improve the operational efficiency of the associated infrastructure.

[0702] Step 8:

[0703] The server applies security protocols to all data processing and communication to protect the confidentiality and integrity of information. It prevents unauthorized access and securely manages data related to user sentiment.

[0704] (Example 2)

[0705] 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".

[0706] Conventional data processing systems have struggled to manage data in different formats in a unified manner and to provide decision-making support while understanding the user's psychological state. Furthermore, insufficient resource optimization and security enhancements have hindered efficient data utilization. To address these issues, a system is needed that comprehensively handles everything from data reception and analysis to user interface provision and efficient resource utilization.

[0707] 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.

[0708] In this invention, the server includes means for receiving data from an observation device and converting the data into a unified format, means for analyzing the data using an analysis algorithm and storing the results in a recording device, and means for recognizing the user's emotional state and providing an interface to support decision-making by providing information tailored to that state. This enables the efficient integration of data in different formats, the provision of information that takes the user's emotions into consideration, and achieves resource optimization and improved security.

[0709] An "observation device" is a device used to collect data in an external environment or under specific circumstances, and is a device that has the function of acquiring and recording data.

[0710] A "unified format" is a data format that facilitates data processing and analysis by converting data obtained in different formats and structures into a consistent format.

[0711] An "analysis algorithm" is a set of computational procedures designed to evaluate data or perform pattern recognition, and is a method used to obtain specific results or insights.

[0712] A "recording device" is a device for storing data on a storage medium, and is a device that enables the retention of information and its access at a later date.

[0713] A "visualization device" is a device or software used to visually represent data, generating charts and graphs to facilitate understanding of the data.

[0714] "Emotional state" refers to a user's psychological or emotional condition, and is a subjective state analyzed using an emotion engine.

[0715] "Interface provision means" refers to means that enable the exchange of information between the user and the system, and includes user interfaces that function as a window for operation and information provision.

[0716] "Resource optimization" refers to techniques for efficiently allocating available resources to maximize performance.

[0717] "Encryption technology" is a technology used to protect data from unauthorized external access by encrypting it, thereby ensuring the confidentiality and security of the data.

[0718] A "generative AI model" is a model trained to generate new insights or outputs from data using artificial intelligence technology, and it processes prompts and other text as input.

[0719] A "prompt statement" is an input statement given to a generative AI model, and it is text that provides instructions or information for the model to process.

[0720] This invention relates to a system that receives data from observation devices, converts it into a unified format, analyzes it, and manages it. The server first receives data from the observation devices. The received data is converted into a data frame using the Python library Pandas and managed in a consistent format.

[0721] Next, the server analyzes the data trends using an analysis algorithm based on NumPy. The analysis results are stored in a database and managed visually using visualization tools. An SQL database is used for storing and retrieving results, while tools such as Chart.js are used for visualization.

[0722] Furthermore, the server utilizes an emotion engine to analyze user input. This engine employs natural language processing (NLP) technology, with the Google Cloud Natural Language API analyzing the user's voice and text input to identify their emotional state. This enables the provision of information that takes the user's psychological state into consideration.

[0723] The device provides analysis results to the user through a user-friendly interface. For example, if the emotion engine detects anxiety, the device presents the information in a more user-friendly design. This allows the user to receive the information with confidence and make informed decisions.

[0724] Furthermore, the servers use AWS Lambda to automatically optimize and efficiently reallocate available resources. The servers apply the SSL / TLS protocol to all communications and maintain data confidentiality using AES encryption technology.

[0725] For example, if a user wants to create a presentation based on next week's weather forecast using climate change information, they can input a prompt message into the AI ​​model saying, "I want to prepare the data for the next meeting." This prompt message allows the system to automatically perform appropriate data processing and support the user's decision-making.

[0726] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0727] Step 1:

[0728] The server receives data from the observation device. The observation device collects and transmits various environmental data. The server receives this data and first checks its format. The input is raw data sent from the observation device, which is converted into a unified format suitable for processing within the server.

