system

Abstract

Provide a system. [Solution] an input means for a user to input information about a search target; a data collection means for collecting related data from the Internet or a database based on the information received from the input means; data analysis means for analyzing the data collected by the data collection means and identifying search hints and highly accurate search points; a result providing means for providing the information identified by the data analysis means to a user; A system including:

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Searches for undiscovered buried treasures, unknown ruins, and hidden treasures have traditionally been based on a great deal of speculation and uncertain information, making it difficult to pinpoint their exact locations. Furthermore, searches based on legend and speculation have led many people and organizations to use their time and resources inefficiently. Furthermore, because related information is widely scattered, comprehensive data collection and analysis are required, but doing so manually is inefficient. [Means for solving the problem]

[0005] The present invention is a system that includes an input means for a user to input information regarding a search target, a data collection means for collecting related data from the Internet or a database based on the information received from the input means, a data analysis means for analyzing the data collected by the data collection means and identifying search hints and highly likely search points, and a result provision means for providing the user with the information identified by the data analysis means.

[0006] Furthermore, the data analysis means uses natural language processing technology and machine learning algorithms to classify and organize related information and identify the location of the search target, enabling more accurate information to be provided. Furthermore, the result provision means includes map display means and graph display means for visually displaying the analysis results for the identified location, making it easier for users to intuitively understand the information. This makes it possible to efficiently increase the accuracy of searches and reduce unnecessary resource consumption.

[0007] "User" refers to an individual or organization that uses the system.

[0008] "Input means" refers to an interface through which a user provides information about a search target to the system.

[0009] "Data collection means" refers to the module through which the system collects relevant information from the internet and databases.

[0010] "Data analysis means" refers to a module that analyzes collected data and identifies search hints and highly likely search points.

[0011] The "result providing means" refers to a module for presenting the information obtained by the data analysis means to the user.

[0012] "Natural language processing technology" refers to technology for understanding, classifying, and analyzing collected text data.

[0013] "Machine learning algorithms" refer to automated learning techniques used to find patterns in collected data and identify areas to search.

[0014] "Map display means" refers to an interface for visually displaying the analyzed results on a map.

[0015] "Graph display means" refers to an interface for visually displaying the analyzed results in a graph.

[0016] "Search target" refers to buried treasure, unknown ruins, hidden treasure, or legendary creatures that the user is trying to discover. [Brief explanation of the drawings]

[0017] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram illustrating a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0018] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0019] First, the terms used in the following description will be explained.

[0020] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0021] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0022] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0023] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0024] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 1, a 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.

[0028] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0029] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0030] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the 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.

[0031] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0032] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process 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.

[0035] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0036] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0037] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0038] This invention is a system for efficiently searching for undiscovered buried treasure and unknown ruins and treasures. This system collects and analyzes related data based on information about the search target entered by the user, and provides useful information to the user. Below, the processing of the program of this system is explained in natural language.

[0039] System Overview

[0040] 1. User data entry

[0041] The user provides information about the search target to the system through the input interface of the terminal. For example, the user inputs information such as "Tokugawa's buried treasure" or "Tsuchinoko discovery."

[0042] 2. Data Collection

[0043] The terminal converts the information received from the user into an appropriate format and sends it to the server. The server then collects related data based on the received information. Web crawling and database query techniques are used to collect related information such as literature, papers, news articles, and historical maps from the Internet and various databases.

[0044] 3. Data Analysis

[0045] The server launches an analysis module to analyze the collected data. The analysis module uses natural language processing technology and machine learning algorithms to classify and organize the collected data. It then identifies search locations and clues and calculates their reliability.

[0046] 4. Providing results

[0047] The server compiles the analysis results into a report format and sends it to the terminal, which then visually displays the results to the user in an intuitive, easy-to-understand format, such as using maps or graphs.

[0048] Specific examples

[0049] Example 1: Searching for Tokugawa's buried treasure

[0050] 1. Data Entry

[0051] The user types into the terminal, "Tell me the location of Tokugawa's buried treasure."

[0052] 2. Data Collection

[0053] The terminal sends information to a server, which then collects documents, papers, news articles, and historical maps related to "Tokugawa buried treasure" from the Internet and various databases.

[0054] 3. Data Analysis

[0055] The server's analysis module analyzes the collected data and extracts historical geographical information and records related to buried treasure based on a specific area. For example, the area around a certain temple in Tokyo is identified as a likely search point.

[0056] 4. Providing results

[0057] The server compiles the analysis results into a report and sends it to the device, which then displays to the user, "There is a high possibility that buried treasure is near a certain temple in Tokyo."

