system

Abstract

We provide the system. [Solution] A means for the user to enter a search query, A means of sending the input query to the server, A means of searching a database based on a query, A method for processing search results with a generating AI model and adding supplementary information, A means of returning the processed results to the user, A means of displaying the returned results on the user interface, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance 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] Landscape regulations vary from municipality to municipality, and construction companies must each consider countermeasures, so there is a problem that the quality varies in compliance with laws and regulations. In addition, there are problems with insufficient legal knowledge and insufficient sharing of updated information, and there is a lack of specific case studies, making it difficult to solve problems smoothly. As a result, the quality of responses varies among construction companies, and it is difficult to efficiently comply with landscape laws and regulations. It is necessary to solve these problems and provide a knowledge base that enables construction companies to quickly and efficiently respond to landscape regulations.

Means for Solving the Problems

[0005] The present invention provides a system that includes means for a user to input a search query, means for sending the input query to a server, means for searching a database based on the query, means for processing the search results with a generating AI model and adding supplementary information, means for returning the processed results to the user, and means for displaying the returned results on a user interface. Furthermore, by adding means for selecting and presenting the most suitable countermeasures from the search results, and means for providing product information necessary for landscape countermeasures and managing the purchase procedure, the invention provides construction companies with a knowledge base for quickly and efficiently responding to the landscape ordinances of each local government. With this system, construction companies will be able to instantly search and view compliance points and optimal countermeasures, thereby enabling them to efficiently comply with landscape regulations.

[0006] "User" refers to a construction company representative who uses this system to search for and view landscape regulations and countermeasures.

[0007] A "search query" refers to the keywords or phrases that a user enters to search for specific information.

[0008] A "terminal" refers to a client device accessed by a user, and includes web browsers and mobile applications.

[0009] A "server" refers to a computer system that handles the backend of a system, managing databases, operating AI models, and processing requests.

[0010] A "database" refers to a data storage system for efficiently accumulating and searching for landscape regulations and countermeasures from various local governments.

[0011] A "generative AI model" refers to an artificial intelligence model that processes search results obtained from a database and proposes supplementary information and optimal solutions.

[0012] "User interface" refers to the screens and operating methods that users use to access a system and search for and view information.

[0013] "Optimal countermeasure examples" refer to the solutions and implementation examples that are most suitable for a particular landscape regulation, and are selected by an AI model.

[0014] "Product information" refers to detailed data on products necessary for landscape improvement measures, including specifications, price, and stock availability.

[0015] "Purchase procedure" refers to the series of operations and payment processes involved in a user purchasing a selected product online. [Brief explanation of the drawing]

[0016] [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] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0020] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] 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."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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".

[0037] This invention provides a system that offers a knowledge base for quickly and efficiently responding to the landscape regulations of each local government. This system operates through the coordinated efforts of user, terminal, and server components.

[0038] System Overview

[0039] This system consists of the following elements:

[0040] 1. User: A representative from a construction company who uses the system to search and view landscape regulations and examples of countermeasures.

[0041] 2. Terminal: A client device used by the user, including web browsers and mobile applications.

[0042] 3. Server: A computer system that handles the backend of the system, managing databases, operating AI models, and processing requests.

[0043] System operation

[0044] User actions

[0045] Users access the system using their devices to search for specific landscape regulations and best practices. The information retrieval process begins when the user enters a search query into the search box on their device and presses the submit button.

[0046] Terminal role

[0047] The terminal provides a user interface, including a form for entering search queries and a screen for receiving display results. When a user enters and submits a search query, that query is sent to the server as an HTTP request.

[0048] Server Processing

[0049] The server analyzes the search query received from the terminal and searches the database based on it. The database contains landscape regulations from each local government and examples of countermeasures from across the country. The search results are processed by a generative AI model, which suggests supplementary information and optimal countermeasures. The server returns these results to the terminal in JSON format.

[0050] Specific operation examples

[0051] Example 1: Searching for information on landscape regulations

[0052] User: Enter "Osaka City's latest landscape ordinance" into the search box on your device.

[0053] Terminal: Receives search queries and sends them to the server as HTTP requests.

[0054] Server: Analyzes search queries, searches the database to retrieve Osaka City's landscape ordinance information, and adds supplementary information using a generative AI model. The results are returned to the terminal in JSON format.

[0055] Terminal: Displays received data on the user interface, allowing users to check information regarding Osaka City's landscape regulations.

[0056] Example 2: Searching for countermeasure examples

[0057] User: Enter "Examples of landscape measures for commercial buildings in Shibuya Ward" into the search box on your device.

[0058] Terminal: Receives search queries and sends them to the server as HTTP requests.

[0059] Server: Analyzes search queries, searches the database to retrieve case studies for Shibuya Ward, and selects the most suitable case using a generative AI model. The results are returned to the terminal in JSON format.

[0060] Terminal: Displays received data on the user interface, allowing users to view examples of optimal commercial building landscape measures in Shibuya Ward.

[0061] Other features

[0062] This system also offers the following features:

[0063] Product Information Provision: We provide detailed information (specifications, price, stock availability, etc.) on products necessary for landscape improvement measures.

[0064] Purchase Procedure: Manages the process for users to purchase selected products online.

[0065] This allows users to efficiently comply with landscape regulations and easily purchase necessary products. Implementing this invention promotes legal compliance among construction companies and facilitates mutual information sharing.

[0066] The following describes the processing flow.

[0067] Specific processing steps of the program

[0068] Example 1: Searching for information on landscape regulations

[0069] Step 1:

[0070] User: Enter "Osaka City's latest landscape ordinance" into the search box on your device and click the search button.

[0071] Step 2:

[0072] Terminal: Receives the search query entered by the user and sends it to the server as an HTTP request.

[0073] Step 3:

[0074] Server: Receives HTTP requests from terminals, parses the request content, and extracts the search query.

[0075] Step 4:

[0076] Server: Searches the "Landscape Regulations" table in the database based on the search query. Uses indexes for efficient searching.

[0077] Step 5:

[0078] Server: Passes search results retrieved from the database to the AI ​​model, which then generates necessary supplementary information and annotations.

[0079] Step 6:

[0080] Server: Combines the generated results with the original search results to produce the final response in JSON format.

[0081] Step 7:

[0082] Server: Sends the generated JSON response back to the terminal.

[0083] Step 8:

[0084] Terminal: Receives the JSON response sent back from the server and parses the data.

[0085] Step 9:

[0086] Terminal: Displays the parsed data on the user interface, allowing the user to see the information they need.

[0087] Example 2: Searching for countermeasure examples

[0088] Step 1:

[0089] User: Enter "Examples of landscape measures for commercial buildings in Shibuya Ward" into the search box on your device and click the search button.

[0090] Step 2:

[0091] Terminal: Receives the search query entered by the user and sends it to the server as an HTTP request.

[0092] Step 3:

[0093] Server: Receives HTTP requests from terminals, parses the request content, and extracts the search query.

[0094] Step 4:

[0095] Server: Based on the search query, it searches the "Case Studies" table in the database. Indexes are used to improve efficiency.

[0096] Step 5:

[0097] Server: The server passes search results obtained from the database to an AI model that scores and selects the most suitable countermeasures.

[0098] Step 6:

[0099] Server: Combines scored optimal countermeasures and supplementary information to generate the final response in JSON format.

[0100] Step 7:

[0101] Server: Sends the generated JSON response back to the terminal.

[0102] Step 8:

[0103] Terminal: Receives the JSON response sent back from the server and parses the data.

[0104] Step 9:

[0105] Terminal: Displays the parsed data on the user interface, allowing users to review case studies.

[0106] Example 3: Providing product information and purchasing procedures

[0107] Step 1:

[0108] User: Enter "fence for landscape improvement" into the product search box on the terminal and click the search button.

[0109] Step 2:

[0110] Terminal: Receives the search query entered by the user and sends it to the server as an HTTP request.

[0111] Step 3:

[0112] Server: Receives HTTP requests from terminals, parses the request content, and extracts the search query.

[0113] Step 4:

[0114] Server: Searches the product database based on the search query. Performs filtering based on product category and characteristics.

[0115] Step 5:

[0116] Server: Organizes product information retrieved from the database and prepares detailed information such as specifications, price, and stock status.

[0117] Step 6:

[0118] Server: Generates organized product information in JSON format and sends it back to the terminal.

[0119] Step 7:

[0120] Terminal: Receives the JSON response sent back from the server and parses the data.

[0121] Step 8:

[0122] Terminal: Displays parsed data on the user interface, allowing users to view detailed product information.

[0123] Step 9:

[0124] User: Review the displayed product information, add the desired product to your cart, and begin the purchase process.

[0125] Step 10:

[0126] Terminal: Collects information related to the purchase process (user information, payment information, etc.) and sends it to the server as an HTTP request.

[0127] Step 11:

[0128] Server: Receives information regarding the purchase procedure and processes the payment. Confirms the success of the payment.

[0129] Step 12:

[0130] Server: If the purchase is successful, send a confirmation email to the user and a purchase completion message back to the device.

[0131] Step 13:

[0132] Terminal: Displays the purchase completion message returned from the server on the user interface, notifying the user that the purchase was successful.

[0133] (Example 1)

[0134] 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."

[0135] In recent years, there has been a growing need to respond quickly and efficiently to the landscape regulations of various local governments. However, for many construction company personnel, collecting information on these regulations and taking appropriate measures is a very time-consuming and laborious task. Furthermore, obtaining appropriate product information and smoothly carrying out the purchase process is also difficult. The present invention aims to solve these problems and provide a system that can respond to landscape regulations efficiently and quickly.

[0136] 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.

[0137] In this invention, the server includes means for selecting and presenting the most suitable countermeasures from the search results, means for providing product information necessary for landscape countermeasures and managing the purchase procedure, and means for returning the processed results to the user. This allows the user to efficiently comply with landscape regulations and easily purchase the necessary products.

[0138] A "user" is a representative from a construction company whose role is to use the system to search for and view landscape regulations and examples of countermeasures.

[0139] A "terminal" refers to a client device used by a user, including web browsers and mobile applications.

[0140] A "server" is a computer system that handles the backend of a system, managing databases, operating generated AI models, and processing requests.

[0141] A "search query" is a string of characters or a phrase that a user enters into their device to seek specific information.

[0142] An "HTTP request" is a type of communication sent from a client device (terminal) to a server requesting a specific operation or data.

[0143] A "database" is a collection of information that stores landscape regulations and examples of countermeasures from various local governments.

[0144] A "generative AI model" is an artificial intelligence model that operates on a server and generates supplementary information and optimal countermeasures based on search results.

[0145] JSON format is a standard format for representing data in text format, and it is a data exchange format that has the structure of objects and arrays.

[0146] A "user interface" refers to the screen or interface that a user uses to operate a device, input information, or view displayed results.

[0147] A "landscape ordinance" is a set of laws and regulations enacted by a local government for the purpose of protecting or improving the landscape of the area.

[0148] An "example of countermeasure" is an example of a specific countermeasure that was taken in the past to address a particular situation or problem.

[0149] "Product information" refers to detailed information about products necessary for landscape improvement measures, including specifications, price, and stock availability.

[0150] The "purchase process" refers to the series of tasks and management processes involved in a user purchasing a selected product online.

[0151] This invention provides a system that offers a knowledge base for quickly and efficiently responding to the landscape regulations of each local government. This system operates through the coordinated action of multiple components, including user terminals, servers, and generative AI models.

[0152] 1. User actions

[0153] Users access the system through their devices to search for and view specific landscape regulations and examples of countermeasures. The system utilizes web browsers and mobile applications, allowing users to enter search queries into a search box and initiate the information retrieval process by pressing a submit button.

[0154] 2. The role of the terminal

[0155] The terminal receives search queries from the user and sends them to the server as HTTP requests. The user interface also provides a screen for displaying search results and supports the input and display of information. Specifically, the user enters search queries such as "Osaka City's latest landscape ordinance" or "Examples of landscape measures for commercial buildings in Shibuya Ward."

[0156] 3. Server processing

[0157] The server analyzes the search query received from the terminal and searches the database based on it. This database contains landscape regulations from each local government and examples of countermeasures from across the country. The search results are processed by a generative AI model running on the server, which suggests supplementary information and optimal countermeasures. This transforms the results into more useful and specific information. The generated results are converted into JSON format and sent back to the terminal.

[0158] 4. Processing of Generative AI Models

[0159] The generation AI model is installed on the server and is responsible for generating supplementary information and optimal solutions based on search results. This model utilizes user search queries and information obtained from the database to perform advanced data processing and calculations. For example, it proposes optimal solutions for "Examples of landscape improvement measures for commercial buildings in Shibuya Ward."

[0160] Specific operation examples

[0161] 1. Search for information on landscape regulations

[0162] - User: Enter "Osaka City's latest landscape ordinance" into the search box on your device.

[0163] - Terminal: Receives search queries and sends them to the server as HTTP requests.

[0164] - Server: Analyzes search queries, searches the database to retrieve Osaka City's landscape ordinance information, and adds supplementary information using a generative AI model. The results are returned to the terminal in JSON format.

[0165] - Terminal: Displays received data on the user interface, allowing users to check information regarding Osaka City's landscape regulations.

[0166] 2. Search for examples of countermeasures

[0167] - User: Enter "Examples of landscape measures for commercial buildings in Shibuya Ward" into the search box on your device.

[0168] - Terminal: Receives search queries and sends them to the server as HTTP requests.

[0169] - Server: Analyzes search queries, searches the database to retrieve countermeasure examples for Shibuya Ward, and selects the most suitable examples using a generative AI model. The results are returned to the terminal in JSON format.

[0170] - Terminal: Displays received data on the user interface, allowing users to view examples of optimal commercial building landscape measures in Shibuya Ward.

[0171] Examples of prompt statements

[0172] "Could you please tell me about the latest landscape regulations in Osaka City?"

[0173] "Please show us examples of landscape design measures for commercial buildings in Shibuya Ward."

[0174] By implementing this invention, users will be able to efficiently comply with landscape regulations and easily purchase necessary products. The system structure and the coordination of each component will promote compliance with regulations and information sharing among construction companies.

[0175] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0176] Step 1:

[0177] The user enters a search query into the search box on their device and presses the submit button.

[0178] Specific actions:

[0179] The user enters a specific search query into their device, such as "Osaka City's latest landscape ordinance."

[0180] Input: A search query such as "Osaka City's latest landscape ordinance".

[0181] Output: The query is generated as an HTTP request when the submit button is pressed.

[0182] Step 2:

[0183] The terminal receives user input and sends it to the server as an HTTP request.

[0184] Specific actions:

[0185] The terminal generates an HTTP request for the entered search query "Osaka City's latest landscape ordinance" and sends it to the server.

[0186] Input: The search query entered by the user.

[0187] Output: Data sent to the server as an HTTP request.

[0188] Step 3:

[0189] The server parses the HTTP request received from the terminal and extracts the query.

[0190] Specific actions:

[0191] The server receives the HTTP request, analyzes its contents, and extracts the search query "Osaka City's latest landscape ordinance."

[0192] Input: HTTP request.

[0193] Output: Analyzed search query.

[0194] Step 4:

[0195] The server searches the database based on the analyzed query.

[0196] Specific actions:

[0197] The server generates an SQL query against the database based on the search query "Osaka City's latest landscape ordinance" and executes the search.

[0198] Input: The analyzed search query.

[0199] Output: Relevant information retrieved from the database.

[0200] Step 5:

[0201] The server inputs information retrieved from the database into an AI model, which then generates supplementary information.

[0202] Specific actions:

[0203] The server inputs information retrieved from the database into an AI model to generate supplementary information and optimal countermeasures.

[0204] Input: Information retrieved from the database.

[0205] Output: Generated results including supplementary information and optimal solutions.

[0206] Step 6:

[0207] The server converts the generated results into JSON format and sends them back to the terminal.

[0208] Specific actions:

[0209] The server converts the results obtained from the generated AI model into JSON format and sends it to the terminal as an HTTP response.

[0210] Input: Results obtained from a generative AI model.

[0211] Output: Data converted to JSON format.

[0212] Step 7:

[0213] The terminal parses the received JSON data and displays it in the user interface.

[0214] Specific actions:

[0215] The terminal parses the JSON data received from the server and displays it on the user interface as information about "Osaka City's latest landscape regulations."

[0216] Input: Data in JSON format.

[0217] Output: Search results displayed in the user interface.

[0218] The above outlines the system's program processing flow. This flow allows users to quickly and efficiently obtain the information they need.

[0219] (Application Example 1)

[0220] 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."

[0221] Conventional landscape regulation compliance systems required manual information retrieval and evaluation, which was inefficient. Especially in large-scale facilities such as factories, real-time evaluation and proposal of appropriate countermeasures are required. Furthermore, quickly determining compliance with the latest landscape regulations was difficult, resulting in significant effort to maintain compliance. This invention aims to solve these problems and provide a system that enables factory robots to respond to landscape regulations quickly and efficiently.

[0222] 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.

[0223] In this invention, the server includes means for the user to input a search query, means for sending the input query to the server, means for searching a database based on the query, means for processing the search results with a generating AI model and adding supplementary information, means for returning the processed results to the user, means for displaying the returned results on a user interface, means for acquiring images for landscape evaluation, and means for performing an evaluation based on the acquired images and regulatory data. This makes it possible to evaluate in real time whether a factory robot complies with landscape regulations and to quickly propose the optimal countermeasures.

[0224] "User" refers to individuals or corporate representatives who use the system to search for and view landscape regulations and countermeasures.

[0225] A "search query" refers to a series of words or sentences that a user enters into a system to search for specific information.

[0226] A "server" refers to a computer system that manages databases, operates generated AI models, and processes requests.

[0227] A "database" refers to a collection of information that stores landscape regulations from various local governments and examples of countermeasures from across the country.

[0228] A "generative AI model" refers to an artificial intelligence model used to process search data and suggest supplementary information and optimal solutions.

[0229] A "knowledge base" refers to a collection of information that systematically organizes knowledge about a particular field.

[0230] A "landscape ordinance" refers to the rules and standards established by each local government to protect beautiful environments and landscapes.

[0231] "Factory robots" refer to robots used to automate various tasks within a factory.

[0232] "User interface" refers to the display screens and input devices that users use to interact with a system.

[0233] "Evaluation" refers to the act of judging a value or state based on specific standards or rules.

[0234] "Examples of countermeasures" refer to specific examples and methods of landscape improvement measures implemented in the past.

[0235] This invention provides a system that offers a knowledge base for quickly and efficiently responding to the landscape regulations of each local government. This system operates through the coordinated efforts of user, terminal, and server components. Its specific configuration is described below.

[0236] System Configuration

[0237] The system consists of the following elements:

[0238] 1. User: A representative from a construction company who uses the system to search and view landscape regulations and examples of countermeasures.

[0239] 2. Terminal: A client device used by the user, including web browsers and mobile applications, as well as cameras and sensors installed on factory robots.

[0240] 3. Server: A computer system that handles the backend of the system, managing databases, operating generated AI models, and processing requests.

[0241] Server Role

[0242] The server performs the following actions:

[0243] The system receives search queries from users, parses those queries, and searches the database.

[0244] The search results are processed using an AI model, and supplementary information and optimal solutions are suggested.

[0245] The processed results are returned to the user in JSON format.

[0246] Terminal role

[0247] The terminal performs the following actions:

[0248] It provides a user interface, offering forms for entering search queries and screens for receiving display results.

[0249] Cameras and sensors scan the interior and exterior of the factory, and the captured images are sent to a server.

[0250] Display the results returned from the server and make them available for users to view.

[0251] User roles

[0252] The user performs the following actions:

[0253] Access the system and enter a search query to find specific landscape regulations or best practice solutions.

[0254] Operate factory robots to acquire necessary images using cameras and sensors.

[0255] Review the evaluation results and proposed countermeasures returned from the server and implement them.

[0256] Hardware and software to be used

[0257] Hardware: Camera devices, sensors, terminals (PCs, smartphones, tablets), servers

[0258] Software: OpenCV (image processing library), requests (HTTP request library), generative AI models (AI model frameworks such as TENSORFLOW® and PyTorch)

[0259] Specific example

[0260] For example, consider a scenario where a factory in Tokyo checks whether a newly installed sign complies with the latest landscape regulations. The user operates a factory robot to capture images of the sign with a camera and sends the images to a server. The generated AI model evaluates the acquired images and the latest Tokyo landscape regulations data, and proposes appropriate countermeasures. The following prompt statements are used as an example of system operation:

[0261] "Please evaluate whether the signage at this factory is appropriate based on the latest Shinjuku Ward landscape regulations. Also, please provide suggestions for improvement if necessary."

[0262] This makes it possible to evaluate in real time whether factory robots are complying with landscape regulations and to quickly propose the most suitable countermeasures.

[0263] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0264] Step 1:

[0265] The user enters the search query through the device's user interface. Specifically, the user uses a web browser or mobile application to enter a search query such as "Shinjuku Ward's latest landscape regulations." The data entered is the search query (text), which is then used for the next step.

[0266] Step 2:

[0267] The terminal sends the entered search query to the server. The terminal generates an HTTP request and includes the search query as the payload. This request is sent to the server, which receives the search query to use for analysis.

[0268] Step 3:

[0269] The server parses the received search query and searches the database. The server then accesses the database and generates and executes an SQL query to retrieve the relevant municipal landscape regulations and related information. The data retrieved from the database includes the text of the regulations and related information.

[0270] Step 4:

[0271] The server uses a generative AI model to process the retrieved search results and add supplementary information and suggested actions. The generative AI model takes the ordinance text retrieved from the database as input and generates additional interpretations and appropriate suggested actions. The output consists of search results with supplementary information and suggested actions.

[0272] Step 5:

[0273] The server converts the processed results into JSON format and sends them back to the terminal. Here, the server appropriately formats the generated supplementary information and proposed solutions and sends them to the terminal as an HTTP response. This response becomes the input data for the next step.

[0274] Step 6:

[0275] The terminal receives the results sent back from the server and displays them in the user interface. The terminal parses the received JSON data, converts it into a user-friendly format, and displays it on the screen.

[0276] Step 7:

[0277] The user operates a factory robot and acquires images of the landscape using a camera. The user uses the robot to scan the local landscape and capture the necessary image data with the camera. The acquired data is in the form of image files.

[0278] Step 8:

[0279] The terminal sends the acquired image data to the server. The terminal includes the image file as the payload of the HTTP request and sends it to the server. The transmitted image data serves as the input for the next step.

[0280] Step 9:

[0281] The server performs an evaluation based on the image data and the regulation data. The server uses the generative AI model and passes the transmitted image data and the regulation data acquired earlier as inputs. The AI model analyzes these and evaluates whether it complies with the landscape regulations. As output, an evaluation result and necessary countermeasure plans are generated.

[0282] Step 10:

[0283] The server converts the evaluation result and the countermeasure plan into JSON format and returns them to the terminal. Here, the server appropriately formats the data including the evaluation result and the generated countermeasure plan and sends it to the terminal as an HTTP response.

[0284] Step 11:

[0285] The terminal receives the evaluation result returned from the server and displays it on the user interface. The terminal parses the received JSON data and displays it so that the user can view it. Based on this, the user can execute appropriate countermeasures.

[0286] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion recognition model 59 and perform specific processing using the user's emotion.