[0729] Step 2:

[0730] The server processes the data, which has been converted to a unified format, using an analysis algorithm. This analysis uses the numerical computation library NumPy to evaluate trends across the entire dataset. It also runs an anomaly detection algorithm to identify anomalies within the data. The input is data in a unified format, and the output is the analyzed information and an anomaly report.

[0731] Step 3:

[0732] The server stores the analysis results in a recording device and manages and visualizes them using a visualization device. Chart.js is used for visualization, graphing the data in a user-friendly format. The input is the analyzed data, and the output is a graphical report that the user can view.

[0733] Step 4:

[0734] The server uses an emotion engine to analyze the user's voice and text input. Natural language processing techniques are used to identify the user's emotional state from their speech and text. Google Cloud Natural Language API is utilized here. Input is user voice and text data, and output is the identified emotional state.

[0735] Step 5:

[0736] The terminal provides the user with a visualized version of the analysis results through an interface. The display method is adjusted to reflect the user's emotional state. For example, if anxiety is detected, a user-friendly design is adopted to reduce complexity. The input consists of the analysis results and emotional data, while the output is the adjusted interface.

[0737] Step 6:

[0738] The server optimizes resources and automatically reallocates them. It utilizes cloud features such as AWS Lambda to dynamically allocate computing resources according to the load. This process improves the overall system efficiency. The input is the system load, and the output is the optimized resource allocation.

[0739] Step 7:

[0740] The server protects all communications using encryption technology. By applying the SSL / TLS protocol and encrypting all data transmission and reception, data confidentiality is ensured. Input is the data being transmitted, and output is the encrypted transmitted data.

[0741] Step 8:

[0742] The user inputs prompts into a generative AI model, and the system performs appropriate data processing based on those instructions. The model receives user requests as input, and results are obtained based on those requests. Through this process, user support and decision-making are effectively facilitated. The input is prompts, and the output is the information and insights the user seeks.

[0743] (Application Example 2)

[0744] 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".

[0745] In recent years, with the spread of e-commerce and online payments, there has been a growing need to streamline user decision-making and improve the user experience. However, conventional systems often provide information without considering user emotions, which can cause stress and lead to decreased purchasing intent and a decline in the quality of the purchasing experience. There are also challenges in providing personalized product suggestions tailored to purchasing trends and in speeding up payments.

[0746] 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.

[0747] In this invention, the server includes means for receiving data from an observation device and converting that data into a unified format, means for an emotion engine that recognizes and analyzes the user's emotions and adjusts the method of providing information based on the user's emotional state, and means for analyzing the user's purchase data and providing product recommendations based on purchasing trends. This makes it possible to provide information that matches the user's emotional state, reducing stress and enabling optimized content and payment procedures.

[0748] An "observation device" is a device used to collect and detect data, and it has the role of transmitting necessary information to a server.

[0749] A "unified format" is a standard for converting data in different formats into a common format, ensuring data consistency and compatibility.

[0750] An "analysis algorithm" is a procedure for analyzing received data and extracting useful information; it is a means of improving the efficiency and accuracy of data analysis.

[0751] "Visualization" is the process of displaying analyzed data in a way that is easy for humans to understand, representing information in graphs, charts, and other visual formats.

[0752] An "emotion engine" is a technology that recognizes and analyzes emotions from a user's voice or text, and is a system for understanding the user's emotional state in real time.

[0753] An "interface provisioning method" is a method for exchanging information between the user and the system to support decision-making and improve the user experience.

[0754] "Resource optimization" is the process of efficiently using available resources to maximize their effectiveness, thereby improving the operational efficiency of a system.

[0755] A "security protocol" is a set of rules and procedures designed to ensure the confidentiality and integrity of data and protect it from unauthorized access.

[0756] "Product recommendation" is a method of suggesting appropriate products based on a user's purchasing tendencies and past purchase history, and is a means of providing a personalized shopping experience.

[0757] "Fast purchase procedures" refer to processes that simplify the user's purchasing process, reducing effort and time, and improving user convenience.