[0058] Example 2: Discovering a worm

[0059] 1. Data Entry

[0060] The user types into the terminal, "Tell me where Tsuchinoko appears."

[0061] 2. Data Collection

[0062] The terminal sends information to a server, which then collects information about sightings, legends, and data on the ecology of "Tsuchinoko" from the Internet and various documents.

[0063] 3. Data Analysis

[0064] The server's analysis module analyzes the collected data and identifies areas with a high number of Tsuchinoko sightings. For example, the mountainous areas of Gifu Prefecture are identified as likely areas.

[0065] 4. Providing results

[0066] The server compiles the analysis results into a report and sends it to the device, which then displays to the user, "There have been many reports of Tsuchinoko sightings in the mountainous areas of Gifu Prefecture."

[0067] In this way, the system of the present invention enables users to conduct efficient searches by providing the exact locations and highly accurate search points of undiscovered buried treasures, unknown ruins, and hidden treasures.

[0068] The processing flow will be explained below.

[0069] Step 1: The user operates the device and inputs information about the search target. Specifically, the user enters the name and related information of the buried treasure or legendary treasure they are searching for in the input form, and clicks the submit button.

[0070] Step 2: The terminal receives the information entered by the user, converts this information into an appropriate format (e.g., JSON format), and then sends it to the server.

[0071] Step 3: The server analyzes the information received from the device and extracts keywords related to the search target. These keywords are the basis for data collection.

[0072] Step 4: The server's data collection module starts up and collects relevant information from the Internet and databases, using web crawling techniques and APIs to search data sources such as literature, papers, news articles, and historical maps based on the specified keywords.

[0073] Step 5: The server filters the collected data and retains only the most reliable information, which includes assessing the reliability of the data and removing duplicate data.

[0074] Step 6: The server's data analysis module analyzes the collected data, using natural language processing techniques to classify and organize relevant information, and machine learning algorithms to identify search locations and specific clues.

[0075] Step 7: The server validates the analysis results and calculates a confidence score, which assesses the probability that the identified location is actually related to the search target.

[0076] Step 8: The server compiles the analysis results into a report, which includes the identified locations, hints, and a confidence rating.

[0077] Step 9: The server sends the generated report to the terminal, which then displays the received report to the user.

[0078] Step 10: The terminal visualizes the report and presents it to the user in an intuitive format, such as a map or graph display, so the user can check the results and plan their next exploration.

[0079] The above is the processing flow of the system and the specific operations performed at each step.

[0080] Example 1

[0081] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0082] Currently, the search for undiscovered buried treasures, unknown ruins, and hidden treasures requires a lot of time and resources, and the success rate is low. Traditional methods often rely on manual information collection and analysis, which lack accuracy and efficiency, resulting in a large amount of search effort. This makes it difficult for explorers to quickly and efficiently obtain useful information.

[0083] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0084] In this invention, the server includes an input means for a user to input information about a search target, a format conversion means for converting the information received from the input means into JSON format data and transmitting it to the server, a data collection means for collecting related data from the Internet or a database based on the data generated by the format conversion means, a data analysis means for analyzing the data collected by the data collection means and identifying search hints and highly likely search points using natural language processing technology and machine learning algorithms, and a result provision means for providing the user with the information identified by the data analysis means. This makes it possible to efficiently and accurately collect information about the search target, analyze it, and provide the results, thereby increasing the success rate of the search.

[0085] A "user" is an entity that inputs information about a search target into the system and receives results based on that information.

[0086] "Input means" refers to an interface through which a user inputs information about a search target into the system.

[0087] The "format conversion means" is a function that converts information received from the user into JSON format data and sends it to the server.

[0088] The "data collection means" is a function that collects related information from the Internet and databases based on the data generated by the format conversion means.

[0089] "Data analysis means" is a function that analyzes collected data, organizes and classifies information using natural language processing technology and machine learning algorithms, and identifies search hints and highly likely search points.

[0090] The "result providing means" is a function for providing the information identified by the data analysis means to the user.

[0091] "Natural language processing technology" is a technology for analyzing text data and understanding meaning and relationships.

[0092] A "machine learning algorithm" is an algorithm that learns from large amounts of data, finds patterns, and makes predictions and classifications.

[0093] "Search target" refers to undiscovered buried treasure, unknown ruins, or hidden treasures that the user wishes to discover or identify.

[0094] The "JSON format" is a lightweight data exchange format for representing data in a structured manner.

[0095] The "Internet" is an information and communications network that connects computer networks around the world.