[0287] The present invention is a system that provides a knowledge base for quickly and efficiently responding to the landscape regulations of each local government. This system is characterized in that each component of the user, terminal, and server operates in cooperation, and particularly in that it is equipped with an emotion engine for recognizing the user's emotion.

[0288] System Overview

[0289] This system consists of the following elements:

[0290] 1. User: A representative from a construction company who uses the system to search and view landscape regulations and examples of countermeasures.

[0291] 2. Terminal: A client device used by the user, including web browsers and mobile applications.

[0292] 3. Server: A computer system that handles the backend of the system, managing databases, operating AI models, and processing requests.

[0293] 4. Emotion Engine: A component that recognizes the user's emotions when entering search queries or viewing information, and adjusts search results and displayed content accordingly.

[0294] System operation

[0295] User actions

[0296] Users access the system using their devices to search for specific landscape regulations and best practices. The information retrieval process begins when the user enters a search query into the search box on their device and presses the submit button.

[0297] Terminal role

[0298] The device provides a user interface, offering forms for entering search queries and screens for receiving display results. When a user enters and submits a search query, it is sent to the server as an HTTP request. The device is equipped with an emotion engine that recognizes the user's emotional state in real time.

[0299] Server Processing

[0300] The server analyzes the search query received from the terminal and searches the database based on it. The database stores the landscape regulations of each local government and national countermeasure cases. The search results are processed by the generative AI model, and supplementary information and optimal countermeasures are proposed. The server returns these results to the terminal in JSON format. Results adjusted by the sentiment engine are also included.

[0301] Specific operation examples

[0302] Example 1: Search for landscape regulation information

[0303] User: Enter "The latest landscape regulations in Osaka City" in the search box of the terminal.

[0304] Sentiment engine: Recognize and analyze the user's sentiment when inputting.

[0305] Terminal: Receive the search query and send it to the server as an HTTP request.

[0306] Server: Analyze the search query, search the database to obtain the landscape regulation information of Osaka City, and generate supplementary information according to the user's sentiment by the sentiment engine.

[0307] Server: Return the generated results to the terminal in JSON format.

[0308] Terminal: Display the received data on the user interface so that the user can check the landscape regulation information of Osaka City.

[0309] Example 2: Search for countermeasure cases

[0310] User: Enter "Landscape countermeasure cases for commercial buildings in Shibuya Ward" in the search box of the terminal.

[0311] Sentiment engine: Recognize and analyze the user's sentiment when inputting, and monitor the user's sentiment state even after sending the search query.

[0312] Terminal: Receives search queries and sends them to the server as HTTP requests.

[0313] Server: Analyzes search queries, searches the database to retrieve case studies for Shibuya Ward. An emotion engine scores and selects case studies based on the user's emotions.

[0314] Server: Generates optimal countermeasure examples and supplementary information, and sends them back to the terminal in JSON format.

[0315] Terminal: Displays received data on the user interface, allowing users to review examples of countermeasures.

[0316] Example 3: Providing product information and purchasing procedures

[0317] User: Enter "fence for landscape improvement" into the product search box on the device.

[0318] Emotion Engine: Recognizes and analyzes the user's emotions at the time of input. It also monitors the user's emotional state after the search query has been submitted.

[0319] Terminal: Receives search queries and sends them to the server as HTTP requests.

[0320] Server: Analyzes search queries and searches the product database. The sentiment engine provides product information tailored to the user's emotions.

[0321] Server: Generates organized product information in JSON format and sends it back to the terminal.

[0322] Terminal: Displays received data on the user interface, allowing users to view detailed product information.

[0323] User: Review the displayed product information, add the desired product to your cart, and begin the purchase process.

[0324] Terminal: Collects information related to the purchase process and sends it to the server as an HTTP request.

[0325] Server: Receives information regarding the purchase process and processes the payment. The emotion engine monitors the user's emotions during the payment process and sends follow-up messages as needed.

[0326] Server: If the purchase is successful, send a confirmation email to the user and a purchase completion message back to the device.

[0327] Terminal: Displays a purchase completion message on the user interface to inform the user that the purchase was successful. The emotion engine also analyzes the user's emotions after the purchase to help with future purchases.

[0328] Other features

[0329] This system also offers the following features:

[0330] Emotional Engine: Adjusts search results and displayed content based on user emotions. Also recommends important solutions and appropriate products.

[0331] Information provided: We provide detailed information (specifications, price, stock availability, etc.) on products necessary for landscape improvement measures.

[0332] Purchase Process: Manage the process for users to purchase selected products online and provide support tailored to their emotional needs.

[0333] This allows users to efficiently comply with landscape regulations and easily purchase necessary products. The combination with the emotion engine improves the user experience and enables more personalized and effective information delivery. By implementing this invention, compliance with regulations and mutual information sharing among construction companies will be promoted.

[0334] The following describes the processing flow.

[0335] Specific processing steps of the program

[0336] Example 1: Searching for information on landscape regulations

[0337] Step 1:

[0338] User: Enter "Osaka City's latest landscape ordinance" into the search box on your device and click the search button.

[0339] Step 2:

[0340] Terminal: Receives the search query entered by the user, and uses an emotion engine to acquire the user's emotion at the time of input using an emotion recognition sensor.

[0341] Step 3:

[0342] Terminal: Combines sentiment data and search queries into an HTTP request and sends it to the server.

[0343] Step 4:

[0344] Server: Receives HTTP requests from terminals, parses the request content, and extracts search queries and sentiment data.

[0345] Step 5:

[0346] Server: Searches the "Landscape Regulations" table in the database based on the search query. Uses indexes for efficient searching.

[0347] Step 6:

[0348] Server: Passes search results retrieved from the database to the AI ​​model, which generates necessary supplementary information and annotations. Adjusts the supplementary information according to sentiment data.

[0349] Step 7:

[0350] Server: Combines the generated results and supplementary information to produce the final response in JSON format.

[0351] Step 8:

[0352] Server: Sends the generated JSON response back to the terminal.

[0353] Step 9:

[0354] Terminal: Receives the JSON response sent back from the server and parses the data.

[0355] Step 10:

[0356] Terminal: Displays parsed data on the user interface, allowing users to check information regarding Osaka City's landscape regulations.

[0357] Example 2: Searching for countermeasure examples

[0358] Step 1:

[0359] User: Enter "Examples of landscape measures for commercial buildings in Shibuya Ward" into the search box on your device and click the search button.

[0360] Step 2:

[0361] Terminal: Receives the search query entered by the user and uses the sentiment engine to recognize the user's emotion at the time of input.

[0362] Step 3:

[0363] Terminal: Combines sentiment data and search queries into an HTTP request and sends it to the server.

[0364] Step 4:

[0365] Server: Receives HTTP requests from terminals, parses the request content, and extracts search queries and sentiment data.

[0366] Step 5:

[0367] Server: Based on the search query, it searches the "Case Studies" table in the database. Indexes are used to improve efficiency.

[0368] Step 6:

[0369] Server: Passes search results obtained from the database to a generating AI model, which scores and selects the most suitable countermeasures. Based on sentiment data, it prioritizes selecting cases with high recommendation levels.

[0370] Step 7:

[0371] Server: Combines scored optimal countermeasures with supplementary information to generate the final response in JSON format.

[0372] Step 8:

[0373] Server: Sends the generated JSON response back to the terminal.

[0374] Step 9:

[0375] Terminal: Receives the JSON response sent back from the server and parses the data.

[0376] Step 10:

[0377] Terminal: Displays the parsed data on the user interface, allowing users to review case studies.

[0378] Example 3: Providing product information and purchasing procedures

[0379] Step 1:

[0380] User: Enter "fence for landscape improvement" into the product search box on the terminal and click the search button.

[0381] Step 2:

[0382] Terminal: Receives the search query entered by the user and uses the sentiment engine to recognize the user's emotion at the time of input.

[0383] Step 3:

[0384] Terminal: Combines sentiment data and search queries into an HTTP request and sends it to the server.

[0385] Step 4:

[0386] Server: Receives HTTP requests from terminals, parses the request content, and extracts search queries and sentiment data.

[0387] Step 5:

[0388] Server: Searches the product database based on the search query. Performs filtering based on product category and characteristics.

[0389] Step 6:

[0390] Server: Organizes product information retrieved from the database and prioritizes presenting highly recommended products based on sentiment data.

[0391] Step 7:

[0392] Server: Generates organized product information in JSON format and sends it back to the terminal.

[0393] Step 8:

[0394] Terminal: Receives the JSON response sent back from the server and parses the data.

[0395] Step 9:

[0396] Terminal: Displays parsed data on the user interface, allowing users to view detailed product information.

[0397] Step 10:

[0398] User: Review the displayed product information, add the desired product to your cart, and begin the purchase process.

[0399] Step 11:

[0400] Terminal: Collects information related to the purchase process (user information, payment information, etc.) and sends it to the server as an HTTP request.

[0401] Step 12:

[0402] Server: Receives information regarding the purchase process and processes the payment. Confirms the success of the payment. Based on sentiment data, sends follow-up messages to the user as needed.

[0403] Step 13:

[0404] Server: If the purchase is successful, send a confirmation email to the user and a purchase completion message back to the device.

[0405] Step 14:

[0406] Terminal: Displays the purchase completion message returned from the server on the user interface, notifying the user that the purchase was successful. The emotion engine also monitors the user's emotional state after the purchase and uses this information to improve future services.

[0407] (Example 2)

[0408] 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".

[0409] Traditional systems often failed to consider the user's emotional state when they entered search queries to obtain information, resulting in insufficient information quality. Furthermore, especially when sophisticated information such as legal compliance or landscape protection was required, users could experience stress or confusion due to information overload. The information acquisition and purchasing processes also suffered from frustration due to the lack of consideration for user emotions. Therefore, there is a growing need for systems that recognize user emotions in real time and optimize information provision based on those emotions.

[0410] 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.

[0411] In this invention, the server includes means for the user to input a search query, means for recognizing the user's emotions in real time at the time of input, means for transmitting the input query and emotion information to the server, means for searching a database based on the query and emotion information, means for processing the search results with a generating artificial intelligence model and adding supplementary information based on the emotion information, means for returning the processed results to the user, and means for displaying the returned results on the user interface. This makes it possible to provide optimal information while taking the user's emotions into consideration, improving the user experience and enabling efficient and effective acquisition of information related to legal compliance and landscape measures.

[0412] A "user" refers to a person or organization that uses the system to enter search queries and obtain information.

[0413] "Terminal" refers to a client device used by a user, including devices such as web browsers and mobile applications.

[0414] A "server" refers to a computer system that handles the backend of a system, managing databases, operating AI models, and processing requests.

[0415] A "search query" refers to a question, either in text or other form, that a user enters into their device to obtain specific information.

[0416] An "emotion engine" refers to a system component that recognizes and analyzes a user's emotional state in real time when they enter search queries or browse information.

[0417] An "HTTP request" refers to a form of communication protocol used to send user search queries and other data to a server.

[0418] A "database" refers to an information aggregation system that systematically stores specific information and allows it to be searched and retrieved.

[0419] A "generative artificial intelligence model" refers to a technology that uses pre-trained algorithms to analyze data and generate predictions and supplementary information.

[0420] "JSON format" refers to a lightweight data exchange format for structuring, storing, and exchanging data.

[0421] "User interface" refers to screens and designs that provide visual or interactive elements for users to interact with a system.

[0422] "Search results" refer to the collection of information retrieved from the database based on the entered search query.

[0423] "Supplemental information" refers to additional information added to the main search results, data intended to help users gain a deeper understanding.

[0424] A "case study of countermeasures" refers to a specific example that shows the solutions implemented to address a particular problem, along with their details.

[0425] "Product information" refers to detailed information, specifications, prices, and stock availability of products necessary for landscape improvement measures.

[0426] "Purchase process" refers to the series of steps involved in a user purchasing a selected product online.

[0427] "Following" refers to the act of providing appropriate support or additional information to users based on their emotional state during the purchase or search process.

[0428] This invention provides a system that offers a knowledge base for quickly and efficiently responding to the landscape regulations of each local government. Specifically, it is characterized by the coordinated operation of user, terminal, and server components, and in particular by the inclusion of an emotion engine that recognizes the user's emotions. The following details specific embodiments of this system.

[0429] System Configuration

[0430] This system consists of the following elements:

[0431] 1. User: A person or organization that searches for and views landscape regulations and examples of countermeasures.

[0432] 2. Terminal: This refers to a client device used by the user, including devices such as web browsers and mobile applications. The terminal is equipped with an emotion engine.

[0433] 3. Server: Responsible for the system's backend, including database management, operation of generated AI models, and request processing.

[0434] Hardware and software details

[0435] 1. Emotional Engine:

[0436] Hardware: Cameras and microphones for real-time analysis of the user's facial expressions and voice.

[0437] Software: Facial expression recognition software, voice emotion analysis tool.

[0438] 2. Database:

[0439] Hardware: High-performance data server.

[0440] Software: SQL Database Management System (DBMS).

[0441] 3. Generative AI Models:

[0442] Hardware: Server equipped with a high-performance GPU.

[0443] Software: Deep learning libraries (e.g., TensorFlow, PyTorch).

[0444] System Operation Description

[0445] User actions

[0446] Users access the system using their devices to search for specific landscape regulations and best practices. Specifically, the information retrieval process begins when the user enters a search query into the search box on their device and presses the submit button.

[0447] Terminal role

[0448] The device provides a user interface, including a form for entering search queries and a screen for receiving display results. When a user enters and submits a search query, it is sent to the server as an HTTP request. The device also incorporates an emotion engine that recognizes the user's emotional state in real time. This emotional information is included in the data sent to the server.

[0449] Server Processing

[0450] The server analyzes the search query and sentiment information received from the terminal and searches the database based on this analysis. The database contains landscape regulations from each local government and examples of countermeasures from across the country. The search results are processed by a generative AI model, which suggests supplementary information and optimal countermeasures. The sentiment engine also makes adjustments based on the user's emotions. The server returns these results to the terminal in JSON format.

[0451] Specific operation examples

[0452] Example 1: Searching for information on landscape regulations

[0453] User: Enter "Osaka City's latest landscape ordinance" into the search box on your device.

[0454] Emotion Engine: Recognizes and analyzes the emotions of the user during input.

[0455] Terminal: Receives search queries and sentiment information and sends them to the server as HTTP requests.

[0456] Server: Analyzes queries and sentiment information, retrieves Osaka City's landscape ordinance information from the database, and adds supplementary information.

[0457] Server: Returns the generated results to the terminal in JSON format.

[0458] Terminal: Displays received data in the user interface.

[0459] Example 2: Searching for countermeasure examples

[0460] User: Enter "Examples of landscape measures for commercial buildings in Shibuya Ward" into the search box on your device.

[0461] Emotion Engine: Continuously recognizes the user's emotions as they input.

[0462] Terminal: Receives search queries and sentiment information and sends them to the server as HTTP requests.

[0463] Server: Analyzes queries and sentiment information, and retrieves countermeasure examples from Shibuya Ward in the database. Selects countermeasure examples that have been adjusted by the sentiment engine.

[0464] Server: Returns optimal countermeasure examples to the terminal in JSON format.

[0465] Terminal: Displays received data in the user interface.

[0466] Example of a prompt

[0467] "I want to know the latest Osaka City landscape regulations."

[0468] "Search for examples of landscape improvement measures for commercial buildings in Shibuya Ward."

[0469] "We would like more detailed information about fences used for landscape preservation."

[0470] This allows users to efficiently comply with landscape regulations and easily purchase necessary products. The combination of emotional engines improves the user experience and enables personalized and effective information delivery. It also promotes compliance with regulations by construction companies and facilitates mutual information sharing.

[0471] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0472] Step 1:

[0473] User input:

[0474] The user enters a specific query (e.g., "Osaka City's latest landscape regulations") into the search box on their device and clicks the submit button.

[0475] Input: The search query entered by the user.

[0476] Output: Click event of the submit button.

[0477] Specific operation: Enter text into the search box and click the submit button. During this process, the emotion engine analyzes the user's facial expressions and voice in real time to obtain emotional information.

[0478] Step 2:

[0479] Generating a search request on the device:

[0480] The terminal sends the user's input query and analyzed sentiment information to the server as an HTTP request.

[0481] Input: User-entered search queries and sentiment information analyzed by the sentiment engine.

[0482] Output: HTTP request.

[0483] Specific operation: Structures search queries and sentiment information entered within the terminal and generates them as HTTP requests.

[0484] Step 3:

[0485] Query parsing on the server:

[0486] The server parses the received HTTP request, extracts the query content and sentiment information, and then analyzes it.

[0487] Input: HTTP request (including search query and sentiment information).

[0488] Output: Analyzed query information and sentiment information.

[0489] Specific actions: The query content is parsed to extract key information for searching. Simultaneously, sentiment information is analyzed to understand the user's current emotional state.

[0490] Step 4:

[0491] Database search and AI model usage:

[0492] The server searches the database based on the query and uses a generative AI model to retrieve and analyze relevant information.

[0493] Input: Analyzed query information and sentiment information.

[0494] Output: Relevant information and analysis results retrieved from the database.

[0495] Specific operations: Access the database, search for and retrieve data related to the query. Analyze the retrieved data using a generative AI model and select the information to provide to the user.

[0496] Step 5:

[0497] Search result generation and sentiment engine optimization:

[0498] The server uses an emotion engine to adjust search results based on the user's emotional information and adds supplementary information.

[0499] Input: Relevant information and analysis results obtained from the database, and user sentiment information.

[0500] Output: Search results with adjustments and supplementary information added.

[0501] Specific operation: Based on data analyzed by the generative AI model, necessary supplementary information is generated, and the emotion engine adds information according to the user's emotional state.

[0502] Step 6:

[0503] Returning search results:

[0504] The server returns the adjusted search results to the terminal in JSON format.

[0505] Input: Search results with adjustments and supplementary information added.

[0506] Output: Search results encoded in JSON format.

[0507] Specific operation: Encode the generated search results into JSON format and send them to the terminal as an HTTP response.

[0508] Step 7:

[0509] Displaying results on the device:

[0510] The terminal parses the received JSON data and displays it on the user interface.

[0511] Input: Search results in JSON format.

[0512] Output: Search results displayed on the user interface.

[0513] Specific operation: Parses JSON data and displays it in a user-friendly format. Provides information to the user, such as highlighting particularly important sections. The sentiment engine continues to analyze the user's emotions at this point and dynamically adjusts the displayed content as needed.

[0514] Step 8:

[0515] User feedback:

[0516] The user reviews the displayed search results and then takes the next action.

[0517] Input: The displayed search results.

[0518] Output: Input for the next query and other operations.

[0519] Specific actions: The system reviews the information provided by the user and asks further questions if there are any unclear points. Additionally, the sentiment engine records the user's emotional state to help with future searches.

[0520] (Application Example 2)

[0521] 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".

[0522] The conventional system lacked a sufficient knowledge base to respond quickly and efficiently to the landscape regulations and countermeasures of each local government, and was unable to address the emotions of users. Furthermore, in material selection and design proposals at the factory site, appropriate support that took into account the emotions of operators was not provided, resulting in decreased work efficiency.

[0523] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for the user to input a search query, means for sending the input query to the server, means for searching the database based on the query, means for processing the search results with a generating AI model and adding supplementary information, means for returning the processed results to the user, means for displaying the returned results on the user interface, and means for recognizing the user's emotions using an emotion engine and adjusting the search results and displayed content based on those emotions. This makes it possible to select the optimal materials and propose designs that correspond to the user's emotions.

[0524] A "user" is a person or operator who uses this system to enter search queries and receive the results.

[0525] A "search query" is a question or keyword that a user enters to obtain specific information.

[0526] A "server" is a computer system that handles the backend of a system, managing databases, operating AI models, and processing requests.

[0527] A "database" is a collection of information that stores landscape regulations, countermeasures, and product information from various local governments.

[0528] A "generative AI model" is an artificial intelligence algorithm that processes information corresponding to search queries and suggests supplementary information and optimal solutions.

[0529] "Supplemental information" refers to related information and reference materials added to the search results.

[0530] A "user interface" refers to the screens and means of operation that a user uses to interact with a system.

[0531] An "emotion engine" is emotion recognition software that recognizes a user's emotions and adjusts search results and displayed content accordingly.

[0532] A "landscape ordinance" is a set of rules and policies established by each local government regarding the protection of the landscape.

[0533] A "case study of countermeasures" is a specific example of a method or approach for solving a particular problem.

[0534] "Product information" refers to detailed data about a product, such as specifications, price, and stock availability.

[0535] The "purchase process" is the process by which a user buys a selected product online.

[0536] This invention provides a system that offers a knowledge base for quickly and efficiently responding to the landscape regulations of each local government. A detailed description of the embodiments for carrying out the invention is given below.

[0537] System components

[0538] This system consists of the following elements:

[0539] 1. User: A factory operator who uses the system to search and view landscape regulations and examples of countermeasures.

[0540] 2. Terminal: A client device used by the user, including touch panels and voice input devices.

[0541] 3. Server: A computer system that handles the backend of the system, managing databases, operating AI models, and processing requests.

[0542] 4. Emotion Engine: A component that recognizes the user's emotions when entering search queries or viewing information, and adjusts search results and displayed content accordingly.

[0543] System operation

[0544] User actions

[0545] Users access the system using a terminal to search for specific landscape regulations and optimal solutions. For example, if a factory operator wants to search for "eco-friendly materials," they would type the search term into the touch panel and begin the search. During this process, the user's emotions (positive or negative) are recognized and analyzed by an emotion engine.

[0546] Terminal role

[0547] The device provides a user interface, offering forms for entering search queries and screens for receiving display results. The device also incorporates an emotion engine that recognizes the user's emotional state in real time.

[0548] Server Processing

[0549] The server analyzes the search query received from the terminal and searches the database based on it. The database contains landscape regulations and countermeasures examples from each local government. The search results are processed by a generative AI model, which suggests supplementary information and optimal countermeasures. The server returns these results to the terminal in JSON format. The results also include adjustments made by an emotion engine.

[0550] Specific operation examples

[0551] Example 1: Searching for information on landscape regulations

[0552] User: Enter "Osaka City's latest landscape ordinance" into the search box on your device.

[0553] Emotion Engine: Recognizes and analyzes the emotions of the user during input.

[0554] Terminal: Receives search queries and sends them to the server.

[0555] Server: Analyzes search queries, searches the database to retrieve information on Osaka City's landscape regulations. A sentiment engine generates supplementary information tailored to the user's emotions.

[0556] Server: Returns the generated results to the terminal in JSON format.

[0557] Terminal: Displays received data on the user interface, allowing users to check information regarding Osaka City's landscape regulations.

[0558] Example 2: Searching for countermeasure examples

[0559] User: Enter "Examples of landscape measures for commercial buildings in Shibuya Ward" into the search box on your device.

[0560] Emotion Engine: Recognizes and analyzes the user's emotions at the time of input. It also monitors the user's emotional state after the search query has been submitted.

[0561] Terminal: Receives search queries and sends them to the server.

[0562] Server: Analyzes search queries, searches the database to retrieve case studies for Shibuya Ward. An emotion engine scores and selects case studies based on the user's emotions.

[0563] Server: Generates optimal countermeasure examples and supplementary information, and sends them back to the terminal.

[0564] Terminal: Displays received data on the user interface, allowing users to review examples of countermeasures.