[0758] The system that realizes this application example works in conjunction with various devices to provide optimal information and process payments based on the user's emotions and purchasing behavior. Specifically, a server plays a central role, converting data received from observation devices into a unified format. This converted data is then analyzed in detail and visualized by an analysis algorithm. The server is equipped with an emotion engine that recognizes and analyzes the user's emotions, and grasps the user's emotional state through voice and text input.

[0759] The server provides information to the user's terminal based on analysis results and the user's emotional state. This supports the user's decision-making and improves the purchasing experience. This information provision includes product recommendations that take purchasing trends into account, presenting suitable options for the user. Furthermore, if the user is experiencing stress, the interface and operating procedures are simplified and adjusted to enable faster purchase processing.

[0760] Data security is a top priority in the operation of this system, and the servers use security protocols to protect all data communications. This ensures the confidentiality of user sentiment data and purchase information.

[0761] As a concrete example, consider the case of a user purchasing food online. When the user is choosing tonight's dinner, if the emotion engine detects the user's impatience, a "Buy Now" button will appear on the screen, allowing the user to complete the purchase with minimal input. Furthermore, based on past purchase history, food recommendations reflecting the user's preferences will be provided.

[0762] An example of a prompt for a generative AI model would be: "Create an AI response that recognizes the user's emotional state from their voice input and suggests a simplified purchase process if they are feeling stressed." This serves as input for the AI ​​to generate interactions that respond to the user's emotions.

[0763] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0764] Step 1:

[0765] The server receives data from the observation instrument and converts it into a unified format. By receiving raw data sent from the observation instrument and converting it into a unified format, it enables subsequent analysis algorithms to operate efficiently. The input consists of various data streams from the observation instrument, and the output is data formatted into a unified format.

[0766] Step 2:

[0767] The server analyzes the data, which has been converted to a unified format, using an analysis algorithm. In this step, specific trends and patterns are automatically detected using numerical parameters of the data. The input is formatted data, and the output is the analysis result. Specifically, data mining is performed using machine learning algorithms.

[0768] Step 3:

[0769] The server uses the user's voice and text input to recognize the user's emotional state using an emotion engine. The input consists of voice and text data provided by the user, and emotion analysis is performed to identify the emotional state as output. Specifically, it estimates emotions by combining a natural language processing model and voice analysis software.

[0770] Step 4:

[0771] The server provides information optimized for the user's terminal based on the analyzed data and the user's emotional state. The input is the analysis results and the user's emotional state, while the output is the customized information presented to the user. In this process, the user interface is dynamically adjusted to clearly and visually represent the information.

[0772] Step 5:

[0773] The device analyzes the user's purchasing patterns and generates recommended products. It takes past purchase history and current purchase candidates as input and outputs personalized product recommendations. In practice, a collaborative filtering algorithm is used to display the most relevant products to the user.

[0774] Step 6:

[0775] The terminal displays an interface tailored to the user's emotional state and provides a simplified procedure. Inputs are the user's emotional state and current purchase status, while output is a simple interface to facilitate the user's smooth purchase completion. Here, the user interface elements are dynamically adjusted to display appropriate buttons and navigation.

[0776] Step 7:

[0777] The server protects all data through security protocols and ensures user privacy. Inputs are processed analytical data and sentiment data, and outputs are encrypted data. This operation includes data encryption using the SSL / TLS protocol, and all communications are secure.

[0778] 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.

[0779] 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.

[0780] 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.

[0781] 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.

[0782] 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.

[0783] 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.

[0784] 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.

[0785] 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.

[0786] 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."

[0787] 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.

[0788] 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.

[0789] 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.

[0790] 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.

[0791] 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.

[0792] 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.

[0793] 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.

[0794] 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.

[0795] 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.

[0796] 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.

[0797] 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.

[0798] 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.

[0799] The following is further disclosed regarding the embodiments described above.

[0800] (Claim 1)

[0801] A means for receiving data from an observation device and converting that data into a unified format,

[0802] A means for analyzing the data using an analysis algorithm,

[0803] Means for managing and visualizing the analyzed data,

[0804] A means of providing an interface to provide information to users and support decision-making,

[0805] A means to optimize available resources and automatically readjust their placement,

[0806] A system that includes means of protecting data using security protocols.