[0096] A "database" is a system for efficiently managing and searching data.

[0097] "Search hints" refer to important clues or information about the subject of the search.

[0098] A "high probability search point" refers to a location that is determined to be highly likely to contain the search target.

[0099] This invention is a system for efficiently searching for undiscovered buried treasures and unknown ruins and treasures. This system collects and analyzes related data based on information about the search target entered by the user, and provides the user with useful information.

[0100] System Overview

[0101] 1. User data entry

[0102] The user provides information about the search target to the system through the terminal's input interface. This interface has keyboard and voice input functions. For example, the user can input information such as "Tokugawa's buried treasure" or "Tsuchinoko discovery."

[0103] 2. Information format conversion

[0104] The terminal converts the information received from the user into an appropriate format and sends it to the server. This format conversion has the function of converting information entered in natural language into JSON format. As a concrete example, the question entered by the user, "Tell me the location of Tokugawa's buried treasure," is converted into the JSON format {"request": "Tell me the location of Tokugawa's buried treasure"}.

[0105] 3. Data Collection

[0106] The server collects relevant data based on the converted data. Data collection methods include web crawling and database querying, and gathers information from the internet and various databases. Specific software used includes Scrapy and Beautiful Soup for web crawling, and SQL and NoSQL query tools for database queries.

[0107] 4. Data Analysis

[0108] The server launches an analysis module to analyze the collected data. This analysis module classifies and organizes the data using natural language processing technology and machine learning algorithms. Specific technologies used are Google® Cloud Natural Language and Amazon Comprehend, and machine learning algorithms include Tensorflow® and Pandas.

[0109] 5. Providing results

[0110] The server compiles the analysis results into a report and sends it to the device. The device has the functionality to visually display the results. For example, it can use the Google Maps API to display a specific area on a map, providing the results in an easy-to-understand format for the user.

[0111] Specific examples

[0112] Example 1: Searching for Tokugawa's buried treasure

[0113] 1. User data entry

[0114] The user types into the terminal, "Tell me the location of Tokugawa's buried treasure."

[0115] 2. Information format conversion

[0116] The terminal converts this input into the JSON format {"request": "Tell me the location of Tokugawa's buried treasure"}.

[0117] 3. Data Collection

[0118] The server uses Scrapy and SQL queries to collect documents and historical maps related to "Tokugawa buried treasure" from the web and various databases.

[0119] 4. Data Analysis

[0120] The server uses the Google Cloud Natural Language API to analyze literature and data, and identifies "near a certain temple in Tokyo" as a likely search point.

[0121] 5. Providing results

[0122] The server compiles the analysis results into an HTML report and sends it to the device, which then uses the Google Maps API to display the identified location and tell the user, "There is a high possibility that buried treasure is near a certain temple in Tokyo."

[0123] Example 2: Discovering a worm

[0124] 1. User data entry

[0125] The user types into the terminal, "Tell me where Tsuchinoko appears."

[0126] 2. Information format conversion

[0127] The device converts this input into the JSON format {"request": "Tell me where the Tsuchinoko appears"}.

[0128] 3. Data Collection

[0129] The server uses Beautiful Soup and SQL queries to gather information about sightings and legends related to "Tsuchinoko" from the web and databases.

[0130] 4. Data Analysis

[0131] The server uses the Google Cloud Natural Language API to analyze the sightings and identify the "mountainous areas of Gifu Prefecture" as a likely area.

[0132] 5. Providing results

[0133] The server compiles the analysis results into a report and sends it to the device. The device then displays the location identified using the Google Maps API, informing the user that "there have been many reports of Tsuchinoko sightings in the mountainous areas of Gifu Prefecture."

[0134] This allows the user to efficiently and accurately search for buried treasure, unknown ruins, and hidden treasures.

[0135] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0136] Step 1:

[0137] The terminal receives input from the user. The user enters information about the search target into the terminal's interface. This information is written in natural language and includes phrases such as "Tokugawa's buried treasure" and "Tsuchinoko discovery." The input is raw data that the terminal prepares for the next processing step.

[0138] Step 2:

[0139] The device converts the received user input information into JSON format. For example, the input "Tell me where Tokugawa's buried treasure is" is converted into the JSON format {"request": "Tell me where Tokugawa's buried treasure is"}. This conversion process enables subsequent data collection and analysis to be carried out efficiently. The converted data is then sent to the server via a dedicated API.

[0140] Step 3:

[0141] The server collects related information based on the JSON-formatted data received from the device. The server uses web crawling technologies (such as Scrapy and Beautiful Soup) to collect data from Internet documents, news articles, blog posts, etc. It also uses database query technologies to retrieve related information from various databases. The collected data is primarily text data related to the subject the user is searching for.