[0565] Example 3: Providing product information and purchasing procedures

[0566] User: Enter "fence for landscape protection" into the search box on your device.

[0567] Emotion Engine: Recognizes and analyzes the user's emotions at the time of input. It also monitors the user's emotional state after the search query has been submitted.

[0568] Terminal: Receives search queries and sends them to the server.

[0569] Server: Analyzes search queries and searches the product database. The sentiment engine provides product information tailored to the user's emotions.

[0570] Server: Generates organized product information and sends it back to the terminal.

[0571] Terminal: Displays received data on the user interface, allowing users to view detailed product information.

[0572] User: Review the displayed product information, add the desired product to your cart, and begin the purchase process.

[0573] Terminal: Collects information related to the purchase process and sends it to the server.

[0574] Server: Receives information regarding the purchase process and processes the payment. The emotion engine monitors the user's emotions during the payment process and sends follow-up messages as needed.

[0575] Server: If the purchase is successful, send a confirmation email to the user and a purchase completion message back to the device.

[0576] Terminal: Displays a purchase completion message on the user interface to inform the user that the purchase was successful. The emotion engine also analyzes the user's emotions after the purchase to help with future purchases.

[0577] Examples of prompts to input into a generative AI model

[0578] "Search for eco-friendly materials and suggest them to the operator. The operator's current sentiment is positive. Please suggest the best materials and explain why."

[0579] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0580] Step 1:

[0581] The user enters a search query. Specifically, a factory operator uses the terminal's touch panel or voice input device to enter a query seeking specific information. This input is stored on the terminal as text data.

[0582] Step 2:

[0583] The terminal sends the entered query to the server. This is done using an HTTP request. The entered query text is included in the body of the HTTP request and sent to the specified endpoint on the server.

[0584] Step 3:

[0585] The server parses the search queries it receives. Specifically, it analyzes the received query text using a natural language processing engine and extracts its meaning. This analysis result is then used as a condition for database searches.

[0586] Step 4:

[0587] The server searches the database based on the query. Based on the analysis results, it creates an SQL query and retrieves relevant information (landscape regulations, countermeasure examples, product information, etc.) from the database. The retrieved information is stored in an internal database.

[0588] Step 5:

[0589] The server processes the search results using a generative AI model and adds supplementary information. Specifically, the search results are input into the generative AI model, and relevant supplementary information and further suggestions are obtained as output. This output includes the information the user is looking for, along with related supplementary information.

[0590] Step 6:

[0591] The server sends input queries and user sentiment data to the sentiment engine, which then adjusts the search results and displayed content. The sentiment engine analyzes the user's sentiment data input and adjusts the priority and display format of search results based on that analysis.

[0592] Step 7:

[0593] The server converts the processed results into JSON format and sends them back to the terminal. The generated search results, supplementary information, and data including the sentiment engine adjustment results are encoded in JSON format and sent back as an HTTP response.

[0594] Step 8:

[0595] The system receives the results of the device's return and displays them on the user interface. It parses the received JSON data and displays it in a user-friendly format (text, graphs, images, etc.). The user can then review this information and decide on their next action (e.g., additional search or purchase).

[0596] 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.

[0597] 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.

[0598] 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.

[0599] [Second Embodiment]

[0600] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0601] 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.

[0602] 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).

[0603] 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.

[0604] 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.

[0605] 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).

[0606] 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.

[0607] 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.

[0608] 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.

[0609] 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.

[0610] 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.

[0611] 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".

[0612] This invention provides a system that offers a knowledge base for quickly and efficiently responding to the landscape regulations of each local government. This system operates through the coordinated efforts of user, terminal, and server components.

[0613] System Overview

[0614] This system consists of the following elements:

[0615] 1. User: A representative from a construction company who uses the system to search and view landscape regulations and examples of countermeasures.

[0616] 2. Terminal: A client device used by the user, including web browsers and mobile applications.

[0617] 3. Server: A computer system that handles the backend of the system, managing databases, operating AI models, and processing requests.

[0618] System operation

[0619] User actions

[0620] Users access the system using their devices to search for specific landscape regulations and best practices. The information retrieval process begins when the user enters a search query into the search box on their device and presses the submit button.

[0621] Terminal role

[0622] The terminal provides a user interface, including a form for entering search queries and a screen for receiving display results. When a user enters and submits a search query, that query is sent to the server as an HTTP request.

[0623] Server Processing

[0624] The server analyzes the search query received from the terminal and searches the database based on it. The database contains landscape regulations from each local government and examples of countermeasures from across the country. The search results are processed by a generative AI model, which suggests supplementary information and optimal countermeasures. The server returns these results to the terminal in JSON format.

[0625] Specific operation examples

[0626] Example 1: Searching for information on landscape regulations

[0627] User: Enter "Osaka City's latest landscape ordinance" into the search box on your device.

[0628] Terminal: Receives search queries and sends them to the server as HTTP requests.

[0629] Server: Analyzes search queries, searches the database to retrieve Osaka City's landscape ordinance information, and adds supplementary information using a generative AI model. The results are returned to the terminal in JSON format.

[0630] Terminal: Displays received data on the user interface, allowing users to check information regarding Osaka City's landscape regulations.

[0631] Example 2: Searching for countermeasure examples

[0632] User: Enter "Examples of landscape measures for commercial buildings in Shibuya Ward" into the search box on your device.

[0633] Terminal: Receives search queries and sends them to the server as HTTP requests.

[0634] Server: Analyzes search queries, searches the database to retrieve case studies for Shibuya Ward, and selects the most suitable case using a generative AI model. The results are returned to the terminal in JSON format.

[0635] Terminal: Displays received data on the user interface, allowing users to view examples of optimal commercial building landscape measures in Shibuya Ward.

[0636] Other features

[0637] This system also offers the following features:

[0638] Product Information Provision: We provide detailed information (specifications, price, stock availability, etc.) on products necessary for landscape improvement measures.

[0639] Purchase Procedure: Manages the process for users to purchase selected products online.

[0640] This allows users to efficiently comply with landscape regulations and easily purchase necessary products. Implementing this invention promotes legal compliance among construction companies and facilitates mutual information sharing.

[0641] The following describes the processing flow.

[0642] Specific processing steps of the program

[0643] Example 1: Searching for information on landscape regulations

[0644] Step 1:

[0645] User: Enter "Osaka City's latest landscape ordinance" into the search box on your device and click the search button.

[0646] Step 2:

[0647] Terminal: Receives the search query entered by the user and sends it to the server as an HTTP request.

[0648] Step 3:

[0649] Server: Receives HTTP requests from terminals, parses the request content, and extracts the search query.

[0650] Step 4:

[0651] Server: Searches the "Landscape Regulations" table in the database based on the search query. Uses indexes for efficient searching.

[0652] Step 5:

[0653] Server: Passes search results retrieved from the database to the AI ​​model, which then generates necessary supplementary information and annotations.

[0654] Step 6:

[0655] Server: Combines the generated results with the original search results to produce the final response in JSON format.

[0656] Step 7:

[0657] Server: Sends the generated JSON response back to the terminal.

[0658] Step 8:

[0659] Terminal: Receives the JSON response sent back from the server and parses the data.

[0660] Step 9:

[0661] Terminal: Displays the parsed data on the user interface, allowing the user to see the information they need.

[0662] Example 2: Searching for countermeasure examples

[0663] Step 1:

[0664] User: Enter "Examples of landscape measures for commercial buildings in Shibuya Ward" into the search box on your device and click the search button.

[0665] Step 2:

[0666] Terminal: Receives the search query entered by the user and sends it to the server as an HTTP request.

[0667] Step 3:

[0668] Server: Receives HTTP requests from terminals, parses the request content, and extracts the search query.

[0669] Step 4:

[0670] Server: Based on the search query, it searches the "Case Studies" table in the database. Indexes are used to improve efficiency.

[0671] Step 5:

[0672] Server: The server passes search results obtained from the database to an AI model that scores and selects the most suitable countermeasures.

[0673] Step 6:

[0674] Server: Combines scored optimal countermeasures and supplementary information to generate the final response in JSON format.

[0675] Step 7:

[0676] Server: Sends the generated JSON response back to the terminal.

[0677] Step 8:

[0678] Terminal: Receives the JSON response sent back from the server and parses the data.

[0679] Step 9:

[0680] Terminal: Displays the parsed data on the user interface, allowing users to review case studies.

[0681] Example 3: Providing product information and purchasing procedures

[0682] Step 1:

[0683] User: Enter "fence for landscape improvement" into the product search box on the terminal and click the search button.

[0684] Step 2:

[0685] Terminal: Receives the search query entered by the user and sends it to the server as an HTTP request.

[0686] Step 3:

[0687] Server: Receives HTTP requests from terminals, parses the request content, and extracts the search query.

[0688] Step 4:

[0689] Server: Searches the product database based on the search query. Performs filtering based on product category and characteristics.

[0690] Step 5:

[0691] Server: Organizes product information retrieved from the database and prepares detailed information such as specifications, price, and stock status.

[0692] Step 6:

[0693] Server: Generates organized product information in JSON format and sends it back to the terminal.

[0694] Step 7:

[0695] Terminal: Receives the JSON response sent back from the server and parses the data.

[0696] Step 8:

[0697] Terminal: Displays parsed data on the user interface, allowing users to view detailed product information.

[0698] Step 9:

[0699] User: Review the displayed product information, add the desired product to your cart, and begin the purchase process.

[0700] Step 10:

[0701] Terminal: Collects information related to the purchase process (user information, payment information, etc.) and sends it to the server as an HTTP request.

[0702] Step 11:

[0703] Server: Receives information regarding the purchase procedure and processes the payment. Confirms the success of the payment.

[0704] Step 12:

[0705] Server: If the purchase is successful, send a confirmation email to the user and a purchase completion message back to the device.

[0706] Step 13:

[0707] Terminal: Displays the purchase completion message returned from the server on the user interface, notifying the user that the purchase was successful.

[0708] (Example 1)

[0709] 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."

[0710] In recent years, there has been a growing need to respond quickly and efficiently to the landscape regulations of various local governments. However, for many construction company personnel, collecting information on these regulations and taking appropriate measures is a very time-consuming and laborious task. Furthermore, obtaining appropriate product information and smoothly carrying out the purchase process is also difficult. The present invention aims to solve these problems and provide a system that can respond to landscape regulations efficiently and quickly.

[0711] 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.

[0712] In this invention, the server includes means for selecting and presenting the most suitable countermeasures from the search results, means for providing product information necessary for landscape countermeasures and managing the purchase procedure, and means for returning the processed results to the user. This allows the user to efficiently comply with landscape regulations and easily purchase the necessary products.

[0713] A "user" is a representative from a construction company whose role is to use the system to search for and view landscape regulations and examples of countermeasures.

[0714] A "terminal" refers to a client device used by a user, including web browsers and mobile applications.

[0715] A "server" is a computer system that handles the backend of a system, managing databases, operating generated AI models, and processing requests.

[0716] A "search query" is a string of characters or a phrase that a user enters into their device to seek specific information.

[0717] An "HTTP request" is a type of communication sent from a client device (terminal) to a server requesting a specific operation or data.

[0718] A "database" is a collection of information that stores landscape regulations and examples of countermeasures from various local governments.

[0719] A "generative AI model" is an artificial intelligence model that operates on a server and generates supplementary information and optimal countermeasures based on search results.

[0720] JSON format is a standard format for representing data in text format, and it is a data exchange format that has the structure of objects and arrays.

[0721] A "user interface" refers to the screen or interface that a user uses to operate a device, input information, or view displayed results.

[0722] A "landscape ordinance" is a set of laws and regulations enacted by a local government for the purpose of protecting or improving the landscape of the area.

[0723] An "example of countermeasure" is an example of a specific countermeasure that was taken in the past to address a particular situation or problem.

[0724] "Product information" refers to detailed information about products necessary for landscape improvement measures, including specifications, price, and stock availability.

[0725] The "purchase process" refers to the series of tasks and management processes involved in a user purchasing a selected product online.

[0726] This invention provides a system that offers a knowledge base for quickly and efficiently responding to the landscape regulations of each local government. This system operates through the coordinated action of multiple components, including user terminals, servers, and generative AI models.

[0727] 1. User actions

[0728] Users access the system through their devices to search for and view specific landscape regulations and examples of countermeasures. The system utilizes web browsers and mobile applications, allowing users to enter search queries into a search box and initiate the information retrieval process by pressing a submit button.

[0729] 2. The role of the terminal

[0730] The terminal receives search queries from the user and sends them to the server as HTTP requests. The user interface also provides a screen for displaying search results and supports the input and display of information. Specifically, the user enters search queries such as "Osaka City's latest landscape ordinance" or "Examples of landscape measures for commercial buildings in Shibuya Ward."

[0731] 3. Server processing

[0732] The server analyzes the search query received from the terminal and searches the database based on it. This database contains landscape regulations from each local government and examples of countermeasures from across the country. The search results are processed by a generative AI model running on the server, which suggests supplementary information and optimal countermeasures. This transforms the results into more useful and specific information. The generated results are converted into JSON format and sent back to the terminal.

[0733] 4. Processing of Generative AI Models

[0734] The generation AI model is installed on the server and is responsible for generating supplementary information and optimal solutions based on search results. This model utilizes user search queries and information obtained from the database to perform advanced data processing and calculations. For example, it proposes optimal solutions for "Examples of landscape improvement measures for commercial buildings in Shibuya Ward."

[0735] Specific operation examples

[0736] 1. Search for information on landscape regulations

[0737] - User: Enter "Osaka City's latest landscape ordinance" into the search box on your device.

[0738] - Terminal: Receives search queries and sends them to the server as HTTP requests.

[0739] - Server: Analyzes search queries, searches the database to retrieve Osaka City's landscape ordinance information, and adds supplementary information using a generative AI model. The results are returned to the terminal in JSON format.

[0740] - Terminal: Displays received data on the user interface, allowing users to check information regarding Osaka City's landscape regulations.

[0741] 2. Search for examples of countermeasures

[0742] - User: Enter "Examples of landscape measures for commercial buildings in Shibuya Ward" into the search box on your device.

[0743] - Terminal: Receives search queries and sends them to the server as HTTP requests.

[0744] - Server: Analyzes search queries, searches the database to retrieve countermeasure examples for Shibuya Ward, and selects the most suitable examples using a generative AI model. The results are returned to the terminal in JSON format.

[0745] - Terminal: Displays received data on the user interface, allowing users to view examples of optimal commercial building landscape measures in Shibuya Ward.

[0746] Examples of prompt statements

[0747] "Could you please tell me about the latest landscape regulations in Osaka City?"

[0748] "Please show us examples of landscape design measures for commercial buildings in Shibuya Ward."

[0749] By implementing this invention, users will be able to efficiently comply with landscape regulations and easily purchase necessary products. The system structure and the coordination of each component will promote compliance with regulations and information sharing among construction companies.

[0750] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0751] Step 1:

[0752] The user enters a search query into the search box on their device and presses the submit button.

[0753] Specific actions:

[0754] The user enters a specific search query into their device, such as "Osaka City's latest landscape ordinance."

[0755] Input: A search query such as "Osaka City's latest landscape ordinance".

[0756] Output: The query is generated as an HTTP request when the submit button is pressed.

[0757] Step 2:

[0758] The terminal receives user input and sends it to the server as an HTTP request.

[0759] Specific actions:

[0760] The terminal generates an HTTP request for the entered search query "Osaka City's latest landscape ordinance" and sends it to the server.

[0761] Input: The search query entered by the user.

[0762] Output: Data sent to the server as an HTTP request.

[0763] Step 3:

[0764] The server parses the HTTP request received from the terminal and extracts the query.

[0765] Specific actions:

[0766] The server receives the HTTP request, analyzes its contents, and extracts the search query "Osaka City's latest landscape ordinance."

[0767] Input: HTTP request.

[0768] Output: Analyzed search query.

[0769] Step 4:

[0770] The server searches the database based on the analyzed query.

[0771] Specific actions:

[0772] The server generates an SQL query against the database based on the search query "Osaka City's latest landscape ordinance" and executes the search.

[0773] Input: The analyzed search query.

[0774] Output: Relevant information retrieved from the database.

[0775] Step 5:

[0776] The server inputs information retrieved from the database into an AI model, which then generates supplementary information.

[0777] Specific actions:

[0778] The server inputs information retrieved from the database into an AI model to generate supplementary information and optimal countermeasures.

[0779] Input: Information retrieved from the database.

[0780] Output: Generated results including supplementary information and optimal solutions.

[0781] Step 6:

[0782] The server converts the generated results into JSON format and sends them back to the terminal.

[0783] Specific actions:

[0784] The server converts the results obtained from the generated AI model into JSON format and sends it to the terminal as an HTTP response.

[0785] Input: Results obtained from a generative AI model.

[0786] Output: Data converted to JSON format.

[0787] Step 7:

[0788] The terminal parses the received JSON data and displays it in the user interface.

[0789] Specific actions:

[0790] The terminal parses the JSON data received from the server and displays it on the user interface as information about "Osaka City's latest landscape regulations."

[0791] Input: Data in JSON format.

[0792] Output: Search results displayed in the user interface.

[0793] The above outlines the system's program processing flow. This flow allows users to quickly and efficiently obtain the information they need.

[0794] (Application Example 1)

[0795] 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."

[0796] Conventional landscape regulation compliance systems required manual information retrieval and evaluation, which was inefficient. Especially in large-scale facilities such as factories, real-time evaluation and proposal of appropriate countermeasures are required. Furthermore, quickly determining compliance with the latest landscape regulations was difficult, resulting in significant effort to maintain compliance. This invention aims to solve these problems and provide a system that enables factory robots to respond to landscape regulations quickly and efficiently.

[0797] 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.

[0798] In this invention, the server includes means for the user to input a search query, means for sending the input query to the server, means for searching a database based on the query, means for processing the search results with a generating AI model and adding supplementary information, means for returning the processed results to the user, means for displaying the returned results on a user interface, means for acquiring images for landscape evaluation, and means for performing an evaluation based on the acquired images and regulatory data. This makes it possible to evaluate in real time whether a factory robot complies with landscape regulations and to quickly propose the optimal countermeasures.

[0799] "User" refers to individuals or corporate representatives who use the system to search for and view landscape regulations and countermeasures.

[0800] A "search query" refers to a series of words or sentences that a user enters into a system to search for specific information.

[0801] A "server" refers to a computer system that manages databases, operates generated AI models, and processes requests.

[0802] A "database" refers to a collection of information that stores landscape regulations from various local governments and examples of countermeasures from across the country.

[0803] A "generative AI model" refers to an artificial intelligence model used to process search data and suggest supplementary information and optimal solutions.

[0804] A "knowledge base" refers to a collection of information that systematically organizes knowledge about a particular field.

[0805] A "landscape ordinance" refers to the rules and standards established by each local government to protect beautiful environments and landscapes.

[0806] "Factory robots" refer to robots used to automate various tasks within a factory.

[0807] "User interface" refers to the display screens and input devices that users use to interact with a system.

[0808] "Evaluation" refers to the act of judging a value or state based on specific standards or rules.

[0809] "Examples of countermeasures" refer to specific examples and methods of landscape improvement measures implemented in the past.

[0810] This invention provides a system that offers a knowledge base for quickly and efficiently responding to the landscape regulations of each local government. This system operates through the coordinated efforts of user, terminal, and server components. Its specific configuration is described below.

[0811] System Configuration

[0812] The system consists of the following elements:

[0813] 1. User: A representative from a construction company who uses the system to search and view landscape regulations and examples of countermeasures.

[0814] 2. Terminal: A client device used by the user, including web browsers and mobile applications, as well as cameras and sensors installed on factory robots.

[0815] 3. Server: A computer system that handles the backend of the system, managing databases, operating generated AI models, and processing requests.

[0816] Server Role

[0817] The server performs the following actions:

[0818] The system receives search queries from users, parses those queries, and searches the database.

[0819] The search results are processed using an AI model, and supplementary information and optimal solutions are suggested.

[0820] The processed results are returned to the user in JSON format.

[0821] Terminal role

[0822] The terminal performs the following actions:

[0823] It provides a user interface, offering forms for entering search queries and screens for receiving display results.

[0824] Cameras and sensors scan the interior and exterior of the factory, and the captured images are sent to a server.

[0825] Display the results returned from the server and make them available for users to view.

[0826] User roles

[0827] The user performs the following actions:

[0828] Access the system and enter a search query to find specific landscape regulations or best practice solutions.

[0829] Operate factory robots to acquire necessary images using cameras and sensors.

[0830] Review the evaluation results and proposed countermeasures returned from the server and implement them.

[0831] Hardware and software to be used

[0832] Hardware: Camera devices, sensors, terminals (PCs, smartphones, tablets), servers

[0833] Software: OpenCV (image processing library), requests (HTTP request library), generative AI models (TensorFlow and PyTorch as AI model frameworks)

[0834] Specific example

[0835] For example, consider a scenario where a factory in Tokyo checks whether a newly installed sign complies with the latest landscape regulations. The user operates a factory robot to capture images of the sign with a camera and sends the images to a server. The generated AI model evaluates the acquired images and the latest Tokyo landscape regulations data, and proposes appropriate countermeasures. The following prompt statements are used as an example of system operation:

[0836] "Please evaluate whether the signage at this factory is appropriate based on the latest Shinjuku Ward landscape regulations. Also, please provide suggestions for improvement if necessary."

[0837] This makes it possible to evaluate in real time whether factory robots are complying with landscape regulations and to quickly propose the most suitable countermeasures.

[0838] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0839] Step 1:

[0840] The user enters the search query through the device's user interface. Specifically, the user uses a web browser or mobile application to enter a search query such as "Shinjuku Ward's latest landscape regulations." The data entered is the search query (text), which is then used for the next step.

[0841] Step 2:

[0842] The terminal sends the entered search query to the server. The terminal generates an HTTP request and includes the search query as the payload. This request is sent to the server, which receives the search query to use for analysis.

[0843] Step 3:

[0844] The server parses the received search query and searches the database. The server then accesses the database and generates and executes an SQL query to retrieve the relevant municipal landscape regulations and related information. The data retrieved from the database includes the text of the regulations and related information.

[0845] Step 4:

[0846] The server uses a generative AI model to process the retrieved search results and add supplementary information and suggested actions. The generative AI model takes the ordinance text retrieved from the database as input and generates additional interpretations and appropriate suggested actions. The output consists of search results with supplementary information and suggested actions.

[0847] Step 5:

[0848] The server converts the processed results into JSON format and sends them back to the terminal. Here, the server appropriately formats the generated supplementary information and proposed solutions and sends them to the terminal as an HTTP response. This response becomes the input data for the next step.

[0849] Step 6:

[0850] The terminal receives the results sent back from the server and displays them in the user interface. The terminal parses the received JSON data, converts it into a user-friendly format, and displays it on the screen.

[0851] Step 7:

[0852] The user operates a factory robot and acquires images of the landscape using a camera. The user uses the robot to scan the local landscape and capture the necessary image data with the camera. The acquired data is in the form of image files.

[0853] Step 8:

[0854] The device sends the acquired image data to the server. The device includes the image file as the payload of an HTTP request and sends it to the server. The transmitted image data becomes the input for the next step.

[0855] Step 9:

[0856] The server performs an evaluation based on image data and regulatory data. The server uses a generative AI model, receiving the transmitted image data and the previously acquired regulatory data as input. The AI ​​model analyzes these and evaluates whether it complies with the landscape regulations. The output generates the evaluation results and necessary countermeasures.