[0807] (Claim 2)

[0808] The system according to claim 1, characterized in that the observation device is an instrument located in space.

[0809] (Claim 3)

[0810] The system according to claim 1, characterized in that the analysis algorithm performs image data analysis.

[0811] "Example 1"

[0812] (Claim 1)

[0813] A means for receiving data from an observation device and converting that data into a unified format,

[0814] A means for analyzing the data using an analysis algorithm,

[0815] A means for deploying the analyzed data to a management dashboard in real time,

[0816] A means of providing users with information and a visual interface to support their decision-making,

[0817] A means of applying an optimization algorithm to optimize and automatically reallocate available resources,

[0818] By using security protocols to ensure secure data communication, and by employing means to protect data,

[0819] A means of inputting prompt sentences into a generator AI model based on data provided by the user and receiving the results,

[0820] A system that includes this.

[0821] (Claim 2)

[0822] The system according to claim 1, characterized in that the observation device is an instrument located in space.

[0823] (Claim 3)

[0824] The system according to claim 1, characterized in that the analysis algorithm performs image data analysis.

[0825] "Application Example 1"

[0826] (Claim 1)

[0827] A means for receiving information from an observation support device and converting that information into a unified format,

[0828] Means for analyzing the information using analytical methods,

[0829] The analyzed information is managed and visualized, and means for detecting anomalies,

[0830] A means of providing information to humans and providing an operating screen to support decision-making,

[0831] A means to optimize available resources and automatically readjust their placement,

[0832] A system that includes means of protecting information using security maintenance methods.

[0833] (Claim 2)

[0834] The system according to claim 1, characterized in that the observation support device is equipment installed in a public facility and detects abnormalities.

[0835] (Claim 3)

[0836] The system according to claim 1, characterized in that the analysis method performs security data analysis.

[0837] "Example 2 of combining an emotion engine"

[0838] (Claim 1)

[0839] A means for receiving data from an observation device and converting that data into a unified format,

[0840] A means for analyzing the data using an analysis algorithm and storing the results in a recording device,

[0841] Means for managing and visualizing the analyzed data using a visualization device,

[0842] An interface providing means that recognizes the user's emotional state and provides information corresponding to that psychological state to support decision-making,

[0843] A means to optimize available resources and automatically readjust their placement,

[0844] Means of using encryption technology to protect communication data and emotional data,

[0845] A system including means for creating prompt statements for a generated AI model based on the analysis results.

[0846] (Claim 2)

[0847] The system according to claim 1, characterized in that the observation device is an instrument located in space.

[0848] (Claim 3)

[0849] The system according to claim 1, characterized in that the analysis algorithm performs visual information data analysis.

[0850] "Application example 2 when combining with an emotional engine"

[0851] (Claim 1)

[0852] A means for receiving data from an observation device and converting that data into a unified format,

[0853] A means for analyzing the data using an analysis algorithm,

[0854] Means for managing and visualizing the analyzed data,

[0855] A system equipped with an emotion engine that recognizes and analyzes user emotions, and means for adjusting the method of providing information based on the user's emotional state,

[0856] A means to optimize available resources and automatically readjust their placement,

[0857] A means of protecting data using security protocols,

[0858] A means of analyzing user purchase data and providing product recommendations based on purchasing trends,

[0859] A system that includes means to adjust the interface based on user emotions and enable a quick purchase process.

[0860] (Claim 2)

[0861] The system according to claim 1, characterized in that the observation device is an instrument located in space.

[0862] (Claim 3)

[0863] The system according to claim 1, characterized in that the analysis algorithm performs image data analysis. [Explanation of Symbols]

[0864] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for receiving data from an observation device and converting that data into a unified format, A means for analyzing the data using an analysis algorithm, Means for managing and visualizing the analyzed data, A means of providing an interface to provide information to users and support decision-making, A means to optimize available resources and automatically readjust their placement, A system that includes means of protecting data using security protocols.

2. The system according to claim 1, characterized in that the observation device is an instrument located in space.

3. The system according to claim 1, characterized in that the analysis algorithm performs image data analysis.

Images