[0142] Step 4:

[0143] The server analyzes the data collected by the data collection method. It launches an analysis module (using TensorFlow or Pandas) to classify and organize the collected data. At this stage, natural language processing technology (Google Cloud Natural Language or Amazon Comprehend) is used to extract important keywords and context. For example, "near a certain temple in Tokyo" is identified as the search target. This analysis process provides search hints and identifies highly likely search points.

[0144] Step 5:

[0145] The server creates a report of the analysis results to provide to the user. This report is created in HTML format and includes the locations identified as analysis results and related information. The report is presented to the user using map display means (Google Maps API) and graph display means for easy visual viewing. The server sends the completed report to the terminal.

[0146] Step 6:

[0147] The device displays the report received from the server to the user. For example, it uses the Google Maps API to display the identified area on a map, visually showing the analysis results. It also provides information in text format, such as "There is a high possibility that buried treasure is found near a certain temple in Tokyo." The user can then carry out specific search activities based on this information.

[0148] This system allows users to efficiently and accurately search for undiscovered buried treasures, unknown ruins and hidden treasures.

[0149] (Application example 1)

[0150] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0151] In modern times, searching for undiscovered buried treasures, unknown ruins, and hidden treasures is difficult because it is difficult to find useful information from the vast amount of information. In particular, because the amount of information is vast and diverse, there is a demand for a system that can efficiently and accurately collect and analyze related information. In addition, it is necessary to visually display the analysis results so that users can easily understand them intuitively. The purpose of this invention is to solve these problems.

[0152] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0153] In this invention, the server includes an input means for a user to input information about a search target, a data collection means for collecting related data from the Internet or a database based on the information received from the input means, a data analysis means for analyzing the data collected by the data collection means and identifying search hints and highly likely search points, and a result display means for visually displaying the analysis results using maps and graphs, thereby enabling users to efficiently search and intuitively understand the information they need.

[0154] A "user" is a person who uses a terminal to input search target information into the system and receive the analysis results.

[0155] The "input means" is an interface through which the user provides information about the search target to the system.

[0156] The "data collection means" is a device or function that collects related data from the Internet or a database based on the information received from the input means.

[0157] The "data analysis means" is a device or function that uses and analyzes the data collected by the data collection means to identify search hints and highly accurate search points.

[0158] The "result providing means" is a device or function that provides the user with the information identified by the data analysis means.

[0159] The "result display means" is a device or function that visually displays the analysis results using maps or graphs.

[0160] "Natural language processing technology" is a technology for analyzing collected data and classifying and organizing important information related to the search target.

[0161] A "machine learning algorithm" is a mathematical method or model used to identify locations and relevant information to search for in the data being analyzed.

[0162] The "geographical display means" is a device or function for displaying the analysis results for the identified location on a map.

[0163] The "map display means" is a means for visually displaying the analysis results for the identified location on a map.

[0164] The "graph display means" is a means for visually displaying the analysis results for the identified location in a graph.

[0165] Hereinafter, embodiments of the present invention will be described in detail.

[0166] System Overview

[0167] The system components are:

[0168] An input means for the user to input information about the search target

[0169] A data collection means for collecting related data from the Internet or a database based on the information received from the input means.

[0170] a data analysis means for analyzing the data collected by the data collection means and identifying search hints and highly accurate search points;

[0171] a result providing means for providing the information identified by the data analysis means to a user;

[0172] A means of visually displaying analysis results using maps and graphs

[0173] User Data Entry

[0174] Users input information about the search target into the system through the smartphone interface. For example, they might enter a search target such as "Tell me the location of Tokugawa's buried treasure" or "Tell me where Tsuchinoko appears."

[0175] Data collection

[0176] The device sends the input information to a server, which then uses that information to collect relevant data from the internet and databases. Techniques used include HTTP requests using the requests library and web scraping using BeautifulSoup. This allows the server to collect data such as related literature, news articles, and historical maps.

[0177] Data analysis

[0178] The server launches a module to analyze the collected data. This module uses natural language processing techniques and machine learning algorithms (e.g., geographic information analysis using the geopy library and classification and organization using the scikit-learn library) to analyze the collected data. Specifically, it classifies the locations and clues to be explored and identifies specific areas.

[0179] Results provision and display

[0180] The analysis results are displayed visually. The server compiles the analysis results into a report format and sends it to the terminal. The terminal then displays the results to the user visually in the form of a map or graph, using the folium library to display a map and indicate related search points. This allows the user to intuitively understand the analysis results.