[0857] Step 10:

[0858] The server converts the evaluation results and proposed countermeasures into JSON format and sends them back to the terminal. Here, the server appropriately formats the data containing the evaluation results and generated countermeasures and sends it to the terminal as an HTTP response.

[0859] Step 11:

[0860] The terminal receives the evaluation results sent back from the server and displays them on the user interface. The terminal parses the received JSON data and displays it for the user to review. Based on this, the user can take appropriate action.

[0861] 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.

[0862] This invention provides a system that offers a knowledge base for quickly and efficiently responding to the landscape regulations of each local government. This system features a user, terminal, and server component working in coordination, and is particularly characterized by its inclusion of an emotion engine that recognizes the user's emotions.

[0863] System Overview

[0864] This system consists of the following elements:

[0865] 1. User: A representative from a construction company who uses the system to search and view landscape regulations and examples of countermeasures.

[0866] 2. Terminal: A client device used by the user, including web browsers and mobile applications.

[0867] 3. Server: A computer system that handles the backend of the system, managing databases, operating AI models, and processing requests.

[0868] 4. Emotion Engine: A component that recognizes the user's emotions when entering search queries or viewing information, and adjusts search results and displayed content accordingly.

[0869] System operation

[0870] User actions

[0871] Users access the system using their devices to search for specific landscape regulations and best practices. The information retrieval process begins when the user enters a search query into the search box on their device and presses the submit button.

[0872] Terminal role

[0873] The device provides a user interface, offering forms for entering search queries and screens for receiving display results. When a user enters and submits a search query, it is sent to the server as an HTTP request. The device is equipped with an emotion engine that recognizes the user's emotional state in real time.

[0874] Server Processing

[0875] The server analyzes the search query received from the terminal and searches the database based on it. The database contains landscape regulations from each local government and examples of countermeasures from across the country. The search results are processed by a generative AI model, which suggests supplementary information and optimal countermeasures. The server returns these results to the terminal in JSON format. The results also include adjustments made by an emotion engine.

[0876] Specific operation examples

[0877] Example 1: Searching for information on landscape regulations

[0878] User: Enter "Osaka City's latest landscape ordinance" into the search box on your device.

[0879] Emotion Engine: Recognizes and analyzes the emotions of the user during input.

[0880] Terminal: Receives search queries and sends them to the server as HTTP requests.

[0881] Server: Analyzes search queries, searches the database to retrieve information on Osaka City's landscape regulations. A sentiment engine generates supplementary information tailored to the user's emotions.

[0882] Server: Returns the generated results to the terminal in JSON format.

[0883] Terminal: Displays received data on the user interface, allowing users to check information regarding Osaka City's landscape regulations.

[0884] Example 2: Searching for countermeasure examples

[0885] User: Enter "Examples of landscape measures for commercial buildings in Shibuya Ward" into the search box on your device.

[0886] Emotion Engine: Recognizes and analyzes the user's emotions at the time of input. It also monitors the user's emotional state after the search query has been submitted.

[0887] Terminal: Receives search queries and sends them to the server as HTTP requests.

[0888] Server: Analyzes search queries, searches the database to retrieve case studies for Shibuya Ward. An emotion engine scores and selects case studies based on the user's emotions.

[0889] Server: Generates optimal countermeasure examples and supplementary information, and sends them back to the terminal in JSON format.

[0890] Terminal: Displays received data on the user interface, allowing users to review examples of countermeasures.

[0891] Example 3: Providing product information and purchasing procedures

[0892] User: Enter "fence for landscape improvement" into the product search box on the device.

[0893] Emotion Engine: Recognizes and analyzes the user's emotions at the time of input. It also monitors the user's emotional state after the search query has been submitted.

[0894] Terminal: Receives search queries and sends them to the server as HTTP requests.

[0895] Server: Analyzes search queries and searches the product database. The sentiment engine provides product information tailored to the user's emotions.

[0896] Server: Generates organized product information in JSON format and sends it back to the terminal.

[0897] Terminal: Displays received data on the user interface, allowing users to view detailed product information.

[0898] User: Review the displayed product information, add the desired product to your cart, and begin the purchase process.

[0899] Terminal: Collects information related to the purchase process and sends it to the server as an HTTP request.

[0900] Server: Receives information regarding the purchase process and processes the payment. The emotion engine monitors the user's emotions during the payment process and sends follow-up messages as needed.

[0901] Server: If the purchase is successful, send a confirmation email to the user and a purchase completion message back to the device.

[0902] Terminal: Displays a purchase completion message on the user interface to inform the user that the purchase was successful. The emotion engine also analyzes the user's emotions after the purchase to help with future purchases.

[0903] Other features

[0904] This system also offers the following features:

[0905] Emotional Engine: Adjusts search results and displayed content based on user emotions. Also recommends important solutions and appropriate products.

[0906] Information provided: We provide detailed information (specifications, price, stock availability, etc.) on products necessary for landscape improvement measures.

[0907] Purchase Process: Manage the process for users to purchase selected products online and provide support tailored to their emotional needs.

[0908] This allows users to efficiently comply with landscape regulations and easily purchase necessary products. The combination with the emotion engine improves the user experience and enables more personalized and effective information delivery. By implementing this invention, compliance with regulations and mutual information sharing among construction companies will be promoted.

[0909] The following describes the processing flow.

[0910] Specific processing steps of the program

[0911] Example 1: Searching for information on landscape regulations

[0912] Step 1:

[0913] User: Enter "Osaka City's latest landscape ordinance" into the search box on your device and click the search button.

[0914] Step 2:

[0915] Terminal: Receives the search query entered by the user, and uses an emotion engine to acquire the user's emotion at the time of input using an emotion recognition sensor.

[0916] Step 3:

[0917] Terminal: Combines sentiment data and search queries into an HTTP request and sends it to the server.

[0918] Step 4:

[0919] Server: Receives HTTP requests from terminals, parses the request content, and extracts search queries and sentiment data.

[0920] Step 5:

[0921] Server: Searches the "Landscape Regulations" table in the database based on the search query. Uses indexes for efficient searching.

[0922] Step 6:

[0923] Server: Passes search results retrieved from the database to the AI ​​model, which generates necessary supplementary information and annotations. Adjusts the supplementary information according to sentiment data.

[0924] Step 7:

[0925] Server: Combines the generated results and supplementary information to produce the final response in JSON format.

[0926] Step 8:

[0927] Server: Sends the generated JSON response back to the terminal.

[0928] Step 9:

[0929] Terminal: Receives the JSON response sent back from the server and parses the data.

[0930] Step 10:

[0931] Terminal: Displays parsed data on the user interface, allowing users to check information regarding Osaka City's landscape regulations.

[0932] Example 2: Searching for countermeasure examples

[0933] Step 1:

[0934] User: Enter "Examples of landscape measures for commercial buildings in Shibuya Ward" into the search box on your device and click the search button.

[0935] Step 2:

[0936] Terminal: Receives the search query entered by the user and uses the sentiment engine to recognize the user's emotion at the time of input.

[0937] Step 3:

[0938] Terminal: Combines sentiment data and search queries into an HTTP request and sends it to the server.

[0939] Step 4:

[0940] Server: Receives HTTP requests from terminals, parses the request content, and extracts search queries and sentiment data.

[0941] Step 5:

[0942] Server: Based on the search query, it searches the "Case Studies" table in the database. Indexes are used to improve efficiency.

[0943] Step 6:

[0944] Server: Passes search results obtained from the database to a generating AI model, which scores and selects the most suitable countermeasures. Based on sentiment data, it prioritizes selecting cases with high recommendation levels.

[0945] Step 7:

[0946] Server: Combines scored optimal countermeasures with supplementary information to generate the final response in JSON format.

[0947] Step 8:

[0948] Server: Sends the generated JSON response back to the terminal.

[0949] Step 9:

[0950] Terminal: Receives the JSON response sent back from the server and parses the data.

[0951] Step 10:

[0952] Terminal: Displays the parsed data on the user interface, allowing users to review case studies.

[0953] Example 3: Providing product information and purchasing procedures

[0954] Step 1:

[0955] User: Enter "fence for landscape improvement" into the product search box on the terminal and click the search button.

[0956] Step 2:

[0957] Terminal: Receives the search query entered by the user and uses the sentiment engine to recognize the user's emotion at the time of input.

[0958] Step 3:

[0959] Terminal: Combines sentiment data and search queries into an HTTP request and sends it to the server.

[0960] Step 4:

[0961] Server: Receives HTTP requests from terminals, parses the request content, and extracts search queries and sentiment data.

[0962] Step 5:

[0963] Server: Searches the product database based on the search query. Performs filtering based on product category and characteristics.

[0964] Step 6:

[0965] Server: Organizes product information retrieved from the database and prioritizes presenting highly recommended products based on sentiment data.

[0966] Step 7:

[0967] Server: Generates organized product information in JSON format and sends it back to the terminal.

[0968] Step 8:

[0969] Terminal: Receives the JSON response sent back from the server and parses the data.

[0970] Step 9:

[0971] Terminal: Displays parsed data on the user interface, allowing users to view detailed product information.

[0972] Step 10:

[0973] User: Review the displayed product information, add the desired product to your cart, and begin the purchase process.

[0974] Step 11:

[0975] Terminal: Collects information related to the purchase process (user information, payment information, etc.) and sends it to the server as an HTTP request.

[0976] Step 12:

[0977] Server: Receives information regarding the purchase process and processes the payment. Confirms the success of the payment. Based on sentiment data, sends follow-up messages to the user as needed.

[0978] Step 13:

[0979] Server: If the purchase is successful, send a confirmation email to the user and a purchase completion message back to the device.

[0980] Step 14:

[0981] Terminal: Displays the purchase completion message returned from the server on the user interface, notifying the user that the purchase was successful. The emotion engine also monitors the user's emotional state after the purchase and uses this information to improve future services.

[0982] (Example 2)

[0983] 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".

[0984] Traditional systems often failed to consider the user's emotional state when they entered search queries to obtain information, resulting in insufficient information quality. Furthermore, especially when sophisticated information such as legal compliance or landscape protection was required, users could experience stress or confusion due to information overload. The information acquisition and purchasing processes also suffered from frustration due to the lack of consideration for user emotions. Therefore, there is a growing need for systems that recognize user emotions in real time and optimize information provision based on those emotions.

[0985] 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.

[0986] In this invention, the server includes means for the user to input a search query, means for recognizing the user's emotions in real time at the time of input, means for transmitting the input query and emotion information to the server, means for searching a database based on the query and emotion information, means for processing the search results with a generating artificial intelligence model and adding supplementary information based on the emotion information, means for returning the processed results to the user, and means for displaying the returned results on the user interface. This makes it possible to provide optimal information while taking the user's emotions into consideration, improving the user experience and enabling efficient and effective acquisition of information related to legal compliance and landscape measures.

[0987] A "user" refers to a person or organization that uses the system to enter search queries and obtain information.

[0988] "Terminal" refers to a client device used by a user, including devices such as web browsers and mobile applications.

[0989] A "server" refers to a computer system that handles the backend of a system, managing databases, operating AI models, and processing requests.

[0990] A "search query" refers to a question, either in text or other form, that a user enters into their device to obtain specific information.

[0991] An "emotion engine" refers to a system component that recognizes and analyzes a user's emotional state in real time when they enter search queries or browse information.

[0992] An "HTTP request" refers to a form of communication protocol used to send user search queries and other data to a server.

[0993] A "database" refers to an information aggregation system that systematically stores specific information and allows it to be searched and retrieved.

[0994] A "generative artificial intelligence model" refers to a technology that uses pre-trained algorithms to analyze data and generate predictions and supplementary information.

[0995] "JSON format" refers to a lightweight data exchange format for structuring, storing, and exchanging data.

[0996] "User interface" refers to screens and designs that provide visual or interactive elements for users to interact with a system.

[0997] "Search results" refer to the collection of information retrieved from the database based on the entered search query.

[0998] "Supplemental information" refers to additional information added to the main search results, data intended to help users gain a deeper understanding.

[0999] A "case study of countermeasures" refers to a specific example that shows the solutions implemented to address a particular problem, along with their details.

[1000] "Product information" refers to detailed information, specifications, prices, and stock availability of products necessary for landscape improvement measures.

[1001] "Purchase process" refers to the series of steps involved in a user purchasing a selected product online.

[1002] "Following" refers to the act of providing appropriate support or additional information to users based on their emotional state during the purchase or search process.

[1003] This invention provides a system that offers a knowledge base for quickly and efficiently responding to the landscape regulations of each local government. Specifically, it is characterized by the coordinated operation of user, terminal, and server components, and in particular by the inclusion of an emotion engine that recognizes the user's emotions. The following details specific embodiments of this system.

[1004] System Configuration

[1005] This system consists of the following elements:

[1006] 1. User: A person or organization that searches for and views landscape regulations and examples of countermeasures.

[1007] 2. Terminal: This refers to a client device used by the user, including devices such as web browsers and mobile applications. The terminal is equipped with an emotion engine.

[1008] 3. Server: Responsible for the system's backend, including database management, operation of generated AI models, and request processing.

[1009] Hardware and software details

[1010] 1. Emotional Engine:

[1011] Hardware: Cameras and microphones for real-time analysis of the user's facial expressions and voice.

[1012] Software: Facial expression recognition software, voice emotion analysis tool.

[1013] 2. Database:

[1014] Hardware: High-performance data server.

[1015] Software: SQL Database Management System (DBMS).

[1016] 3. Generative AI Models:

[1017] Hardware: Server equipped with a high-performance GPU.

[1018] Software: Deep learning libraries (e.g., TensorFlow, PyTorch).

[1019] System Operation Description

[1020] User actions

[1021] Users access the system using their devices to search for specific landscape regulations and best practices. Specifically, the information retrieval process begins when the user enters a search query into the search box on their device and presses the submit button.

[1022] Terminal role

[1023] The device provides a user interface, including a form for entering search queries and a screen for receiving display results. When a user enters and submits a search query, it is sent to the server as an HTTP request. The device also incorporates an emotion engine that recognizes the user's emotional state in real time. This emotional information is included in the data sent to the server.

[1024] Server Processing

[1025] The server analyzes the search query and sentiment information received from the terminal and searches the database based on this analysis. The database contains landscape regulations from each local government and examples of countermeasures from across the country. The search results are processed by a generative AI model, which suggests supplementary information and optimal countermeasures. The sentiment engine also makes adjustments based on the user's emotions. The server returns these results to the terminal in JSON format.

[1026] Specific operation examples

[1027] Example 1: Searching for information on landscape regulations

[1028] User: Enter "Osaka City's latest landscape ordinance" into the search box on your device.

[1029] Emotion Engine: Recognizes and analyzes the emotions of the user during input.

[1030] Terminal: Receives search queries and sentiment information and sends them to the server as HTTP requests.

[1031] Server: Analyzes queries and sentiment information, retrieves Osaka City's landscape ordinance information from the database, and adds supplementary information.

[1032] Server: Returns the generated results to the terminal in JSON format.

[1033] Terminal: Displays received data in the user interface.

[1034] Example 2: Searching for countermeasure examples

[1035] User: Enter "Examples of landscape measures for commercial buildings in Shibuya Ward" into the search box on your device.

[1036] Emotion Engine: Continuously recognizes the user's emotions as they input.

[1037] Terminal: Receives search queries and sentiment information and sends them to the server as HTTP requests.

[1038] Server: Analyzes queries and sentiment information, and retrieves countermeasure examples from Shibuya Ward in the database. Selects countermeasure examples that have been adjusted by the sentiment engine.

[1039] Server: Returns optimal countermeasure examples to the terminal in JSON format.

[1040] Terminal: Displays received data in the user interface.

[1041] Example of a prompt

[1042] "I want to know the latest Osaka City landscape regulations."

[1043] "Search for examples of landscape improvement measures for commercial buildings in Shibuya Ward."

[1044] "We would like more detailed information about fences used for landscape preservation."

[1045] This allows users to efficiently comply with landscape regulations and easily purchase necessary products. The combination of emotional engines improves the user experience and enables personalized and effective information delivery. It also promotes compliance with regulations by construction companies and facilitates mutual information sharing.

[1046] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1047] Step 1:

[1048] User input:

[1049] The user enters a specific query (e.g., "Osaka City's latest landscape regulations") into the search box on their device and clicks the submit button.

[1050] Input: The search query entered by the user.

[1051] Output: Click event of the submit button.

[1052] Specific operation: Enter text into the search box and click the submit button. During this process, the emotion engine analyzes the user's facial expressions and voice in real time to obtain emotional information.

[1053] Step 2:

[1054] Generating a search request on the device:

[1055] The terminal sends the user's input query and analyzed sentiment information to the server as an HTTP request.

[1056] Input: User-entered search queries and sentiment information analyzed by the sentiment engine.

[1057] Output: HTTP request.

[1058] Specific operation: Structures search queries and sentiment information entered within the terminal and generates them as HTTP requests.

[1059] Step 3:

[1060] Query parsing on the server:

[1061] The server parses the received HTTP request, extracts the query content and sentiment information, and then analyzes it.

[1062] Input: HTTP request (including search query and sentiment information).

[1063] Output: Analyzed query information and sentiment information.

[1064] Specific actions: The query content is parsed to extract key information for searching. Simultaneously, sentiment information is analyzed to understand the user's current emotional state.

[1065] Step 4:

[1066] Database search and AI model usage:

[1067] The server searches the database based on the query and uses a generative AI model to retrieve and analyze relevant information.

[1068] Input: Analyzed query information and sentiment information.

[1069] Output: Relevant information and analysis results retrieved from the database.

[1070] Specific operations: Access the database, search for and retrieve data related to the query. Analyze the retrieved data using a generative AI model and select the information to provide to the user.

[1071] Step 5:

[1072] Search result generation and sentiment engine optimization:

[1073] The server uses an emotion engine to adjust search results based on the user's emotional information and adds supplementary information.

[1074] Input: Relevant information and analysis results obtained from the database, and user sentiment information.

[1075] Output: Search results with adjustments and supplementary information added.

[1076] Specific operation: Based on data analyzed by the generative AI model, necessary supplementary information is generated, and the emotion engine adds information according to the user's emotional state.

[1077] Step 6:

[1078] Returning search results:

[1079] The server returns the adjusted search results to the terminal in JSON format.

[1080] Input: Search results with adjustments and supplementary information added.

[1081] Output: Search results encoded in JSON format.

[1082] Specific operation: Encode the generated search results into JSON format and send them to the terminal as an HTTP response.

[1083] Step 7:

[1084] Displaying results on the device:

[1085] The terminal parses the received JSON data and displays it on the user interface.

[1086] Input: Search results in JSON format.

[1087] Output: Search results displayed on the user interface.

[1088] Specific operation: Parses JSON data and displays it in a user-friendly format. Provides information to the user, such as highlighting particularly important sections. The sentiment engine continues to analyze the user's emotions at this point and dynamically adjusts the displayed content as needed.

[1089] Step 8:

[1090] User feedback:

[1091] The user reviews the displayed search results and then takes the next action.

[1092] Input: The displayed search results.

[1093] Output: Input for the next query and other operations.

[1094] Specific actions: The system reviews the information provided by the user and asks further questions if there are any unclear points. Additionally, the sentiment engine records the user's emotional state to help with future searches.

[1095] (Application Example 2)

[1096] 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."

[1097] The conventional system lacked a sufficient knowledge base to respond quickly and efficiently to the landscape regulations and countermeasures of each local government, and was unable to address the emotions of users. Furthermore, in material selection and design proposals at the factory site, appropriate support that took into account the emotions of operators was not provided, resulting in decreased work efficiency.

[1098] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for the user to input a search query, means for sending the input query to the server, means for searching the database based on the query, means for processing the search results with a generating AI model and adding supplementary information, means for returning the processed results to the user, means for displaying the returned results on the user interface, and means for recognizing the user's emotions using an emotion engine and adjusting the search results and displayed content based on those emotions. This makes it possible to select the optimal materials and propose designs that correspond to the user's emotions.

[1099] A "user" is a person or operator who uses this system to enter search queries and receive the results.

[1100] A "search query" is a question or keyword that a user enters to obtain specific information.

[1101] A "server" is a computer system that handles the backend of a system, managing databases, operating AI models, and processing requests.

[1102] A "database" is a collection of information that stores landscape regulations, countermeasures, and product information from various local governments.

[1103] A "generative AI model" is an artificial intelligence algorithm that processes information corresponding to search queries and suggests supplementary information and optimal solutions.

[1104] "Supplemental information" refers to related information and reference materials added to the search results.

[1105] A "user interface" refers to the screens and means of operation that a user uses to interact with a system.

[1106] An "emotion engine" is emotion recognition software that recognizes a user's emotions and adjusts search results and displayed content accordingly.

[1107] A "landscape ordinance" is a set of rules and policies established by each local government regarding the protection of the landscape.

[1108] A "case study of countermeasures" is a specific example of a method or approach for solving a particular problem.

[1109] "Product information" refers to detailed data about a product, such as specifications, price, and stock availability.

[1110] The "purchase process" is the process by which a user buys a selected product online.

[1111] This invention provides a system that offers a knowledge base for quickly and efficiently responding to the landscape regulations of each local government. A detailed description of the embodiments for carrying out the invention is given below.

[1112] System components

[1113] This system consists of the following elements:

[1114] 1. User: A factory operator who uses the system to search and view landscape regulations and examples of countermeasures.

[1115] 2. Terminal: A client device used by the user, including touch panels and voice input devices.

[1116] 3. Server: A computer system that handles the backend of the system, managing databases, operating AI models, and processing requests.

[1117] 4. Emotion Engine: A component that recognizes the user's emotions when entering search queries or viewing information, and adjusts search results and displayed content accordingly.

[1118] System operation

[1119] User actions

[1120] Users access the system using a terminal to search for specific landscape regulations and optimal solutions. For example, if a factory operator wants to search for "eco-friendly materials," they would type the search term into the touch panel and begin the search. During this process, the user's emotions (positive or negative) are recognized and analyzed by an emotion engine.

[1121] Terminal role

[1122] The device provides a user interface, offering forms for entering search queries and screens for receiving display results. The device also incorporates an emotion engine that recognizes the user's emotional state in real time.

[1123] Server Processing

[1124] The server analyzes the search query received from the terminal and searches the database based on it. The database contains landscape regulations and countermeasures examples from each local government. The search results are processed by a generative AI model, which suggests supplementary information and optimal countermeasures. The server returns these results to the terminal in JSON format. The results also include adjustments made by an emotion engine.

[1125] Specific operation examples

[1126] Example 1: Searching for information on landscape regulations

[1127] User: Enter "Osaka City's latest landscape ordinance" into the search box on your device.

[1128] Emotion Engine: Recognizes and analyzes the emotions of the user during input.

[1129] Terminal: Receives search queries and sends them to the server.

[1130] Server: Analyzes search queries, searches the database to retrieve information on Osaka City's landscape regulations. A sentiment engine generates supplementary information tailored to the user's emotions.

[1131] Server: Returns the generated results to the terminal in JSON format.

[1132] Terminal: Displays received data on the user interface, allowing users to check information regarding Osaka City's landscape regulations.

[1133] Example 2: Searching for countermeasure examples

[1134] User: Enter "Examples of landscape measures for commercial buildings in Shibuya Ward" into the search box on your device.

[1135] Emotion Engine: Recognizes and analyzes the user's emotions at the time of input. It also monitors the user's emotional state after the search query has been submitted.

[1136] Terminal: Receives search queries and sends them to the server.

[1137] Server: Analyzes search queries, searches the database to retrieve case studies for Shibuya Ward. An emotion engine scores and selects case studies based on the user's emotions.

[1138] Server: Generates optimal countermeasure examples and supplementary information, and sends them back to the terminal.

[1139] Terminal: Displays received data on the user interface, allowing users to review examples of countermeasures.

[1140] Example 3: Providing product information and purchasing procedures

[1141] User: Enter "fence for landscape protection" into the search box on your device.

[1142] Emotion Engine: Recognizes and analyzes the user's emotions at the time of input. It also monitors the user's emotional state after the search query has been submitted.

[1143] Terminal: Receives search queries and sends them to the server.

[1144] Server: Analyzes search queries and searches the product database. The sentiment engine provides product information tailored to the user's emotions.

[1145] Server: Generates organized product information and sends it back to the terminal.

[1146] Terminal: Displays received data on the user interface, allowing users to view detailed product information.

[1147] User: Review the displayed product information, add the desired product to your cart, and begin the purchase process.

[1148] Terminal: Collects information related to the purchase process and sends it to the server.

[1149] Server: Receives information regarding the purchase process and processes the payment. The emotion engine monitors the user's emotions during the payment process and sends follow-up messages as needed.

[1150] Server: If the purchase is successful, send a confirmation email to the user and a purchase completion message back to the device.

[1151] Terminal: Displays a purchase completion message on the user interface to inform the user that the purchase was successful. The emotion engine also analyzes the user's emotions after the purchase to help with future purchases.

[1152] Examples of prompts to input into a generative AI model

[1153] "Search for eco-friendly materials and suggest them to the operator. The operator's current sentiment is positive. Please suggest the best materials and explain why."

[1154] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1155] Step 1:

[1156] The user enters a search query. Specifically, a factory operator uses the terminal's touch panel or voice input device to enter a query seeking specific information. This input is stored on the terminal as text data.

[1157] Step 2:

[1158] The terminal sends the entered query to the server. This is done using an HTTP request. The entered query text is included in the body of the HTTP request and sent to the specified endpoint on the server.

[1159] Step 3:

[1160] The server parses the search queries it receives. Specifically, it analyzes the received query text using a natural language processing engine and extracts its meaning. This analysis result is then used as a condition for database searches.

[1161] Step 4:

[1162] The server searches the database based on the query. Based on the analysis results, it creates an SQL query and retrieves relevant information (landscape regulations, countermeasure examples, product information, etc.) from the database. The retrieved information is stored in an internal database.

[1163] Step 5:

[1164] The server processes the search results using a generative AI model and adds supplementary information. Specifically, the search results are input into the generative AI model, and relevant supplementary information and further suggestions are obtained as output. This output includes the information the user is looking for, along with related supplementary information.

[1165] Step 6:

[1166] The server sends input queries and user sentiment data to the sentiment engine, which then adjusts the search results and displayed content. The sentiment engine analyzes the user's sentiment data input and adjusts the priority and display format of search results based on that analysis.

[1167] Step 7:

[1168] The server converts the processed results into JSON format and sends them back to the terminal. The generated search results, supplementary information, and data including the sentiment engine adjustment results are encoded in JSON format and sent back as an HTTP response.

[1169] Step 8:

[1170] The system receives the results of the device's return and displays them on the user interface. It parses the received JSON data and displays it in a user-friendly format (text, graphs, images, etc.). The user can then review this information and decide on their next action (e.g., additional search or purchase).

[1171] 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.

[1172] 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.

[1173] 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.

[1174] [Third Embodiment]

[1175] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[1176] 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.

[1177] 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).

[1178] 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.

[1179] 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.

[1180] 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).

[1181] 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.

[1182] 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.

[1183] 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.

[1184] 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.

[1185] 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.

[1186] 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".

[1187] This invention provides a system that offers a knowledge base for quickly and efficiently responding to the landscape regulations of each local government. This system operates through the coordinated efforts of user, terminal, and server components.

[1188] System Overview

[1189] This system consists of the following elements:

[1190] 1. User: A representative from a construction company who uses the system to search and view landscape regulations and examples of countermeasures.

[1191] 2. Terminal: A client device used by the user, including web browsers and mobile applications.

[1192] 3. Server: A computer system that handles the backend of the system, managing databases, operating AI models, and processing requests.

[1193] System operation

[1194] User actions

[1195] Users access the system using their devices to search for specific landscape regulations and best practices. The information retrieval process begins when the user enters a search query into the search box on their device and presses the submit button.

[1196] Terminal role

[1197] The terminal provides a user interface, including a form for entering search queries and a screen for receiving display results. When a user enters and submits a search query, that query is sent to the server as an HTTP request.

[1198] Server Processing

[1199] The server analyzes the search query received from the terminal and searches the database based on it. The database contains landscape regulations from each local government and examples of countermeasures from across the country. The search results are processed by a generative AI model, which suggests supplementary information and optimal countermeasures. The server returns these results to the terminal in JSON format.

[1200] Specific operation examples

[1201] Example 1: Searching for information on landscape regulations

[1202] User: Enter "Osaka City's latest landscape ordinance" into the search box on your device.

[1203] Terminal: Receives search queries and sends them to the server as HTTP requests.

[1204] Server: Analyzes search queries, searches the database to retrieve Osaka City's landscape ordinance information, and adds supplementary information using a generative AI model. The results are returned to the terminal in JSON format.

[1205] Terminal: Displays received data on the user interface, allowing users to check information regarding Osaka City's landscape regulations.

[1206] Example 2: Searching for countermeasure examples

[1207] User: Enter "Examples of landscape measures for commercial buildings in Shibuya Ward" into the search box on your device.

[1208] Terminal: Receives search queries and sends them to the server as HTTP requests.

[1209] Server: Analyzes search queries, searches the database to retrieve case studies for Shibuya Ward, and selects the most suitable case using a generative AI model. The results are returned to the terminal in JSON format.

[1210] Terminal: Displays received data on the user interface, allowing users to view examples of optimal commercial building landscape measures in Shibuya Ward.

[1211] Other features

[1212] This system also offers the following features:

[1213] Product Information Provision: We provide detailed information (specifications, price, stock availability, etc.) on products necessary for landscape improvement measures.

[1214] Purchase Procedure: Manages the process for users to purchase selected products online.

[1215] This allows users to efficiently comply with landscape regulations and easily purchase necessary products. Implementing this invention promotes legal compliance among construction companies and facilitates mutual information sharing.

[1216] The following describes the processing flow.

[1217] Specific processing steps of the program

[1218] Example 1: Searching for information on landscape regulations

[1219] Step 1:

[1220] User: Enter "Osaka City's latest landscape ordinance" into the search box on your device and click the search button.

[1221] Step 2:

[1222] Terminal: Receives the search query entered by the user and sends it to the server as an HTTP request.

[1223] Step 3:

[1224] Server: Receives HTTP requests from terminals, parses the request content, and extracts the search query.

[1225] Step 4:

[1226] Server: Searches the "Landscape Regulations" table in the database based on the search query. Uses indexes for efficient searching.

[1227] Step 5:

[1228] Server: Passes search results retrieved from the database to the AI ​​model, which then generates necessary supplementary information and annotations.

[1229] Step 6:

[1230] Server: Combines the generated results with the original search results to produce the final response in JSON format.

[1231] Step 7:

[1232] Server: Sends the generated JSON response back to the terminal.

[1233] Step 8:

[1234] Terminal: Receives the JSON response sent back from the server and parses the data.

[1235] Step 9:

[1236] Terminal: Displays the parsed data on the user interface, allowing the user to see the information they need.

[1237] Example 2: Searching for countermeasure examples

[1238] Step 1:

[1239] User: Enter "Examples of landscape measures for commercial buildings in Shibuya Ward" into the search box on your device and click the search button.

[1240] Step 2:

[1241] Terminal: Receives the search query entered by the user and sends it to the server as an HTTP request.

[1242] Step 3:

[1243] Server: Receives HTTP requests from terminals, parses the request content, and extracts the search query.

[1244] Step 4:

[1245] Server: Based on the search query, it searches the "Case Studies" table in the database. Indexes are used to improve efficiency.

[1246] Step 5:

[1247] Server: The server passes search results obtained from the database to an AI model that scores and selects the most suitable countermeasures.

[1248] Step 6:

[1249] Server: Combines scored optimal countermeasures and supplementary information to generate the final response in JSON format.

[1250] Step 7:

[1251] Server: Sends the generated JSON response back to the terminal.

[1252] Step 8:

[1253] Terminal: Receives the JSON response sent back from the server and parses the data.

[1254] Step 9:

[1255] Terminal: Displays the parsed data on the user interface, allowing users to review case studies.

[1256] Example 3: Providing product information and purchasing procedures

[1257] Step 1:

[1258] User: Enter "fence for landscape improvement" into the product search box on the terminal and click the search button.

[1259] Step 2:

[1260] Terminal: Receives the search query entered by the user and sends it to the server as an HTTP request.

[1261] Step 3:

[1262] Server: Receives HTTP requests from terminals, parses the request content, and extracts the search query.

[1263] Step 4:

[1264] Server: Searches the product database based on the search query. Performs filtering based on product category and characteristics.

[1265] Step 5:

[1266] Server: Organizes product information retrieved from the database and prepares detailed information such as specifications, price, and stock status.

[1267] Step 6:

[1268] Server: Generates organized product information in JSON format and sends it back to the terminal.

[1269] Step 7:

[1270] Terminal: Receives the JSON response sent back from the server and parses the data.

[1271] Step 8:

[1272] Terminal: Displays parsed data on the user interface, allowing users to view detailed product information.

[1273] Step 9:

[1274] User: Review the displayed product information, add the desired product to your cart, and begin the purchase process.

[1275] Step 10:

[1276] Terminal: Collects information related to the purchase process (user information, payment information, etc.) and sends it to the server as an HTTP request.

[1277] Step 11:

[1278] Server: Receives information regarding the purchase procedure and processes the payment. Confirms the success of the payment.

[1279] Step 12:

[1280] Server: If the purchase is successful, send a confirmation email to the user and a purchase completion message back to the device.

[1281] Step 13:

[1282] Terminal: Displays the purchase completion message returned from the server on the user interface, notifying the user that the purchase was successful.

[1283] (Example 1)

[1284] 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."

[1285] In recent years, there has been a growing need to respond quickly and efficiently to the landscape regulations of various local governments. However, for many construction company personnel, collecting information on these regulations and taking appropriate measures is a very time-consuming and laborious task. Furthermore, obtaining appropriate product information and smoothly carrying out the purchase process is also difficult. The present invention aims to solve these problems and provide a system that can respond to landscape regulations efficiently and quickly.

[1286] 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.

[1287] In this invention, the server includes means for selecting and presenting the most suitable countermeasures from the search results, means for providing product information necessary for landscape countermeasures and managing the purchase procedure, and means for returning the processed results to the user. This allows the user to efficiently comply with landscape regulations and easily purchase the necessary products.

[1288] A "user" is a representative from a construction company whose role is to use the system to search for and view landscape regulations and examples of countermeasures.

[1289] A "terminal" refers to a client device used by a user, including web browsers and mobile applications.

[1290] A "server" is a computer system that handles the backend of a system, managing databases, operating generated AI models, and processing requests.

[1291] A "search query" is a string of characters or a phrase that a user enters into their device to seek specific information.

[1292] An "HTTP request" is a type of communication sent from a client device (terminal) to a server requesting a specific operation or data.

[1293] A "database" is a collection of information that stores landscape regulations and examples of countermeasures from various local governments.

[1294] A "generative AI model" is an artificial intelligence model that operates on a server and generates supplementary information and optimal countermeasures based on search results.

[1295] JSON format is a standard format for representing data in text format, and it is a data exchange format that has the structure of objects and arrays.

[1296] A "user interface" refers to the screen or interface that a user uses to operate a device, input information, or view displayed results.

[1297] A "landscape ordinance" is a set of laws and regulations enacted by a local government for the purpose of protecting or improving the landscape of the area.

[1298] An "example of countermeasure" is an example of a specific countermeasure that was taken in the past to address a particular situation or problem.

[1299] "Product information" refers to detailed information about products necessary for landscape improvement measures, including specifications, price, and stock availability.

[1300] The "purchase process" refers to the series of tasks and management processes involved in a user purchasing a selected product online.

[1301] This invention provides a system that offers a knowledge base for quickly and efficiently responding to the landscape regulations of each local government. This system operates through the coordinated action of multiple components, including user terminals, servers, and generative AI models.

[1302] 1. User actions

[1303] Users access the system through their devices to search for and view specific landscape regulations and examples of countermeasures. The system utilizes web browsers and mobile applications, allowing users to enter search queries into a search box and initiate the information retrieval process by pressing a submit button.

[1304] 2. The role of the terminal

[1305] The terminal receives search queries from the user and sends them to the server as HTTP requests. The user interface also provides a screen for displaying search results and supports the input and display of information. Specifically, the user enters search queries such as "Osaka City's latest landscape ordinance" or "Examples of landscape measures for commercial buildings in Shibuya Ward."

[1306] 3. Server processing

[1307] The server analyzes the search query received from the terminal and searches the database based on it. This database contains landscape regulations from each local government and examples of countermeasures from across the country. The search results are processed by a generative AI model running on the server, which suggests supplementary information and optimal countermeasures. This transforms the results into more useful and specific information. The generated results are converted into JSON format and sent back to the terminal.

[1308] 4. Processing of Generative AI Models

[1309] The generation AI model is installed on the server and is responsible for generating supplementary information and optimal solutions based on search results. This model utilizes user search queries and information obtained from the database to perform advanced data processing and calculations. For example, it proposes optimal solutions for "Examples of landscape improvement measures for commercial buildings in Shibuya Ward."

[1310] Specific operation examples

[1311] 1. Search for information on landscape regulations

[1312] - User: Enter "Osaka City's latest landscape ordinance" into the search box on your device.

[1313] - Terminal: Receives search queries and sends them to the server as HTTP requests.

[1314] - Server: Analyzes search queries, searches the database to retrieve Osaka City's landscape ordinance information, and adds supplementary information using a generative AI model. The results are returned to the terminal in JSON format.

[1315] - Terminal: Displays received data on the user interface, allowing users to check information regarding Osaka City's landscape regulations.

[1316] 2. Search for examples of countermeasures

[1317] - User: Enter "Examples of landscape measures for commercial buildings in Shibuya Ward" into the search box on your device.

[1318] - Terminal: Receives search queries and sends them to the server as HTTP requests.

[1319] - Server: Analyzes search queries, searches the database to retrieve countermeasure examples for Shibuya Ward, and selects the most suitable examples using a generative AI model. The results are returned to the terminal in JSON format.

[1320] - Terminal: Displays received data on the user interface, allowing users to view examples of optimal commercial building landscape measures in Shibuya Ward.

[1321] Examples of prompt statements

[1322] "Could you please tell me about the latest landscape regulations in Osaka City?"

[1323] "Please show us examples of landscape design measures for commercial buildings in Shibuya Ward."

[1324] By implementing this invention, users will be able to efficiently comply with landscape regulations and easily purchase necessary products. The system structure and the coordination of each component will promote compliance with regulations and information sharing among construction companies.

[1325] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1326] Step 1:

[1327] The user enters a search query into the search box on their device and presses the submit button.

[1328] Specific actions:

[1329] The user enters a specific search query into their device, such as "Osaka City's latest landscape ordinance."

[1330] Input: A search query such as "Osaka City's latest landscape ordinance".

[1331] Output: The query is generated as an HTTP request when the submit button is pressed.

[1332] Step 2:

[1333] The terminal receives user input and sends it to the server as an HTTP request.

[1334] Specific actions:

[1335] The terminal generates an HTTP request for the entered search query "Osaka City's latest landscape ordinance" and sends it to the server.

[1336] Input: The search query entered by the user.

[1337] Output: Data sent to the server as an HTTP request.

[1338] Step 3:

[1339] The server parses the HTTP request received from the terminal and extracts the query.

[1340] Specific actions:

[1341] The server receives the HTTP request, analyzes its contents, and extracts the search query "Osaka City's latest landscape ordinance."

[1342] Input: HTTP request.

[1343] Output: Analyzed search query.

[1344] Step 4:

[1345] The server searches the database based on the analyzed query.

[1346] Specific actions:

[1347] The server generates an SQL query against the database based on the search query "Osaka City's latest landscape ordinance" and executes the search.

[1348] Input: The analyzed search query.

[1349] Output: Relevant information retrieved from the database.

[1350] Step 5:

[1351] The server inputs information retrieved from the database into an AI model, which then generates supplementary information.

[1352] Specific actions:

[1353] The server inputs information retrieved from the database into an AI model to generate supplementary information and optimal countermeasures.

[1354] Input: Information retrieved from the database.

[1355] Output: Generated results including supplementary information and optimal solutions.

[1356] Step 6:

[1357] The server converts the generated results into JSON format and sends them back to the terminal.

[1358] Specific actions:

[1359] The server converts the results obtained from the generated AI model into JSON format and sends it to the terminal as an HTTP response.

[1360] Input: Results obtained from a generative AI model.

[1361] Output: Data converted to JSON format.

[1362] Step 7:

[1363] The terminal parses the received JSON data and displays it in the user interface.

[1364] Specific actions:

[1365] The terminal parses the JSON data received from the server and displays it on the user interface as information about "Osaka City's latest landscape regulations."

[1366] Input: Data in JSON format.

[1367] Output: Search results displayed in the user interface.

[1368] The above outlines the system's program processing flow. This flow allows users to quickly and efficiently obtain the information they need.

[1369] (Application Example 1)

[1370] 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."

[1371] Conventional landscape regulation compliance systems required manual information retrieval and evaluation, which was inefficient. Especially in large-scale facilities such as factories, real-time evaluation and proposal of appropriate countermeasures are required. Furthermore, quickly determining compliance with the latest landscape regulations was difficult, resulting in significant effort to maintain compliance. This invention aims to solve these problems and provide a system that enables factory robots to respond to landscape regulations quickly and efficiently.

[1372] 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.

[1373] In this invention, the server includes means for the user to input a search query, means for sending the input query to the server, means for searching a database based on the query, means for processing the search results with a generating AI model and adding supplementary information, means for returning the processed results to the user, means for displaying the returned results on a user interface, means for acquiring images for landscape evaluation, and means for performing an evaluation based on the acquired images and regulatory data. This makes it possible to evaluate in real time whether a factory robot complies with landscape regulations and to quickly propose the optimal countermeasures.

[1374] "User" refers to individuals or corporate representatives who use the system to search for and view landscape regulations and countermeasures.

[1375] A "search query" refers to a series of words or sentences that a user enters into a system to search for specific information.

[1376] A "server" refers to a computer system that manages databases, operates generated AI models, and processes requests.

[1377] A "database" refers to a collection of information that stores landscape regulations from various local governments and examples of countermeasures from across the country.

[1378] A "generative AI model" refers to an artificial intelligence model used to process search data and suggest supplementary information and optimal solutions.

[1379] A "knowledge base" refers to a collection of information that systematically organizes knowledge about a particular field.

[1380] A "landscape ordinance" refers to the rules and standards established by each local government to protect beautiful environments and landscapes.

[1381] "Factory robots" refer to robots used to automate various tasks within a factory.

[1382] "User interface" refers to the display screens and input devices that users use to interact with a system.

[1383] "Evaluation" refers to the act of judging a value or state based on specific standards or rules.

[1384] "Examples of countermeasures" refer to specific examples and methods of landscape improvement measures implemented in the past.

[1385] This invention provides a system that offers a knowledge base for quickly and efficiently responding to the landscape regulations of each local government. This system operates through the coordinated efforts of user, terminal, and server components. Its specific configuration is described below.

[1386] System Configuration

[1387] The system consists of the following elements:

[1388] 1. User: A representative from a construction company who uses the system to search and view landscape regulations and examples of countermeasures.

[1389] 2. Terminal: A client device used by the user, including web browsers and mobile applications, as well as cameras and sensors installed on factory robots.

[1390] 3. Server: A computer system that handles the backend of the system, managing databases, operating generated AI models, and processing requests.

[1391] Server Role

[1392] The server performs the following actions:

[1393] The system receives search queries from users, parses those queries, and searches the database.

[1394] The search results are processed using an AI model, and supplementary information and optimal solutions are suggested.

[1395] The processed results are returned to the user in JSON format.

[1396] Terminal role

[1397] The terminal performs the following actions:

[1398] It provides a user interface, offering forms for entering search queries and screens for receiving display results.

[1399] Cameras and sensors scan the interior and exterior of the factory, and the captured images are sent to a server.

[1400] Display the results returned from the server and make them available for users to view.

[1401] User roles

[1402] The user performs the following actions:

[1403] Access the system and enter a search query to find specific landscape regulations or best practice solutions.

[1404] Operate factory robots to acquire necessary images using cameras and sensors.

[1405] Review the evaluation results and proposed countermeasures returned from the server and implement them.

[1406] Hardware and software to be used

[1407] Hardware: Camera devices, sensors, terminals (PCs, smartphones, tablets), servers

[1408] Software: OpenCV (image processing library), requests (HTTP request library), generative AI models (TensorFlow and PyTorch as AI model frameworks)

[1409] Specific example

[1410] For example, consider a scenario where a factory in Tokyo checks whether a newly installed sign complies with the latest landscape regulations. The user operates a factory robot to capture images of the sign with a camera and sends the images to a server. The generated AI model evaluates the acquired images and the latest Tokyo landscape regulations data, and proposes appropriate countermeasures. The following prompt statements are used as an example of system operation:

[1411] "Please evaluate whether the signage at this factory is appropriate based on the latest Shinjuku Ward landscape regulations. Also, please provide suggestions for improvement if necessary."

[1412] This makes it possible to evaluate in real time whether factory robots are complying with landscape regulations and to quickly propose the most suitable countermeasures.

[1413] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1414] Step 1:

[1415] The user enters the search query through the device's user interface. Specifically, the user uses a web browser or mobile application to enter a search query such as "Shinjuku Ward's latest landscape regulations." The data entered is the search query (text), which is then used for the next step.

[1416] Step 2:

[1417] The terminal sends the entered search query to the server. The terminal generates an HTTP request and includes the search query as the payload. This request is sent to the server, which receives the search query to use for analysis.

[1418] Step 3:

[1419] The server parses the received search query and searches the database. The server then accesses the database and generates and executes an SQL query to retrieve the relevant municipal landscape regulations and related information. The data retrieved from the database includes the text of the regulations and related information.

[1420] Step 4:

[1421] The server uses a generative AI model to process the retrieved search results and add supplementary information and suggested actions. The generative AI model takes the ordinance text retrieved from the database as input and generates additional interpretations and appropriate suggested actions. The output consists of search results with supplementary information and suggested actions.

[1422] Step 5:

[1423] The server converts the processed results into JSON format and sends them back to the terminal. Here, the server appropriately formats the generated supplementary information and proposed solutions and sends them to the terminal as an HTTP response. This response becomes the input data for the next step.