[0181] Specific examples

[0182] Example 1: Searching for Tokugawa's buried treasure

[0183] 1. User data entry

[0184] A user types in "Tell me the location of Tokugawa's buried treasure."

[0185] 2. Data Collection

[0186] The terminal sends the information to a server, which collects documents and historical maps related to the "Tokugawa buried treasure."

[0187] 3. Data Analysis

[0188] The server's analysis module analyzes the data and identifies the location as "near a certain temple in Tokyo."

[0189] 4. Providing and displaying results

[0190] The server sends the analysis results, and the device displays a map saying, "There is a high possibility that buried treasure is near a certain temple in Tokyo."

[0191] Example 2: Discovery of the earthworm

[0192] 1. User data entry

[0193] The user types, "Tell me where Tsuchinoko appears."

[0194] 2. Data Collection

[0195] The terminal sends information to a server, which collects sightings and legends about "Tsuchinoko."

[0196] 3. Data Analysis

[0197] The server's analysis module analyzes the data and identifies the "mountainous area of ​​Gifu Prefecture."

[0198] 4. Providing and displaying results

[0199] The server sends the analysis results, and the device displays on a map, "There have been many sightings of Tsuchinoko in the mountainous areas of Gifu Prefecture."

[0200] Prompt Sentence Examples

[0201] "Design a system to collect and analyze information on the search for undiscovered buried treasure and unknown ruins and treasures, and identify search points. Also, provide an interface that users can use easily on their smartphones."

[0202] The above is a specific embodiment for carrying out the present invention. This system allows the user to perform searches efficiently and intuitively.

[0203] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0204] Step 1:

[0205] The user enters information about the search target.

[0206] Input: The user enters the search target (e.g., "Tokugawa's buried treasure" or "Tsuchinoko") into the smartphone's input interface.

[0207] Output: User input is received and prepared at the terminal.

[0208] Step 2:

[0209] The terminal transmits the input information to the server.

[0210] Input: The device uses the search target information entered by the user.

[0211] Output: The server starts collecting data based on the information received from the device.

[0212] Step 3:

[0213] The server collects the relevant data.

[0214] Input: The server gathers data from the internet and databases based on user input, using the requests library to send web requests and BeautifulSoup to parse web pages.

[0215] Output: Collected data (literatures, papers, news articles, historical maps, etc.) are stored on a server.

[0216] Step 4:

[0217] The server analyzes the data.

[0218] Input: Based on the collected data, the server uses natural language processing techniques and machine learning algorithms (e.g. geopy and scikit-learn).

[0219] Output: As a result of the analysis, the server identifies search hints and highly likely search points.

[0220] Step 5:

[0221] The server compiles the analysis results into a report.

[0222] Input: Use the results of data analysis (e.g., identified locations and hints).

[0223] Output: Analysis results are generated in a report format (text, images, maps, etc.).

[0224] Step 6:

[0225] The server sends the analysis results to the device.

[0226] Input: Use the analysis results summarized in report format.

[0227] Output: The analysis results are sent to the terminal.

[0228] Step 7:

[0229] The device visually displays the analysis results.

[0230] Input: Use the analysis results (report format) received from the server.

[0231] Output: The terminal visually displays the analysis results on maps and graphs, providing them to the user in an intuitive manner. Specifically, the folium library is used to display markers indicating specific points on a map.

[0232] The above are the processing steps of the program for the system that realizes the application example of the present invention.

[0233] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0234] This invention is a system for efficiently searching for undiscovered buried treasure and unknown ruins and treasures. This system collects and analyzes related data based on information about the search target entered by the user, provides useful information to the user, and optimizes the way results are presented by recognizing the user's emotional state. The program processing of this system is explained below in natural language.

[0235] System Overview

[0236] 1. User data entry

[0237] The user provides information about the search target to the system through the input interface of the terminal. For example, they input information such as "Tokugawa's buried treasure" or "Tsuchinoko discovery." The system uses an emotion engine to recognize the user's emotional state at the time of input and associates it with the data.

[0238] 2. Data Collection

[0239] The terminal converts the information received from the user into an appropriate format and sends it to the server. The server then collects relevant data based on the received information. Web crawling and database query techniques are used to collect data, searching data sources such as literature, papers, news articles, and historical maps based on specified keywords.

[0240] 3. Data Analysis

[0241] The server launches an analysis module to analyze the collected data. The analysis module uses natural language processing technology and machine learning algorithms to classify and organize the collected data. It then identifies search locations and hints and calculates their reliability. The analysis results are provided taking into account the user's emotional state.