[1424] Step 6:

[1425] The terminal receives the results sent back from the server and displays them in the user interface. The terminal parses the received JSON data, converts it into a user-friendly format, and displays it on the screen.

[1426] Step 7:

[1427] The user operates a factory robot and acquires images of the landscape using a camera. The user uses the robot to scan the local landscape and capture the necessary image data with the camera. The acquired data is in the form of image files.

[1428] Step 8:

[1429] The device sends the acquired image data to the server. The device includes the image file as the payload of an HTTP request and sends it to the server. The transmitted image data becomes the input for the next step.

[1430] Step 9:

[1431] The server performs an evaluation based on image data and regulatory data. The server uses a generative AI model, receiving the transmitted image data and the previously acquired regulatory data as input. The AI ​​model analyzes these and evaluates whether it complies with the landscape regulations. The output generates the evaluation results and necessary countermeasures.

[1432] Step 10:

[1433] The server converts the evaluation results and proposed countermeasures into JSON format and sends them back to the terminal. Here, the server appropriately formats the data containing the evaluation results and generated countermeasures and sends it to the terminal as an HTTP response.

[1434] Step 11:

[1435] The terminal receives the evaluation results sent back from the server and displays them on the user interface. The terminal parses the received JSON data and displays it for the user to review. Based on this, the user can take appropriate action.

[1436] 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.

[1437] This invention provides a system that offers a knowledge base for quickly and efficiently responding to the landscape regulations of each local government. This system features a user, terminal, and server component working in coordination, and is particularly characterized by its inclusion of an emotion engine that recognizes the user's emotions.

[1438] System Overview

[1439] This system consists of the following elements:

[1440] 1. User: A representative from a construction company who uses the system to search and view landscape regulations and examples of countermeasures.

[1441] 2. Terminal: A client device used by the user, including web browsers and mobile applications.

[1442] 3. Server: A computer system that handles the backend of the system, managing databases, operating AI models, and processing requests.

[1443] 4. Emotion Engine: A component that recognizes the user's emotions when entering search queries or viewing information, and adjusts search results and displayed content accordingly.

[1444] System operation

[1445] User actions

[1446] Users access the system using their devices to search for specific landscape regulations and best practices. The information retrieval process begins when the user enters a search query into the search box on their device and presses the submit button.

[1447] Terminal role

[1448] The device provides a user interface, offering forms for entering search queries and screens for receiving display results. When a user enters and submits a search query, it is sent to the server as an HTTP request. The device is equipped with an emotion engine that recognizes the user's emotional state in real time.

[1449] Server Processing

[1450] The server analyzes the search query received from the terminal and searches the database based on it. The database contains landscape regulations from each local government and examples of countermeasures from across the country. The search results are processed by a generative AI model, which suggests supplementary information and optimal countermeasures. The server returns these results to the terminal in JSON format. The results also include adjustments made by an emotion engine.

[1451] Specific operation examples

[1452] Example 1: Searching for information on landscape regulations

[1453] User: Enter "Osaka City's latest landscape ordinance" into the search box on your device.

[1454] Emotion Engine: Recognizes and analyzes the emotions of the user during input.

[1455] Terminal: Receives search queries and sends them to the server as HTTP requests.

[1456] Server: Analyzes search queries, searches the database to retrieve information on Osaka City's landscape regulations. A sentiment engine generates supplementary information tailored to the user's emotions.

[1457] Server: Returns the generated results to the terminal in JSON format.

[1458] Terminal: Displays received data on the user interface, allowing users to check information regarding Osaka City's landscape regulations.

[1459] Example 2: Searching for countermeasure examples

[1460] User: Enter "Examples of landscape measures for commercial buildings in Shibuya Ward" into the search box on your device.

[1461] Emotion Engine: Recognizes and analyzes the user's emotions at the time of input. It also monitors the user's emotional state after the search query has been submitted.

[1462] Terminal: Receives search queries and sends them to the server as HTTP requests.

[1463] Server: Analyzes search queries, searches the database to retrieve case studies for Shibuya Ward. An emotion engine scores and selects case studies based on the user's emotions.

[1464] Server: Generates optimal countermeasure examples and supplementary information, and sends them back to the terminal in JSON format.

[1465] Terminal: Displays received data on the user interface, allowing users to review examples of countermeasures.

[1466] Example 3: Providing product information and purchasing procedures

[1467] User: Enter "fence for landscape improvement" into the product search box on the device.

[1468] Emotion Engine: Recognizes and analyzes the user's emotions at the time of input. It also monitors the user's emotional state after the search query has been submitted.

[1469] Terminal: Receives search queries and sends them to the server as HTTP requests.

[1470] Server: Analyzes search queries and searches the product database. The sentiment engine provides product information tailored to the user's emotions.

[1471] Server: Generates organized product information in JSON format and sends it back to the terminal.

[1472] Terminal: Displays received data on the user interface, allowing users to view detailed product information.

[1473] User: Review the displayed product information, add the desired product to your cart, and begin the purchase process.

[1474] Terminal: Collects information related to the purchase process and sends it to the server as an HTTP request.

[1475] Server: Receives information regarding the purchase process and processes the payment. The emotion engine monitors the user's emotions during the payment process and sends follow-up messages as needed.

[1476] Server: If the purchase is successful, send a confirmation email to the user and a purchase completion message back to the device.

[1477] Terminal: Displays a purchase completion message on the user interface to inform the user that the purchase was successful. The emotion engine also analyzes the user's emotions after the purchase to help with future purchases.

[1478] Other features

[1479] This system also offers the following features:

[1480] Emotional Engine: Adjusts search results and displayed content based on user emotions. Also recommends important solutions and appropriate products.

[1481] Information provided: We provide detailed information (specifications, price, stock availability, etc.) on products necessary for landscape improvement measures.

[1482] Purchase Process: Manage the process for users to purchase selected products online and provide support tailored to their emotional needs.

[1483] This allows users to efficiently comply with landscape regulations and easily purchase necessary products. The combination with the emotion engine improves the user experience and enables more personalized and effective information delivery. By implementing this invention, compliance with regulations and mutual information sharing among construction companies will be promoted.

[1484] The following describes the processing flow.

[1485] Specific processing steps of the program

[1486] Example 1: Searching for information on landscape regulations

[1487] Step 1:

[1488] User: Enter "Osaka City's latest landscape ordinance" into the search box on your device and click the search button.

[1489] Step 2:

[1490] Terminal: Receives the search query entered by the user, and uses an emotion engine to acquire the user's emotion at the time of input using an emotion recognition sensor.

[1491] Step 3:

[1492] Terminal: Combines sentiment data and search queries into an HTTP request and sends it to the server.

[1493] Step 4:

[1494] Server: Receives HTTP requests from terminals, parses the request content, and extracts search queries and sentiment data.

[1495] Step 5:

[1496] Server: Searches the "Landscape Regulations" table in the database based on the search query. Uses indexes for efficient searching.

[1497] Step 6:

[1498] Server: Passes search results retrieved from the database to the AI ​​model, which generates necessary supplementary information and annotations. Adjusts the supplementary information according to sentiment data.

[1499] Step 7:

[1500] Server: Combines the generated results and supplementary information to produce the final response in JSON format.

[1501] Step 8:

[1502] Server: Sends the generated JSON response back to the terminal.

[1503] Step 9:

[1504] Terminal: Receives the JSON response sent back from the server and parses the data.

[1505] Step 10:

[1506] Terminal: Displays parsed data on the user interface, allowing users to check information regarding Osaka City's landscape regulations.

[1507] Example 2: Searching for countermeasure examples

[1508] Step 1:

[1509] User: Enter "Examples of landscape measures for commercial buildings in Shibuya Ward" into the search box on your device and click the search button.

[1510] Step 2:

[1511] Terminal: Receives the search query entered by the user and uses the sentiment engine to recognize the user's emotion at the time of input.

[1512] Step 3:

[1513] Terminal: Combines sentiment data and search queries into an HTTP request and sends it to the server.

[1514] Step 4:

[1515] Server: Receives HTTP requests from terminals, parses the request content, and extracts search queries and sentiment data.

[1516] Step 5:

[1517] Server: Based on the search query, it searches the "Case Studies" table in the database. Indexes are used to improve efficiency.

[1518] Step 6:

[1519] Server: Passes search results obtained from the database to a generating AI model, which scores and selects the most suitable countermeasures. Based on sentiment data, it prioritizes selecting cases with high recommendation levels.

[1520] Step 7:

[1521] Server: Combines scored optimal countermeasures with supplementary information to generate the final response in JSON format.

[1522] Step 8:

[1523] Server: Sends the generated JSON response back to the terminal.

[1524] Step 9:

[1525] Terminal: Receives the JSON response sent back from the server and parses the data.

[1526] Step 10:

[1527] Terminal: Displays the parsed data on the user interface, allowing users to review case studies.

[1528] Example 3: Providing product information and purchasing procedures

[1529] Step 1:

[1530] User: Enter "fence for landscape improvement" into the product search box on the terminal and click the search button.

[1531] Step 2:

[1532] Terminal: Receives the search query entered by the user and uses the sentiment engine to recognize the user's emotion at the time of input.

[1533] Step 3:

[1534] Terminal: Combines sentiment data and search queries into an HTTP request and sends it to the server.

[1535] Step 4:

[1536] Server: Receives HTTP requests from terminals, parses the request content, and extracts search queries and sentiment data.

[1537] Step 5:

[1538] Server: Searches the product database based on the search query. Performs filtering based on product category and characteristics.

[1539] Step 6:

[1540] Server: Organizes product information retrieved from the database and prioritizes presenting highly recommended products based on sentiment data.

[1541] Step 7:

[1542] Server: Generates organized product information in JSON format and sends it back to the terminal.

[1543] Step 8:

[1544] Terminal: Receives the JSON response sent back from the server and parses the data.

[1545] Step 9:

[1546] Terminal: Displays parsed data on the user interface, allowing users to view detailed product information.

[1547] Step 10:

[1548] User: Review the displayed product information, add the desired product to your cart, and begin the purchase process.

[1549] Step 11:

[1550] Terminal: Collects information related to the purchase process (user information, payment information, etc.) and sends it to the server as an HTTP request.

[1551] Step 12:

[1552] Server: Receives information regarding the purchase process and processes the payment. Confirms the success of the payment. Based on sentiment data, sends follow-up messages to the user as needed.

[1553] Step 13:

[1554] Server: If the purchase is successful, send a confirmation email to the user and a purchase completion message back to the device.

[1555] Step 14:

[1556] Terminal: Displays the purchase completion message returned from the server on the user interface, notifying the user that the purchase was successful. The emotion engine also monitors the user's emotional state after the purchase and uses this information to improve future services.

[1557] (Example 2)

[1558] 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."

[1559] Traditional systems often failed to consider the user's emotional state when they entered search queries to obtain information, resulting in insufficient information quality. Furthermore, especially when sophisticated information such as legal compliance or landscape protection was required, users could experience stress or confusion due to information overload. The information acquisition and purchasing processes also suffered from frustration due to the lack of consideration for user emotions. Therefore, there is a growing need for systems that recognize user emotions in real time and optimize information provision based on those emotions.

[1560] 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.

[1561] In this invention, the server includes means for the user to input a search query, means for recognizing the user's emotions in real time at the time of input, means for transmitting the input query and emotion information to the server, means for searching a database based on the query and emotion information, means for processing the search results with a generating artificial intelligence model and adding supplementary information based on the emotion information, means for returning the processed results to the user, and means for displaying the returned results on the user interface. This makes it possible to provide optimal information while taking the user's emotions into consideration, improving the user experience and enabling efficient and effective acquisition of information related to legal compliance and landscape measures.

[1562] A "user" refers to a person or organization that uses the system to enter search queries and obtain information.

[1563] "Terminal" refers to a client device used by a user, including devices such as web browsers and mobile applications.

[1564] A "server" refers to a computer system that handles the backend of a system, managing databases, operating AI models, and processing requests.

[1565] A "search query" refers to a question, either in text or other form, that a user enters into their device to obtain specific information.

[1566] An "emotion engine" refers to a system component that recognizes and analyzes a user's emotional state in real time when they enter search queries or browse information.

[1567] An "HTTP request" refers to a form of communication protocol used to send user search queries and other data to a server.

[1568] A "database" refers to an information aggregation system that systematically stores specific information and allows it to be searched and retrieved.

[1569] A "generative artificial intelligence model" refers to a technology that uses pre-trained algorithms to analyze data and generate predictions and supplementary information.

[1570] "JSON format" refers to a lightweight data exchange format for structuring, storing, and exchanging data.

[1571] "User interface" refers to screens and designs that provide visual or interactive elements for users to interact with a system.

[1572] "Search results" refer to the collection of information retrieved from the database based on the entered search query.

[1573] "Supplemental information" refers to additional information added to the main search results, data intended to help users gain a deeper understanding.

[1574] A "case study of countermeasures" refers to a specific example that shows the solutions implemented to address a particular problem, along with their details.

[1575] "Product information" refers to detailed information, specifications, prices, and stock availability of products necessary for landscape improvement measures.

[1576] "Purchase process" refers to the series of steps involved in a user purchasing a selected product online.

[1577] "Following" refers to the act of providing appropriate support or additional information to users based on their emotional state during the purchase or search process.

[1578] This invention provides a system that offers a knowledge base for quickly and efficiently responding to the landscape regulations of each local government. Specifically, it is characterized by the coordinated operation of user, terminal, and server components, and in particular by the inclusion of an emotion engine that recognizes the user's emotions. The following details specific embodiments of this system.

[1579] System Configuration

[1580] This system consists of the following elements:

[1581] 1. User: A person or organization that searches for and views landscape regulations and examples of countermeasures.

[1582] 2. Terminal: This refers to a client device used by the user, including devices such as web browsers and mobile applications. The terminal is equipped with an emotion engine.

[1583] 3. Server: Responsible for the system's backend, including database management, operation of generated AI models, and request processing.

[1584] Hardware and software details

[1585] 1. Emotional Engine:

[1586] Hardware: Cameras and microphones for real-time analysis of the user's facial expressions and voice.

[1587] Software: Facial expression recognition software, voice emotion analysis tool.

[1588] 2. Database:

[1589] Hardware: High-performance data server.

[1590] Software: SQL Database Management System (DBMS).

[1591] 3. Generative AI Models:

[1592] Hardware: Server equipped with a high-performance GPU.

[1593] Software: Deep learning libraries (e.g., TensorFlow, PyTorch).

[1594] System Operation Description

[1595] User actions

[1596] Users access the system using their devices to search for specific landscape regulations and best practices. Specifically, the information retrieval process begins when the user enters a search query into the search box on their device and presses the submit button.

[1597] Terminal role

[1598] The device provides a user interface, including a form for entering search queries and a screen for receiving display results. When a user enters and submits a search query, it is sent to the server as an HTTP request. The device also incorporates an emotion engine that recognizes the user's emotional state in real time. This emotional information is included in the data sent to the server.

[1599] Server Processing

[1600] The server analyzes the search query and sentiment information received from the terminal and searches the database based on this analysis. The database contains landscape regulations from each local government and examples of countermeasures from across the country. The search results are processed by a generative AI model, which suggests supplementary information and optimal countermeasures. The sentiment engine also makes adjustments based on the user's emotions. The server returns these results to the terminal in JSON format.

[1601] Specific operation examples

[1602] Example 1: Searching for information on landscape regulations

[1603] User: Enter "Osaka City's latest landscape ordinance" into the search box on your device.

[1604] Emotion Engine: Recognizes and analyzes the emotions of the user during input.

[1605] Terminal: Receives search queries and sentiment information and sends them to the server as HTTP requests.

[1606] Server: Analyzes queries and sentiment information, retrieves Osaka City's landscape ordinance information from the database, and adds supplementary information.

[1607] Server: Returns the generated results to the terminal in JSON format.

[1608] Terminal: Displays received data in the user interface.

[1609] Example 2: Searching for countermeasure examples

[1610] User: Enter "Examples of landscape measures for commercial buildings in Shibuya Ward" into the search box on your device.

[1611] Emotion Engine: Continuously recognizes the user's emotions as they input.

[1612] Terminal: Receives search queries and sentiment information and sends them to the server as HTTP requests.

[1613] Server: Analyzes queries and sentiment information, and retrieves countermeasure examples from Shibuya Ward in the database. Selects countermeasure examples that have been adjusted by the sentiment engine.

[1614] Server: Returns optimal countermeasure examples to the terminal in JSON format.

[1615] Terminal: Displays received data in the user interface.

[1616] Example of a prompt

[1617] "I want to know the latest Osaka City landscape regulations."

[1618] "Search for examples of landscape improvement measures for commercial buildings in Shibuya Ward."

[1619] "We would like more detailed information about fences used for landscape preservation."

[1620] This allows users to efficiently comply with landscape regulations and easily purchase necessary products. The combination of emotional engines improves the user experience and enables personalized and effective information delivery. It also promotes compliance with regulations by construction companies and facilitates mutual information sharing.

[1621] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1622] Step 1:

[1623] User input:

[1624] The user enters a specific query (e.g., "Osaka City's latest landscape regulations") into the search box on their device and clicks the submit button.

[1625] Input: The search query entered by the user.

[1626] Output: Click event of the submit button.

[1627] Specific operation: Enter text into the search box and click the submit button. During this process, the emotion engine analyzes the user's facial expressions and voice in real time to obtain emotional information.

[1628] Step 2:

[1629] Generating a search request on the device:

[1630] The terminal sends the user's input query and analyzed sentiment information to the server as an HTTP request.

[1631] Input: User-entered search queries and sentiment information analyzed by the sentiment engine.

[1632] Output: HTTP request.

[1633] Specific operation: Structures search queries and sentiment information entered within the terminal and generates them as HTTP requests.

[1634] Step 3:

[1635] Query parsing on the server:

[1636] The server parses the received HTTP request, extracts the query content and sentiment information, and then analyzes it.

[1637] Input: HTTP request (including search query and sentiment information).

[1638] Output: Analyzed query information and sentiment information.

[1639] Specific actions: The query content is parsed to extract key information for searching. Simultaneously, sentiment information is analyzed to understand the user's current emotional state.

[1640] Step 4:

[1641] Database search and AI model usage:

[1642] The server searches the database based on the query and uses a generative AI model to retrieve and analyze relevant information.

[1643] Input: Analyzed query information and sentiment information.

[1644] Output: Relevant information and analysis results retrieved from the database.

[1645] Specific operations: Access the database, search for and retrieve data related to the query. Analyze the retrieved data using a generative AI model and select the information to provide to the user.

[1646] Step 5:

[1647] Search result generation and sentiment engine optimization:

[1648] The server uses an emotion engine to adjust search results based on the user's emotional information and adds supplementary information.

[1649] Input: Relevant information and analysis results obtained from the database, and user sentiment information.

[1650] Output: Search results with adjustments and supplementary information added.

[1651] Specific operation: Based on data analyzed by the generative AI model, necessary supplementary information is generated, and the emotion engine adds information according to the user's emotional state.

[1652] Step 6:

[1653] Returning search results:

[1654] The server returns the adjusted search results to the terminal in JSON format.

[1655] Input: Search results with adjustments and supplementary information added.

[1656] Output: Search results encoded in JSON format.

[1657] Specific operation: Encode the generated search results into JSON format and send them to the terminal as an HTTP response.

[1658] Step 7:

[1659] Displaying results on the device:

[1660] The terminal parses the received JSON data and displays it on the user interface.

[1661] Input: Search results in JSON format.

[1662] Output: Search results displayed on the user interface.

[1663] Specific operation: Parses JSON data and displays it in a user-friendly format. Provides information to the user, such as highlighting particularly important sections. The sentiment engine continues to analyze the user's emotions at this point and dynamically adjusts the displayed content as needed.

[1664] Step 8:

[1665] User feedback:

[1666] The user reviews the displayed search results and then takes the next action.

[1667] Input: The displayed search results.

[1668] Output: Input for the next query and other operations.

[1669] Specific actions: The system reviews the information provided by the user and asks further questions if there are any unclear points. Additionally, the sentiment engine records the user's emotional state to help with future searches.

[1670] (Application Example 2)

[1671] 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."

[1672] The conventional system lacked a sufficient knowledge base to respond quickly and efficiently to the landscape regulations and countermeasures of each local government, and was unable to address the emotions of users. Furthermore, in material selection and design proposals at the factory site, appropriate support that took into account the emotions of operators was not provided, resulting in decreased work efficiency.

[1673] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for the user to input a search query, means for sending the input query to the server, means for searching the database based on the query, means for processing the search results with a generating AI model and adding supplementary information, means for returning the processed results to the user, means for displaying the returned results on the user interface, and means for recognizing the user's emotions using an emotion engine and adjusting the search results and displayed content based on those emotions. This makes it possible to select the optimal materials and propose designs that correspond to the user's emotions.

[1674] A "user" is a person or operator who uses this system to enter search queries and receive the results.

[1675] A "search query" is a question or keyword that a user enters to obtain specific information.

[1676] A "server" is a computer system that handles the backend of a system, managing databases, operating AI models, and processing requests.

[1677] A "database" is a collection of information that stores landscape regulations, countermeasures, and product information from various local governments.

[1678] A "generative AI model" is an artificial intelligence algorithm that processes information corresponding to search queries and suggests supplementary information and optimal solutions.

[1679] "Supplemental information" refers to related information and reference materials added to the search results.

[1680] A "user interface" refers to the screens and means of operation that a user uses to interact with a system.

[1681] An "emotion engine" is emotion recognition software that recognizes a user's emotions and adjusts search results and displayed content accordingly.

[1682] A "landscape ordinance" is a set of rules and policies established by each local government regarding the protection of the landscape.

[1683] A "case study of countermeasures" is a specific example of a method or approach for solving a particular problem.

[1684] "Product information" refers to detailed data about a product, such as specifications, price, and stock availability.

[1685] The "purchase process" is the process by which a user buys a selected product online.

[1686] This invention provides a system that offers a knowledge base for quickly and efficiently responding to the landscape regulations of each local government. A detailed description of the embodiments for carrying out the invention is given below.

[1687] System components

[1688] This system consists of the following elements:

[1689] 1. User: A factory operator who uses the system to search and view landscape regulations and examples of countermeasures.

[1690] 2. Terminal: A client device used by the user, including touch panels and voice input devices.

[1691] 3. Server: A computer system that handles the backend of the system, managing databases, operating AI models, and processing requests.

[1692] 4. Emotion Engine: A component that recognizes the user's emotions when entering search queries or viewing information, and adjusts search results and displayed content accordingly.

[1693] System operation

[1694] User actions

[1695] Users access the system using a terminal to search for specific landscape regulations and optimal solutions. For example, if a factory operator wants to search for "eco-friendly materials," they would type the search term into the touch panel and begin the search. During this process, the user's emotions (positive or negative) are recognized and analyzed by an emotion engine.

[1696] Terminal role

[1697] The device provides a user interface, offering forms for entering search queries and screens for receiving display results. The device also incorporates an emotion engine that recognizes the user's emotional state in real time.

[1698] Server Processing

[1699] The server analyzes the search query received from the terminal and searches the database based on it. The database contains landscape regulations and countermeasures examples from each local government. The search results are processed by a generative AI model, which suggests supplementary information and optimal countermeasures. The server returns these results to the terminal in JSON format. The results also include adjustments made by an emotion engine.

[1700] Specific operation examples

[1701] Example 1: Searching for information on landscape regulations

[1702] User: Enter "Osaka City's latest landscape ordinance" into the search box on your device.

[1703] Emotion Engine: Recognizes and analyzes the emotions of the user during input.

[1704] Terminal: Receives search queries and sends them to the server.

[1705] Server: Analyzes search queries, searches the database to retrieve information on Osaka City's landscape regulations. A sentiment engine generates supplementary information tailored to the user's emotions.

[1706] Server: Returns the generated results to the terminal in JSON format.

[1707] Terminal: Displays received data on the user interface, allowing users to check information regarding Osaka City's landscape regulations.

[1708] Example 2: Searching for countermeasure examples

[1709] User: Enter "Examples of landscape measures for commercial buildings in Shibuya Ward" into the search box on your device.

[1710] Emotion Engine: Recognizes and analyzes the user's emotions at the time of input. It also monitors the user's emotional state after the search query has been submitted.

[1711] Terminal: Receives search queries and sends them to the server.

[1712] Server: Analyzes search queries, searches the database to retrieve case studies for Shibuya Ward. An emotion engine scores and selects case studies based on the user's emotions.

[1713] Server: Generates optimal countermeasure examples and supplementary information, and sends them back to the terminal.

[1714] Terminal: Displays received data on the user interface, allowing users to review examples of countermeasures.

[1715] Example 3: Providing product information and purchasing procedures

[1716] User: Enter "fence for landscape protection" into the search box on your device.

[1717] Emotion Engine: Recognizes and analyzes the user's emotions at the time of input. It also monitors the user's emotional state after the search query has been submitted.

[1718] Terminal: Receives search queries and sends them to the server.

[1719] Server: Analyzes search queries and searches the product database. The sentiment engine provides product information tailored to the user's emotions.

[1720] Server: Generates organized product information and sends it back to the terminal.

[1721] Terminal: Displays received data on the user interface, allowing users to view detailed product information.

[1722] User: Review the displayed product information, add the desired product to your cart, and begin the purchase process.

[1723] Terminal: Collects information related to the purchase process and sends it to the server.

[1724] Server: Receives information regarding the purchase process and processes the payment. The emotion engine monitors the user's emotions during the payment process and sends follow-up messages as needed.

[1725] Server: If the purchase is successful, send a confirmation email to the user and a purchase completion message back to the device.

[1726] Terminal: Displays a purchase completion message on the user interface to inform the user that the purchase was successful. The emotion engine also analyzes the user's emotions after the purchase to help with future purchases.

[1727] Examples of prompts to input into a generative AI model

[1728] "Search for eco-friendly materials and suggest them to the operator. The operator's current sentiment is positive. Please suggest the best materials and explain why."

[1729] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1730] Step 1:

[1731] The user enters a search query. Specifically, a factory operator uses the terminal's touch panel or voice input device to enter a query seeking specific information. This input is stored on the terminal as text data.

[1732] Step 2:

[1733] The terminal sends the entered query to the server. This is done using an HTTP request. The entered query text is included in the body of the HTTP request and sent to the specified endpoint on the server.

[1734] Step 3:

[1735] The server parses the search queries it receives. Specifically, it analyzes the received query text using a natural language processing engine and extracts its meaning. This analysis result is then used as a condition for database searches.

[1736] Step 4:

[1737] The server searches the database based on the query. Based on the analysis results, it creates an SQL query and retrieves relevant information (landscape regulations, countermeasure examples, product information, etc.) from the database. The retrieved information is stored in an internal database.

[1738] Step 5:

[1739] The server processes the search results using a generative AI model and adds supplementary information. Specifically, the search results are input into the generative AI model, and relevant supplementary information and further suggestions are obtained as output. This output includes the information the user is looking for, along with related supplementary information.

[1740] Step 6:

[1741] The server sends input queries and user sentiment data to the sentiment engine, which then adjusts the search results and displayed content. The sentiment engine analyzes the user's sentiment data input and adjusts the priority and display format of search results based on that analysis.

[1742] Step 7:

[1743] The server converts the processed results into JSON format and sends them back to the terminal. The generated search results, supplementary information, and data including the sentiment engine adjustment results are encoded in JSON format and sent back as an HTTP response.

[1744] Step 8:

[1745] The system receives the results of the device's return and displays them on the user interface. It parses the received JSON data and displays it in a user-friendly format (text, graphs, images, etc.). The user can then review this information and decide on their next action (e.g., additional search or purchase).

[1746] 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.

[1747] 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.

[1748] 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.

[1749] [Fourth Embodiment]

[1750] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1751] 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.

[1752] 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).

[1753] 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.

[1754] 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.

[1755] 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).

[1756] 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.

[1757] 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.

[1758] 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.

[1759] 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.

[1760] 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.

[1761] 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.

[1762] 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".

[1763] This invention provides a system that offers a knowledge base for quickly and efficiently responding to the landscape regulations of each local government. This system operates through the coordinated efforts of user, terminal, and server components.

[1764] System Overview

[1765] This system consists of the following elements:

[1766] 1. User: A representative from a construction company who uses the system to search and view landscape regulations and examples of countermeasures.

[1767] 2. Terminal: A client device used by the user, including web browsers and mobile applications.

[1768] 3. Server: A computer system that handles the backend of the system, managing databases, operating AI models, and processing requests.

[1769] System operation

[1770] User actions

[1771] Users access the system using their devices to search for specific landscape regulations and best practices. The information retrieval process begins when the user enters a search query into the search box on their device and presses the submit button.

[1772] Terminal role

[1773] The terminal provides a user interface, including a form for entering search queries and a screen for receiving display results. When a user enters and submits a search query, that query is sent to the server as an HTTP request.

[1774] Server Processing

[1775] The server analyzes the search query received from the terminal and searches the database based on it. The database contains landscape regulations from each local government and examples of countermeasures from across the country. The search results are processed by a generative AI model, which suggests supplementary information and optimal countermeasures. The server returns these results to the terminal in JSON format.

[1776] Specific operation examples

[1777] Example 1: Searching for information on landscape regulations

[1778] User: Enter "Osaka City's latest landscape ordinance" into the search box on your device.

[1779] Terminal: Receives search queries and sends them to the server as HTTP requests.

[1780] Server: Analyzes search queries, searches the database to retrieve Osaka City's landscape ordinance information, and adds supplementary information using a generative AI model. The results are returned to the terminal in JSON format.

[1781] Terminal: Displays received data on the user interface, allowing users to check information regarding Osaka City's landscape regulations.

[1782] Example 2: Searching for countermeasure examples

[1783] User: Enter "Examples of landscape measures for commercial buildings in Shibuya Ward" into the search box on your device.

[1784] Terminal: Receives search queries and sends them to the server as HTTP requests.

[1785] Server: Analyzes search queries, searches the database to retrieve case studies for Shibuya Ward, and selects the most suitable case using a generative AI model. The results are returned to the terminal in JSON format.

[1786] Terminal: Displays received data on the user interface, allowing users to view examples of optimal commercial building landscape measures in Shibuya Ward.

[1787] Other features

[1788] This system also offers the following features:

[1789] Product Information Provision: We provide detailed information (specifications, price, stock availability, etc.) on products necessary for landscape improvement measures.

[1790] Purchase Procedure: Manages the process for users to purchase selected products online.

[1791] This allows users to efficiently comply with landscape regulations and easily purchase necessary products. Implementing this invention promotes legal compliance among construction companies and facilitates mutual information sharing.

[1792] The following describes the processing flow.

[1793] Specific processing steps of the program

[1794] Example 1: Searching for information on landscape regulations

[1795] Step 1:

[1796] User: Enter "Osaka City's latest landscape ordinance" into the search box on your device and click the search button.

[1797] Step 2:

[1798] Terminal: Receives the search query entered by the user and sends it to the server as an HTTP request.

[1799] Step 3:

[1800] Server: Receives HTTP requests from terminals, parses the request content, and extracts the search query.

[1801] Step 4:

[1802] Server: Searches the "Landscape Regulations" table in the database based on the search query. Uses indexes for efficient searching.

[1803] Step 5:

[1804] Server: Passes search results retrieved from the database to the AI ​​model, which then generates necessary supplementary information and annotations.

[1805] Step 6:

[1806] Server: Combines the generated results with the original search results to produce the final response in JSON format.

[1807] Step 7:

[1808] Server: Sends the generated JSON response back to the terminal.

[1809] Step 8:

[1810] Terminal: Receives the JSON response sent back from the server and parses the data.

[1811] Step 9:

[1812] Terminal: Displays the parsed data on the user interface, allowing the user to see the information they need.

[1813] Example 2: Searching for countermeasure examples

[1814] Step 1:

[1815] User: Enter "Examples of landscape measures for commercial buildings in Shibuya Ward" into the search box on your device and click the search button.

[1816] Step 2:

[1817] Terminal: Receives the search query entered by the user and sends it to the server as an HTTP request.

[1818] Step 3:

[1819] Server: Receives HTTP requests from terminals, parses the request content, and extracts the search query.

[1820] Step 4:

[1821] Server: Based on the search query, it searches the "Case Studies" table in the database. Indexes are used to improve efficiency.

[1822] Step 5:

[1823] Server: The server passes search results obtained from the database to an AI model that scores and selects the most suitable countermeasures.

[1824] Step 6:

[1825] Server: Combines scored optimal countermeasures and supplementary information to generate the final response in JSON format.

[1826] Step 7:

[1827] Server: Sends the generated JSON response back to the terminal.

[1828] Step 8:

[1829] Terminal: Receives the JSON response sent back from the server and parses the data.

[1830] Step 9:

[1831] Terminal: Displays the parsed data on the user interface, allowing users to review case studies.

[1832] Example 3: Providing product information and purchasing procedures

[1833] Step 1:

[1834] User: Enter "fence for landscape improvement" into the product search box on the terminal and click the search button.

[1835] Step 2:

[1836] Terminal: Receives the search query entered by the user and sends it to the server as an HTTP request.

[1837] Step 3:

[1838] Server: Receives HTTP requests from terminals, parses the request content, and extracts the search query.

[1839] Step 4:

[1840] Server: Searches the product database based on the search query. Performs filtering based on product category and characteristics.

[1841] Step 5:

[1842] Server: Organizes product information retrieved from the database and prepares detailed information such as specifications, price, and stock status.

[1843] Step 6:

[1844] Server: Generates organized product information in JSON format and sends it back to the terminal.

[1845] Step 7:

[1846] Terminal: Receives the JSON response sent back from the server and parses the data.

[1847] Step 8:

[1848] Terminal: Displays parsed data on the user interface, allowing users to view detailed product information.

[1849] Step 9:

[1850] User: Review the displayed product information, add the desired product to your cart, and begin the purchase process.

[1851] Step 10:

[1852] Terminal: Collects information related to the purchase process (user information, payment information, etc.) and sends it to the server as an HTTP request.

[1853] Step 11:

[1854] Server: Receives information regarding the purchase procedure and processes the payment. Confirms the success of the payment.

[1855] Step 12:

[1856] Server: If the purchase is successful, send a confirmation email to the user and a purchase completion message back to the device.

[1857] Step 13:

[1858] Terminal: Displays the purchase completion message returned from the server on the user interface, notifying the user that the purchase was successful.

[1859] (Example 1)

[1860] 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".

[1861] In recent years, there has been a growing need to respond quickly and efficiently to the landscape regulations of various local governments. However, for many construction company personnel, collecting information on these regulations and taking appropriate measures is a very time-consuming and laborious task. Furthermore, obtaining appropriate product information and smoothly carrying out the purchase process is also difficult. The present invention aims to solve these problems and provide a system that can respond to landscape regulations efficiently and quickly.

[1862] 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.

[1863] In this invention, the server includes means for selecting and presenting the most suitable countermeasures from the search results, means for providing product information necessary for landscape countermeasures and managing the purchase procedure, and means for returning the processed results to the user. This allows the user to efficiently comply with landscape regulations and easily purchase the necessary products.

[1864] A "user" is a representative from a construction company whose role is to use the system to search for and view landscape regulations and examples of countermeasures.

[1865] A "terminal" refers to a client device used by a user, including web browsers and mobile applications.

[1866] A "server" is a computer system that handles the backend of a system, managing databases, operating generated AI models, and processing requests.

[1867] A "search query" is a string of characters or a phrase that a user enters into their device to seek specific information.

[1868] An "HTTP request" is a type of communication sent from a client device (terminal) to a server requesting a specific operation or data.

[1869] A "database" is a collection of information that stores landscape regulations and examples of countermeasures from various local governments.

[1870] A "generative AI model" is an artificial intelligence model that operates on a server and generates supplementary information and optimal countermeasures based on search results.

[1871] JSON format is a standard format for representing data in text format, and it is a data exchange format that has the structure of objects and arrays.

[1872] A "user interface" refers to the screen or interface that a user uses to operate a device, input information, or view displayed results.

[1873] A "landscape ordinance" is a set of laws and regulations enacted by a local government for the purpose of protecting or improving the landscape of the area.

[1874] An "example of countermeasure" is an example of a specific countermeasure that was taken in the past to address a particular situation or problem.

[1875] "Product information" refers to detailed information about products necessary for landscape improvement measures, including specifications, price, and stock availability.

[1876] The "purchase process" refers to the series of tasks and management processes involved in a user purchasing a selected product online.

[1877] This invention provides a system that offers a knowledge base for quickly and efficiently responding to the landscape regulations of each local government. This system operates through the coordinated action of multiple components, including user terminals, servers, and generative AI models.

[1878] 1. User actions

[1879] Users access the system through their devices to search for and view specific landscape regulations and examples of countermeasures. The system utilizes web browsers and mobile applications, allowing users to enter search queries into a search box and initiate the information retrieval process by pressing a submit button.

[1880] 2. The role of the terminal

[1881] The terminal receives search queries from the user and sends them to the server as HTTP requests. The user interface also provides a screen for displaying search results and supports the input and display of information. Specifically, the user enters search queries such as "Osaka City's latest landscape ordinance" or "Examples of landscape measures for commercial buildings in Shibuya Ward."

[1882] 3. Server processing

[1883] The server analyzes the search query received from the terminal and searches the database based on it. This database contains landscape regulations from each local government and examples of countermeasures from across the country. The search results are processed by a generative AI model running on the server, which suggests supplementary information and optimal countermeasures. This transforms the results into more useful and specific information. The generated results are converted into JSON format and sent back to the terminal.

[1884] 4. Processing of Generative AI Models

[1885] The generation AI model is installed on the server and is responsible for generating supplementary information and optimal solutions based on search results. This model utilizes user search queries and information obtained from the database to perform advanced data processing and calculations. For example, it proposes optimal solutions for "Examples of landscape improvement measures for commercial buildings in Shibuya Ward."

[1886] Specific operation examples

[1887] 1. Search for information on landscape regulations

[1888] - User: Enter "Osaka City's latest landscape ordinance" into the search box on your device.

[1889] - Terminal: Receives search queries and sends them to the server as HTTP requests.

[1890] - Server: Analyzes search queries, searches the database to retrieve Osaka City's landscape ordinance information, and adds supplementary information using a generative AI model. The results are returned to the terminal in JSON format.

[1891] - Terminal: Displays received data on the user interface, allowing users to check information regarding Osaka City's landscape regulations.

[1892] 2. Search for examples of countermeasures

[1893] - User: Enter "Examples of landscape measures for commercial buildings in Shibuya Ward" into the search box on your device.

[1894] - Terminal: Receives search queries and sends them to the server as HTTP requests.

[1895] - Server: Analyzes search queries, searches the database to retrieve countermeasure examples for Shibuya Ward, and selects the most suitable examples using a generative AI model. The results are returned to the terminal in JSON format.

[1896] - Terminal: Displays received data on the user interface, allowing users to view examples of optimal commercial building landscape measures in Shibuya Ward.

[1897] Examples of prompt statements

[1898] "Could you please tell me about the latest landscape regulations in Osaka City?"

[1899] "Please show us examples of landscape design measures for commercial buildings in Shibuya Ward."

[1900] By implementing this invention, users will be able to efficiently comply with landscape regulations and easily purchase necessary products. The system structure and the coordination of each component will promote compliance with regulations and information sharing among construction companies.

[1901] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1902] Step 1:

[1903] The user enters a search query into the search box on their device and presses the submit button.

[1904] Specific actions:

[1905] The user enters a specific search query into their device, such as "Osaka City's latest landscape ordinance."

[1906] Input: A search query such as "Osaka City's latest landscape ordinance".

[1907] Output: The query is generated as an HTTP request when the submit button is pressed.

[1908] Step 2:

[1909] The terminal receives user input and sends it to the server as an HTTP request.

[1910] Specific actions:

[1911] The terminal generates an HTTP request for the entered search query "Osaka City's latest landscape ordinance" and sends it to the server.

[1912] Input: The search query entered by the user.

[1913] Output: Data sent to the server as an HTTP request.

[1914] Step 3:

[1915] The server parses the HTTP request received from the terminal and extracts the query.

[1916] Specific actions:

[1917] The server receives the HTTP request, analyzes its contents, and extracts the search query "Osaka City's latest landscape ordinance."

[1918] Input: HTTP request.

[1919] Output: Analyzed search query.

[1920] Step 4:

[1921] The server searches the database based on the analyzed query.

[1922] Specific actions:

[1923] The server generates an SQL query against the database based on the search query "Osaka City's latest landscape ordinance" and executes the search.

[1924] Input: The analyzed search query.

[1925] Output: Relevant information retrieved from the database.

[1926] Step 5:

[1927] The server inputs information retrieved from the database into an AI model, which then generates supplementary information.

[1928] Specific actions:

[1929] The server inputs information retrieved from the database into an AI model to generate supplementary information and optimal countermeasures.

[1930] Input: Information retrieved from the database.

[1931] Output: Generated results including supplementary information and optimal solutions.

[1932] Step 6:

[1933] The server converts the generated results into JSON format and sends them back to the terminal.

[1934] Specific actions:

[1935] The server converts the results obtained from the generated AI model into JSON format and sends it to the terminal as an HTTP response.

[1936] Input: Results obtained from a generative AI model.

[1937] Output: Data converted to JSON format.

[1938] Step 7:

[1939] The terminal parses the received JSON data and displays it in the user interface.

[1940] Specific actions:

[1941] The terminal parses the JSON data received from the server and displays it on the user interface as information about "Osaka City's latest landscape regulations."

[1942] Input: Data in JSON format.

[1943] Output: Search results displayed in the user interface.

[1944] The above outlines the system's program processing flow. This flow allows users to quickly and efficiently obtain the information they need.

[1945] (Application Example 1)

[1946] 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".

[1947] Conventional landscape regulation compliance systems required manual information retrieval and evaluation, which was inefficient. Especially in large-scale facilities such as factories, real-time evaluation and proposal of appropriate countermeasures are required. Furthermore, quickly determining compliance with the latest landscape regulations was difficult, resulting in significant effort to maintain compliance. This invention aims to solve these problems and provide a system that enables factory robots to respond to landscape regulations quickly and efficiently.

[1948] 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.

[1949] In this invention, the server includes means for the user to input a search query, means for sending the input query to the server, means for searching a database based on the query, means for processing the search results with a generating AI model and adding supplementary information, means for returning the processed results to the user, means for displaying the returned results on a user interface, means for acquiring images for landscape evaluation, and means for performing an evaluation based on the acquired images and regulatory data. This makes it possible to evaluate in real time whether a factory robot complies with landscape regulations and to quickly propose the optimal countermeasures.

[1950] "User" refers to individuals or corporate representatives who use the system to search for and view landscape regulations and countermeasures.

[1951] A "search query" refers to a series of words or sentences that a user enters into a system to search for specific information.

[1952] A "server" refers to a computer system that manages databases, operates generated AI models, and processes requests.

[1953] A "database" refers to a collection of information that stores landscape regulations from various local governments and examples of countermeasures from across the country.

[1954] A "generative AI model" refers to an artificial intelligence model used to process search data and suggest supplementary information and optimal solutions.

[1955] A "knowledge base" refers to a collection of information that systematically organizes knowledge about a particular field.

[1956] A "landscape ordinance" refers to the rules and standards established by each local government to protect beautiful environments and landscapes.

[1957] "Factory robots" refer to robots used to automate various tasks within a factory.

[1958] "User interface" refers to the display screens and input devices that users use to interact with a system.

[1959] "Evaluation" refers to the act of judging a value or state based on specific standards or rules.

[1960] "Examples of countermeasures" refer to specific examples and methods of landscape improvement measures implemented in the past.

[1961] This invention provides a system that offers a knowledge base for quickly and efficiently responding to the landscape regulations of each local government. This system operates through the coordinated efforts of user, terminal, and server components. Its specific configuration is described below.

[1962] System Configuration

[1963] The system consists of the following elements:

[1964] 1. User: A representative from a construction company who uses the system to search and view landscape regulations and examples of countermeasures.

[1965] 2. Terminal: A client device used by the user, including web browsers and mobile applications, as well as cameras and sensors installed on factory robots.

[1966] 3. Server: A computer system that handles the backend of the system, managing databases, operating generated AI models, and processing requests.

[1967] Server Role

[1968] The server performs the following actions:

[1969] The system receives search queries from users, parses those queries, and searches the database.

[1970] The search results are processed using an AI model, and supplementary information and optimal solutions are suggested.

[1971] The processed results are returned to the user in JSON format.

[1972] Terminal role

[1973] The terminal performs the following actions:

[1974] It provides a user interface, offering forms for entering search queries and screens for receiving display results.

[1975] Cameras and sensors scan the interior and exterior of the factory, and the captured images are sent to a server.

[1976] Display the results returned from the server and make them available for users to view.

[1977] User roles

[1978] The user performs the following actions:

[1979] Access the system and enter a search query to find specific landscape regulations or best practice solutions.

[1980] Operate factory robots to acquire necessary images using cameras and sensors.

[1981] Review the evaluation results and proposed countermeasures returned from the server and implement them.

[1982] Hardware and software to be used

[1983] Hardware: Camera devices, sensors, terminals (PCs, smartphones, tablets), servers

[1984] Software: OpenCV (image processing library), requests (HTTP request library), generative AI models (TensorFlow and PyTorch as AI model frameworks)

[1985] Specific example

[1986] For example, consider a scenario where a factory in Tokyo checks whether a newly installed sign complies with the latest landscape regulations. The user operates a factory robot to capture images of the sign with a camera and sends the images to a server. The generated AI model evaluates the acquired images and the latest Tokyo landscape regulations data, and proposes appropriate countermeasures. The following prompt statements are used as an example of system operation:

[1987] "Please evaluate whether the signage at this factory is appropriate based on the latest Shinjuku Ward landscape regulations. Also, please provide suggestions for improvement if necessary."

[1988] This makes it possible to evaluate in real time whether factory robots are complying with landscape regulations and to quickly propose the most suitable countermeasures.

[1989] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1990] Step 1:

[1991] The user enters the search query through the device's user interface. Specifically, the user uses a web browser or mobile application to enter a search query such as "Shinjuku Ward's latest landscape regulations." The data entered is the search query (text), which is then used for the next step.

[1992] Step 2:

[1993] The terminal sends the entered search query to the server. The terminal generates an HTTP request and includes the search query as the payload. This request is sent to the server, which receives the search query to use for analysis.