[0242] 4. Providing results

[0243] The server compiles the analysis results into a report and sends it to the device. The device then presents the results visually to the user in an intuitively understandable format, such as a map or graph. The device can also adjust the way the results are presented depending on the user's emotional state.

[0244] Specific examples

[0245] Example 1: Searching for Tokugawa's buried treasure

[0246] 1. Data Entry

[0247] A user types into a terminal, "Tell me the location of Tokugawa's buried treasure." At this time, the emotion engine recognizes the user's emotional state (e.g., excitement, anticipation, anxiety, etc.) and attaches it to the data.

[0248] 2. Data Collection

[0249] The terminal sends information to a server, which then collects documents, papers, news articles, and historical maps related to "Tokugawa buried treasure" from the Internet and various databases.

[0250] 3. Data Analysis

[0251] The server's analysis module analyzes the collected data and extracts historical geographical information and records related to buried treasure based on a specific area. For example, the area around a certain temple in Tokyo is identified as a likely search point.

[0252] The system takes into account the user's emotional state and adjusts the reliability of the data and the order in which the analysis results are presented.

[0253] 4. Providing results

[0254] The server compiles the analysis results into a report and sends it to the device, which then displays to the user, "There is a high possibility that buried treasure is near a certain temple in Tokyo." The display method is adjusted based on the user's emotional state.

[0255] Example 2: Discovering a worm

[0256] 1. Data Entry

[0257] The user types into the terminal, "Tell me where Tsuchinoko appears." At this time, the emotion engine recognizes the user's emotional state and attaches it to the data.

[0258] 2. Data Collection

[0259] The terminal sends information to a server, which then collects information about sightings, legends, and data on the ecology of "Tsuchinoko" from the Internet and various documents.

[0260] 3. Data Analysis

[0261] The server's analysis module analyzes the collected data and identifies areas with a high number of Tsuchinoko sightings. For example, the mountainous areas of Gifu Prefecture are identified as likely areas.

[0262] The emotional state of the user is taken into account and influenced to effectively present the analysis results.

[0263] 4. Providing results

[0264] The server compiles the analysis results into a report and sends it to the device. The device then displays to the user, "There have been many reports of Tsuchinoko sightings in the mountainous areas of Gifu Prefecture." The optimal display method is selected based on the user's emotional state.

[0265] In this way, the system of the present invention not only provides the exact location and highly accurate search points of undiscovered buried treasures, unknown ruins, and hidden treasures, but also recognizes the user's emotional state and optimizes the way results are presented, thereby increasing the motivation and efficiency of the search.

[0266] The processing flow will be explained below.

[0267] Step 1: The user operates the device and inputs information about the search target. Specifically, the user enters the name of the search target and related information, such as "Tokugawa's buried treasure" or "Tsuchinoko discovery," into the device's input interface, and clicks the send button.

[0268] Step 2: The device receives the information entered by the user. At the same time, the device activates an emotion engine to recognize the user's emotional state. For example, it analyzes the text, facial expressions, and voice of the user when they enter information to determine whether they are excited or anxious.

[0269] Step 3: The device converts the received information and the recognized emotional state into an appropriate format (e.g., JSON format) and then sends it to the server. The transmitted data includes information about the search target and the user's emotional state.

[0270] Step 4: The server analyzes the information received from the device and extracts keywords related to the search target. Specifically, it activates the data collection module based on keywords such as "Tokugawa buried treasure" and "Tsuchinoko."

[0271] Step 5: The server's data collection module collects relevant information from the Internet and databases, using web crawling techniques and APIs to search data sources such as literature, papers, news articles, and historical maps.

[0272] Step 6: The server filters the collected data and keeps only the most reliable information, which includes assessing the reliability of the data and removing duplicate data.

[0273] Step 7: The server's data analysis module analyzes the collected data. This analysis involves using natural language processing techniques to categorize and organize relevant information, and machine learning algorithms to identify locations and specific clues to explore. The user's emotional state is also reflected as part of the analysis.

[0274] Step 8: The server validates the analysis results and calculates a confidence score, which evaluates the probability that the identified location is actually related to the search target. The server also takes into account the user's emotional state and adjusts the response, for example by providing more detailed information if the user is excited.

[0275] Step 9: The server compiles the analysis results into a report, which includes the identified locations, hints, and a confidence rating.

[0276] Step 10: The server sends the generated report to the terminal, which then displays the received report to the user.