[1994] Step 3:

[1995] The server parses the received search query and searches the database. The server then accesses the database and generates and executes an SQL query to retrieve the relevant municipal landscape regulations and related information. The data retrieved from the database includes the text of the regulations and related information.

[1996] Step 4:

[1997] The server uses a generative AI model to process the retrieved search results and add supplementary information and suggested actions. The generative AI model takes the ordinance text retrieved from the database as input and generates additional interpretations and appropriate suggested actions. The output consists of search results with supplementary information and suggested actions.

[1998] Step 5:

[1999] The server converts the processed results into JSON format and sends them back to the terminal. Here, the server appropriately formats the generated supplementary information and proposed solutions and sends them to the terminal as an HTTP response. This response becomes the input data for the next step.

[2000] Step 6:

[2001] The terminal receives the results sent back from the server and displays them in the user interface. The terminal parses the received JSON data, converts it into a user-friendly format, and displays it on the screen.

[2002] Step 7:

[2003] The user operates a factory robot and acquires images of the landscape using a camera. The user uses the robot to scan the local landscape and capture the necessary image data with the camera. The acquired data is in the form of image files.

[2004] Step 8:

[2005] The device sends the acquired image data to the server. The device includes the image file as the payload of an HTTP request and sends it to the server. The transmitted image data becomes the input for the next step.

[2006] Step 9:

[2007] The server performs an evaluation based on image data and regulatory data. The server uses a generative AI model, receiving the transmitted image data and the previously acquired regulatory data as input. The AI ​​model analyzes these and evaluates whether it complies with the landscape regulations. The output generates the evaluation results and necessary countermeasures.

[2008] Step 10:

[2009] The server converts the evaluation results and proposed countermeasures into JSON format and sends them back to the terminal. Here, the server appropriately formats the data containing the evaluation results and generated countermeasures and sends it to the terminal as an HTTP response.

[2010] Step 11:

[2011] The terminal receives the evaluation results sent back from the server and displays them on the user interface. The terminal parses the received JSON data and displays it for the user to review. Based on this, the user can take appropriate action.

[2012] 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.

[2013] This invention provides a system that offers a knowledge base for quickly and efficiently responding to the landscape regulations of each local government. This system features a user, terminal, and server component working in coordination, and is particularly characterized by its inclusion of an emotion engine that recognizes the user's emotions.

[2014] System Overview

[2015] This system consists of the following elements:

[2016] 1. User: A representative from a construction company who uses the system to search and view landscape regulations and examples of countermeasures.

[2017] 2. Terminal: A client device used by the user, including web browsers and mobile applications.

[2018] 3. Server: A computer system that handles the backend of the system, managing databases, operating AI models, and processing requests.

[2019] 4. Emotion Engine: A component that recognizes the user's emotions when entering search queries or viewing information, and adjusts search results and displayed content accordingly.

[2020] System operation

[2021] User actions

[2022] Users access the system using their devices to search for specific landscape regulations and best practices. The information retrieval process begins when the user enters a search query into the search box on their device and presses the submit button.

[2023] Terminal role

[2024] The device provides a user interface, offering forms for entering search queries and screens for receiving display results. When a user enters and submits a search query, it is sent to the server as an HTTP request. The device is equipped with an emotion engine that recognizes the user's emotional state in real time.

[2025] Server Processing

[2026] The server analyzes the search query received from the terminal and searches the database based on it. The database contains landscape regulations from each local government and examples of countermeasures from across the country. The search results are processed by a generative AI model, which suggests supplementary information and optimal countermeasures. The server returns these results to the terminal in JSON format. The results also include adjustments made by an emotion engine.

[2027] Specific operation examples

[2028] Example 1: Searching for information on landscape regulations

[2029] User: Enter "Osaka City's latest landscape ordinance" into the search box on your device.

[2030] Emotion Engine: Recognizes and analyzes the emotions of the user during input.

[2031] Terminal: Receives search queries and sends them to the server as HTTP requests.

[2032] Server: Analyzes search queries, searches the database to retrieve information on Osaka City's landscape regulations. A sentiment engine generates supplementary information tailored to the user's emotions.

[2033] Server: Returns the generated results to the terminal in JSON format.

[2034] Terminal: Displays received data on the user interface, allowing users to check information regarding Osaka City's landscape regulations.

[2035] Example 2: Searching for countermeasure examples

[2036] User: Enter "Examples of landscape measures for commercial buildings in Shibuya Ward" into the search box on your device.

[2037] Emotion Engine: Recognizes and analyzes the user's emotions at the time of input. It also monitors the user's emotional state after the search query has been submitted.

[2038] Terminal: Receives search queries and sends them to the server as HTTP requests.

[2039] Server: Analyzes search queries, searches the database to retrieve case studies for Shibuya Ward. An emotion engine scores and selects case studies based on the user's emotions.

[2040] Server: Generates optimal countermeasure examples and supplementary information, and sends them back to the terminal in JSON format.

[2041] Terminal: Displays received data on the user interface, allowing users to review examples of countermeasures.

[2042] Example 3: Providing product information and purchasing procedures

[2043] User: Enter "fence for landscape improvement" into the product search box on the device.

[2044] Emotion Engine: Recognizes and analyzes the user's emotions at the time of input. It also monitors the user's emotional state after the search query has been submitted.

[2045] Terminal: Receives search queries and sends them to the server as HTTP requests.

[2046] Server: Analyzes search queries and searches the product database. The sentiment engine provides product information tailored to the user's emotions.

[2047] Server: Generates organized product information in JSON format and sends it back to the terminal.

[2048] Terminal: Displays received data on the user interface, allowing users to view detailed product information.

[2049] User: Review the displayed product information, add the desired product to your cart, and begin the purchase process.

[2050] Terminal: Collects information related to the purchase process and sends it to the server as an HTTP request.

[2051] Server: Receives information regarding the purchase process and processes the payment. The emotion engine monitors the user's emotions during the payment process and sends follow-up messages as needed.

[2052] Server: If the purchase is successful, send a confirmation email to the user and a purchase completion message back to the device.

[2053] Terminal: Displays a purchase completion message on the user interface to inform the user that the purchase was successful. The emotion engine also analyzes the user's emotions after the purchase to help with future purchases.

[2054] Other features

[2055] This system also offers the following features:

[2056] Emotional Engine: Adjusts search results and displayed content based on user emotions. Also recommends important solutions and appropriate products.

[2057] Information provided: We provide detailed information (specifications, price, stock availability, etc.) on products necessary for landscape improvement measures.

[2058] Purchase Process: Manage the process for users to purchase selected products online and provide support tailored to their emotional needs.

[2059] This allows users to efficiently comply with landscape regulations and easily purchase necessary products. The combination with the emotion engine improves the user experience and enables more personalized and effective information delivery. By implementing this invention, compliance with regulations and mutual information sharing among construction companies will be promoted.

[2060] The following describes the processing flow.

[2061] Specific processing steps of the program

[2062] Example 1: Searching for information on landscape regulations

[2063] Step 1:

[2064] User: Enter "Osaka City's latest landscape ordinance" into the search box on your device and click the search button.

[2065] Step 2:

[2066] Terminal: Receives the search query entered by the user, and uses an emotion engine to acquire the user's emotion at the time of input using an emotion recognition sensor.

[2067] Step 3:

[2068] Terminal: Combines sentiment data and search queries into an HTTP request and sends it to the server.

[2069] Step 4:

[2070] Server: Receives HTTP requests from terminals, parses the request content, and extracts search queries and sentiment data.

[2071] Step 5:

[2072] Server: Searches the "Landscape Regulations" table in the database based on the search query. Uses indexes for efficient searching.

[2073] Step 6:

[2074] Server: Passes search results retrieved from the database to the AI ​​model, which generates necessary supplementary information and annotations. Adjusts the supplementary information according to sentiment data.

[2075] Step 7:

[2076] Server: Combines the generated results and supplementary information to produce the final response in JSON format.

[2077] Step 8:

[2078] Server: Sends the generated JSON response back to the terminal.

[2079] Step 9:

[2080] Terminal: Receives the JSON response sent back from the server and parses the data.

[2081] Step 10:

[2082] Terminal: Displays parsed data on the user interface, allowing users to check information regarding Osaka City's landscape regulations.

[2083] Example 2: Searching for countermeasure examples

[2084] Step 1:

[2085] User: Enter "Examples of landscape measures for commercial buildings in Shibuya Ward" into the search box on your device and click the search button.

[2086] Step 2:

[2087] Terminal: Receives the search query entered by the user and uses the sentiment engine to recognize the user's emotion at the time of input.

[2088] Step 3:

[2089] Terminal: Combines sentiment data and search queries into an HTTP request and sends it to the server.

[2090] Step 4:

[2091] Server: Receives HTTP requests from terminals, parses the request content, and extracts search queries and sentiment data.

[2092] Step 5:

[2093] Server: Based on the search query, it searches the "Case Studies" table in the database. Indexes are used to improve efficiency.

[2094] Step 6:

[2095] Server: Passes search results obtained from the database to a generating AI model, which scores and selects the most suitable countermeasures. Based on sentiment data, it prioritizes selecting cases with high recommendation levels.

[2096] Step 7:

[2097] Server: Combines scored optimal countermeasures with supplementary information to generate the final response in JSON format.

[2098] Step 8:

[2099] Server: Sends the generated JSON response back to the terminal.

[2100] Step 9:

[2101] Terminal: Receives the JSON response sent back from the server and parses the data.

[2102] Step 10:

[2103] Terminal: Displays the parsed data on the user interface, allowing users to review case studies.

[2104] Example 3: Providing product information and purchasing procedures

[2105] Step 1:

[2106] User: Enter "fence for landscape improvement" into the product search box on the terminal and click the search button.

[2107] Step 2:

[2108] Terminal: Receives the search query entered by the user and uses the sentiment engine to recognize the user's emotion at the time of input.

[2109] Step 3:

[2110] Terminal: Combines sentiment data and search queries into an HTTP request and sends it to the server.

[2111] Step 4:

[2112] Server: Receives HTTP requests from terminals, parses the request content, and extracts search queries and sentiment data.

[2113] Step 5:

[2114] Server: Searches the product database based on the search query. Performs filtering based on product category and characteristics.

[2115] Step 6:

[2116] Server: Organizes product information retrieved from the database and prioritizes presenting highly recommended products based on sentiment data.

[2117] Step 7:

[2118] Server: Generates organized product information in JSON format and sends it back to the terminal.

[2119] Step 8:

[2120] Terminal: Receives the JSON response sent back from the server and parses the data.

[2121] Step 9:

[2122] Terminal: Displays parsed data on the user interface, allowing users to view detailed product information.

[2123] Step 10:

[2124] User: Review the displayed product information, add the desired product to your cart, and begin the purchase process.

[2125] Step 11:

[2126] Terminal: Collects information related to the purchase process (user information, payment information, etc.) and sends it to the server as an HTTP request.

[2127] Step 12:

[2128] Server: Receives information regarding the purchase process and processes the payment. Confirms the success of the payment. Based on sentiment data, sends follow-up messages to the user as needed.

[2129] Step 13:

[2130] Server: If the purchase is successful, send a confirmation email to the user and a purchase completion message back to the device.

[2131] Step 14:

[2132] Terminal: Displays the purchase completion message returned from the server on the user interface, notifying the user that the purchase was successful. The emotion engine also monitors the user's emotional state after the purchase and uses this information to improve future services.

[2133] (Example 2)

[2134] 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".

[2135] Traditional systems often failed to consider the user's emotional state when they entered search queries to obtain information, resulting in insufficient information quality. Furthermore, especially when sophisticated information such as legal compliance or landscape protection was required, users could experience stress or confusion due to information overload. The information acquisition and purchasing processes also suffered from frustration due to the lack of consideration for user emotions. Therefore, there is a growing need for systems that recognize user emotions in real time and optimize information provision based on those emotions.

[2136] 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.

[2137] In this invention, the server includes means for the user to input a search query, means for recognizing the user's emotions in real time at the time of input, means for transmitting the input query and emotion information to the server, means for searching a database based on the query and emotion information, means for processing the search results with a generating artificial intelligence model and adding supplementary information based on the emotion information, means for returning the processed results to the user, and means for displaying the returned results on the user interface. This makes it possible to provide optimal information while taking the user's emotions into consideration, improving the user experience and enabling efficient and effective acquisition of information related to legal compliance and landscape measures.

[2138] A "user" refers to a person or organization that uses the system to enter search queries and obtain information.

[2139] "Terminal" refers to a client device used by a user, including devices such as web browsers and mobile applications.

[2140] A "server" refers to a computer system that handles the backend of a system, managing databases, operating AI models, and processing requests.

[2141] A "search query" refers to a question, either in text or other form, that a user enters into their device to obtain specific information.

[2142] An "emotion engine" refers to a system component that recognizes and analyzes a user's emotional state in real time when they enter search queries or browse information.

[2143] An "HTTP request" refers to a form of communication protocol used to send user search queries and other data to a server.

[2144] A "database" refers to an information aggregation system that systematically stores specific information and allows it to be searched and retrieved.

[2145] A "generative artificial intelligence model" refers to a technology that uses pre-trained algorithms to analyze data and generate predictions and supplementary information.

[2146] "JSON format" refers to a lightweight data exchange format for structuring, storing, and exchanging data.

[2147] "User interface" refers to screens and designs that provide visual or interactive elements for users to interact with a system.

[2148] "Search results" refer to the collection of information retrieved from the database based on the entered search query.

[2149] "Supplemental information" refers to additional information added to the main search results, data intended to help users gain a deeper understanding.

[2150] A "case study of countermeasures" refers to a specific example that shows the solutions implemented to address a particular problem, along with their details.

[2151] "Product information" refers to detailed information, specifications, prices, and stock availability of products necessary for landscape improvement measures.

[2152] "Purchase process" refers to the series of steps involved in a user purchasing a selected product online.

[2153] "Following" refers to the act of providing appropriate support or additional information to users based on their emotional state during the purchase or search process.

[2154] This invention provides a system that offers a knowledge base for quickly and efficiently responding to the landscape regulations of each local government. Specifically, it is characterized by the coordinated operation of user, terminal, and server components, and in particular by the inclusion of an emotion engine that recognizes the user's emotions. The following details specific embodiments of this system.

[2155] System Configuration

[2156] This system consists of the following elements:

[2157] 1. User: A person or organization that searches for and views landscape regulations and examples of countermeasures.

[2158] 2. Terminal: This refers to a client device used by the user, including devices such as web browsers and mobile applications. The terminal is equipped with an emotion engine.

[2159] 3. Server: Responsible for the system's backend, including database management, operation of generated AI models, and request processing.

[2160] Hardware and software details

[2161] 1. Emotional Engine:

[2162] Hardware: Cameras and microphones for real-time analysis of the user's facial expressions and voice.

[2163] Software: Facial expression recognition software, voice emotion analysis tool.

[2164] 2. Database:

[2165] Hardware: High-performance data server.

[2166] Software: SQL Database Management System (DBMS).

[2167] 3. Generative AI Models:

[2168] Hardware: Server equipped with a high-performance GPU.

[2169] Software: Deep learning libraries (e.g., TensorFlow, PyTorch).

[2170] System Operation Description

[2171] User actions

[2172] Users access the system using their devices to search for specific landscape regulations and best practices. Specifically, the information retrieval process begins when the user enters a search query into the search box on their device and presses the submit button.

[2173] Terminal role

[2174] The device provides a user interface, including a form for entering search queries and a screen for receiving display results. When a user enters and submits a search query, it is sent to the server as an HTTP request. The device also incorporates an emotion engine that recognizes the user's emotional state in real time. This emotional information is included in the data sent to the server.

[2175] Server Processing

[2176] The server analyzes the search query and sentiment information received from the terminal and searches the database based on this analysis. The database contains landscape regulations from each local government and examples of countermeasures from across the country. The search results are processed by a generative AI model, which suggests supplementary information and optimal countermeasures. The sentiment engine also makes adjustments based on the user's emotions. The server returns these results to the terminal in JSON format.

[2177] Specific operation examples

[2178] Example 1: Searching for information on landscape regulations

[2179] User: Enter "Osaka City's latest landscape ordinance" into the search box on your device.

[2180] Emotion Engine: Recognizes and analyzes the emotions of the user during input.

[2181] Terminal: Receives search queries and sentiment information and sends them to the server as HTTP requests.

[2182] Server: Analyzes queries and sentiment information, retrieves Osaka City's landscape ordinance information from the database, and adds supplementary information.

[2183] Server: Returns the generated results to the terminal in JSON format.

[2184] Terminal: Displays received data in the user interface.

[2185] Example 2: Searching for countermeasure examples

[2186] User: Enter "Examples of landscape measures for commercial buildings in Shibuya Ward" into the search box on your device.

[2187] Emotion Engine: Continuously recognizes the user's emotions as they input.

[2188] Terminal: Receives search queries and sentiment information and sends them to the server as HTTP requests.

[2189] Server: Analyzes queries and sentiment information, and retrieves countermeasure examples from Shibuya Ward in the database. Selects countermeasure examples that have been adjusted by the sentiment engine.

[2190] Server: Returns optimal countermeasure examples to the terminal in JSON format.

[2191] Terminal: Displays received data in the user interface.

[2192] Example of a prompt

[2193] "I want to know the latest Osaka City landscape regulations."

[2194] "Search for examples of landscape improvement measures for commercial buildings in Shibuya Ward."

[2195] "We would like more detailed information about fences used for landscape preservation."

[2196] This allows users to efficiently comply with landscape regulations and easily purchase necessary products. The combination of emotional engines improves the user experience and enables personalized and effective information delivery. It also promotes compliance with regulations by construction companies and facilitates mutual information sharing.

[2197] The flow of the specific processing in Example 2 will be explained using Figure 13.

[2198] Step 1:

[2199] User input:

[2200] The user enters a specific query (e.g., "Osaka City's latest landscape regulations") into the search box on their device and clicks the submit button.

[2201] Input: The search query entered by the user.

[2202] Output: Click event of the submit button.

[2203] Specific operation: Enter text into the search box and click the submit button. During this process, the emotion engine analyzes the user's facial expressions and voice in real time to obtain emotional information.

[2204] Step 2:

[2205] Generating a search request on the device:

[2206] The terminal sends the user's input query and analyzed sentiment information to the server as an HTTP request.

[2207] Input: User-entered search queries and sentiment information analyzed by the sentiment engine.

[2208] Output: HTTP request.

[2209] Specific operation: Structures search queries and sentiment information entered within the terminal and generates them as HTTP requests.

[2210] Step 3:

[2211] Query parsing on the server:

[2212] The server parses the received HTTP request, extracts the query content and sentiment information, and then analyzes it.

[2213] Input: HTTP request (including search query and sentiment information).

[2214] Output: Analyzed query information and sentiment information.

[2215] Specific actions: The query content is parsed to extract key information for searching. Simultaneously, sentiment information is analyzed to understand the user's current emotional state.

[2216] Step 4:

[2217] Database search and AI model usage:

[2218] The server searches the database based on the query and uses a generative AI model to retrieve and analyze relevant information.

[2219] Input: Analyzed query information and sentiment information.

[2220] Output: Relevant information and analysis results retrieved from the database.

[2221] Specific operations: Access the database, search for and retrieve data related to the query. Analyze the retrieved data using a generative AI model and select the information to provide to the user.

[2222] Step 5:

[2223] Search result generation and sentiment engine optimization:

[2224] The server uses an emotion engine to adjust search results based on the user's emotional information and adds supplementary information.

[2225] Input: Relevant information and analysis results obtained from the database, and user sentiment information.

[2226] Output: Search results with adjustments and supplementary information added.

[2227] Specific operation: Based on data analyzed by the generative AI model, necessary supplementary information is generated, and the emotion engine adds information according to the user's emotional state.

[2228] Step 6:

[2229] Returning search results:

[2230] The server returns the adjusted search results to the terminal in JSON format.

[2231] Input: Search results with adjustments and supplementary information added.

[2232] Output: Search results encoded in JSON format.

[2233] Specific operation: Encode the generated search results into JSON format and send them to the terminal as an HTTP response.

[2234] Step 7:

[2235] Displaying results on the device:

[2236] The terminal parses the received JSON data and displays it on the user interface.

[2237] Input: Search results in JSON format.

[2238] Output: Search results displayed on the user interface.

[2239] Specific operation: Parses JSON data and displays it in a user-friendly format. Provides information to the user, such as highlighting particularly important sections. The sentiment engine continues to analyze the user's emotions at this point and dynamically adjusts the displayed content as needed.

[2240] Step 8:

[2241] User feedback:

[2242] The user reviews the displayed search results and then takes the next action.

[2243] Input: The displayed search results.

[2244] Output: Input for the next query and other operations.

[2245] Specific actions: The system reviews the information provided by the user and asks further questions if there are any unclear points. Additionally, the sentiment engine records the user's emotional state to help with future searches.

[2246] (Application Example 2)

[2247] 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".

[2248] The conventional system lacked a sufficient knowledge base to respond quickly and efficiently to the landscape regulations and countermeasures of each local government, and was unable to address the emotions of users. Furthermore, in material selection and design proposals at the factory site, appropriate support that took into account the emotions of operators was not provided, resulting in decreased work efficiency.

[2249] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for the user to input a search query, means for sending the input query to the server, means for searching the database based on the query, means for processing the search results with a generating AI model and adding supplementary information, means for returning the processed results to the user, means for displaying the returned results on the user interface, and means for recognizing the user's emotions using an emotion engine and adjusting the search results and displayed content based on those emotions. This makes it possible to select the optimal materials and propose designs that correspond to the user's emotions.

[2250] A "user" is a person or operator who uses this system to enter search queries and receive the results.

[2251] A "search query" is a question or keyword that a user enters to obtain specific information.

[2252] A "server" is a computer system that handles the backend of a system, managing databases, operating AI models, and processing requests.

[2253] A "database" is a collection of information that stores landscape regulations, countermeasures, and product information from various local governments.

[2254] A "generative AI model" is an artificial intelligence algorithm that processes information corresponding to search queries and suggests supplementary information and optimal solutions.

[2255] "Supplemental information" refers to related information and reference materials added to the search results.

[2256] A "user interface" refers to the screens and means of operation that a user uses to interact with a system.

[2257] An "emotion engine" is emotion recognition software that recognizes a user's emotions and adjusts search results and displayed content accordingly.

[2258] A "landscape ordinance" is a set of rules and policies established by each local government regarding the protection of the landscape.

[2259] A "case study of countermeasures" is a specific example of a method or approach for solving a particular problem.

[2260] "Product information" refers to detailed data about a product, such as specifications, price, and stock availability.

[2261] The "purchase process" is the process by which a user buys a selected product online.

[2262] This invention provides a system that offers a knowledge base for quickly and efficiently responding to the landscape regulations of each local government. A detailed description of the embodiments for carrying out the invention is given below.

[2263] System components

[2264] This system consists of the following elements:

[2265] 1. User: A factory operator who uses the system to search and view landscape regulati...

Claims

1. The means by which the user enters a search query, A means of sending the input query to the server, A means of searching a database based on a query, A means of processing search results with a generating AI model and adding supplementary information, A means of returning the processed results to the user, A means of displaying the returned results on the user interface, A system that includes this.

2. The system according to claim 1, further comprising means for selecting and presenting the most suitable countermeasures from the search results.

3. The system according to claim 1, further comprising means for providing product information necessary for landscape protection and managing the purchase procedure.

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