[0277] Step 11: The device visualizes the report and presents it to the user in an intuitive format, such as through maps or graphs. Based on the user's emotional state, the presentation of the results is adjusted accordingly. For example, if the user is feeling anxious, additional information to reassure them is added.

[0278] The above is the processing flow of the system that combines the emotion engine and the specific operations that are performed at each step.

[0279] Example 2

[0280] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0281] This invention relates to a system for efficiently searching for undiscovered buried treasures and unknown ruins and treasures. Traditionally, the process of inputting search information, collecting related data, analyzing it, and providing results has often been done manually, which requires time and effort. Furthermore, the results provided may not be appropriate based on the user's emotional state, potentially reducing the user's motivation. Therefore, a system is needed that automates the search process and optimizes the way results are presented based on the user's emotional state.

[0282] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes an input means through which a user inputs information about a search target, a data collection means that collects related data from the Internet or a database based on the information received from the input means, a data analysis means that analyzes the data collected by the data collection means and identifies search hints and highly likely search points, a result providing means that provides the user with the information identified by the data analysis means, and an emotion recognition means that recognizes the user's emotional state and optimizes the method of presenting results accordingly. This makes it possible to automate the search process and provide optimal results according to the user's emotional state.

[0283] The "input means" is a means by which a user inputs information about a search target into the system.

[0284] "Data collection means" refers to a means of collecting related data from the Internet or a database based on the input information.

[0285] "Data analysis means" is a means for analyzing collected data and identifying search hints and highly likely search points.

[0286] The "result providing means" is a means for providing the information identified by the data analysis means to the user.

[0287] An "emotion recognition means" is a means for recognizing the user's emotional state and optimizing the way results are presented accordingly.

[0288] This invention is a system for efficiently searching for undiscovered buried treasures and unknown ruins and treasures. A specific method for implementing this system will be described.

[0289] System configuration

[0290] This system consists of a user terminal and a server.

[0291] 1. User Device

[0292] The user device has an interface for inputting information about the search target. Input methods include a keyboard, touch screen, and voice input. To recognize the user's emotional state, the device is equipped with an emotion engine (e.g., Emotion AI SDK).

[0293] 2. Server

[0294] The server collects data based on the information sent from the user's device, analyzes it, and provides the results. Data collection methods include web scraping tools using Python (e.g., BeautifulSoup, Scrapy) and database query technologies (e.g., SQL). Analysis methods include natural language processing technologies (e.g., NLTK, SpaCy) and machine learning algorithms (e.g., scikit-learn, TensorFlow). Results are provided using map displays (e.g., Leaflet.js) and graph displays (e.g., D3.js).

[0295] System operation explanation

[0296] 1. Data Entry

[0297] The user uses the device's input interface to input information about the search target, for example, "Tell me the location of Tokugawa's buried treasure." At this time, the emotion engine recognizes the user's emotional state at the time of input and adds it to the data.

[0298] 2. Data Collection

[0299] The device converts the information into an appropriate format (e.g., JSON format) and sends it to the server. The server processes the received data and generates queries to collect relevant data. Web crawling techniques (e.g., Scrapy) and database query techniques (e.g., SQL) are used to collect literature, papers, news articles, historical maps, etc. from the Internet and various databases.

[0300] 3. Data Analysis

[0301] The server's analysis module runs and analyzes the collected data. It uses natural language processing techniques (e.g., NLTK, SpaCy) to classify the data, and machine learning algorithms (e.g., scikit-learn, TensorFlow) to identify locations and hints related to the search target and calculate their reliability. Furthermore, it adjusts the order and content of the results based on the user's emotional state.

[0302] 4. Providing results

[0303] The server compiles the analysis results into a report and sends it to the device. The device then presents the results to the user in a visually easy-to-understand format, such as an interactive map (e.g., Leaflet.js) or a graph display (e.g., D3.js). The display method is adjusted according to the user's emotional state to provide an optimal user experience.

[0304] Specific examples

[0305] Example 1: Searching for Tokugawa's buried treasure

[0306] 1. Data Entry

[0307] The user types into the terminal, "Tell me the location of Tokugawa's buried treasure." The emotion engine recognizes the user's emotional state, such as excitement or anticipation, and adds it to the data.

[0308] 2. Data Collection

[0309] The device converts the information into JSON format and sends it to a server that collects documents, papers, news articles, historical maps, and other information related to the "Tokugawa buried treasure."

[0310] 3. Data Analysis

[0311] The server's analysis module tokenizes the data and organizes it into categories. For example, it may identify "near a certain temple in Tokyo" as an analysis result.

[0312] The order and content of the analysis results are adjusted taking into account the user's emotional state.

[0313] 4. Providing results

[0314] The server compiles the analysis results into a report and sends it to the device. The device then displays the results as an interactive map, showing users that "there is a high possibility that buried treasure is near a certain temple in Tokyo." The display style uses bright colors to match the user's expectations.

[0315] Example 2: Discovering a worm

[0316] 1. Data Entry

[0317] The user types into the device, "Tell me where Tsuchinoko appears." The emotion engine recognizes the user's excitement or surprise and adds it to the data.

[0318] 2. Data Collection

[0319] The device converts the information into a suitable format and sends it to a server, which collects data, including sightings and legends, from the internet and literature.

[0320] 3. Data Analysis

[0321] The analysis module analyzes the collected sighting information and identifies the "mountainous areas of Gifu Prefecture" as a likely area.

[0322] The display order and content are adjusted based on the user's emotional state.

[0323] 4. Providing results

[0324] The server compiles the analysis results into a report and sends it to the device. The device then visually displays the results, saying, "There have been many reports of Tsuchinoko sightings in the mountainous areas of Gifu Prefecture." The display style uses a subdued color scheme to avoid amplifying the sense of surprise.

[0325] In this way, the system of the present invention can consistently automate the process from collecting data on search targets to analyzing them and providing the results, and can present the results in a form optimized based on the user's emotional state.

[0326] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0327] Step 1:

[0328] The user inputs information about the search target using the terminal's input interface. The input information includes a prompt sentence, such as "Tell me the location of Tokugawa's buried treasure." The emotion engine uses voice tone and facial expression recognition technology to recognize the user's emotional state (e.g., excitement, anticipation, anxiety) and adds it to the input data. The input at this time is the prompt sentence and emotional information. The output is formed as a single dataset.

[0329] Step 2:

[0330] The device converts the data received from the user into an appropriate format (e.g., JSON) and sends it to the server. A programming language such as Python is used as the conversion method. The input is a dataset containing a prompt sentence and emotion information, and the output is formatted data. Specifically, the format conversion function is called, the data is converted into JSON format, and then sent to the server.

[0331] Step 3:

[0332] The server processes the received formatted data and generates queries to collect relevant data. It uses web crawling techniques (e.g., Scrapy) or database query techniques (e.g., SQL) to collect data. The input is the formatted data, and the output is the collected relevant data. Specifically, it calls the query generation function to collect the required data from the Internet or a database.

[0333] Step 4:

[0334] The server launches an analysis module (e.g., NLTK, scikit-learn) to analyze the collected data. The analysis module tokenizes the collected data using natural language processing techniques and classifies the data using machine learning algorithms. The input is the collected relevant data, and the output is the analysis results. Specific operations include tokenizing text and running a category classification algorithm.

[0335] Step 5:

[0336] The server compiles the analysis results into a report, which includes the location of the search target, related hints, and confidence data. It also configures the content to be presented in an easy-to-interpret manner based on the user's emotional state. The input is the analysis results and the user's emotional information, and the output is data in report format. Specific operations include calling a report generation function, organizing the data, and adjusting the display style according to the user's emotional state.

[0337] Step 6:

[0338] The server sends the analysis results in report format to the terminal. The terminal launches a map display (e.g., Leaflet.js) or graph display (e.g., D3.js) module to visually display the results to the user. The input is data in report format, and the output is the visually displayed results. Specific operations include launching the map display module and graph display module, and selecting the optimal display method based on the user's emotional state.

[0339] Through the above steps, this system efficiently and effectively searches for undiscovered buried treasures and unknown ruins and treasures, and provides optimal information to users.

[0340] (Application example 2)

[0341] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0342] Conventional search systems provide information without considering the user's emotional state, which can result in insufficient results. Furthermore, when detecting anomalies within a factory, providing information without considering the emotional state of the manager can reduce the effectiveness and efficiency of emergency responses. To solve these problems, technology is needed that recognizes the emotional state of the user or manager and optimizes the way information is presented based on that state.

[0343] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0344] In this invention, the server includes an input means for a user to input information about a search target, a data collection means for collecting related data from the Internet or a database based on the information received from the input means, a data analysis means for analyzing the data collected by the data collection means and identifying search hints and highly likely search points, a result provision means for providing the user with the information identified by the data analysis means, and an emotion recognition means for recognizing the user's emotional state using emotion recognition technology and adjusting the content presented by the provision means based on the user's emotional state. This makes it possible to provide optimal information according to the user's emotional state, thereby improving the effectiveness and efficiency of searches and anomaly detection.

[0345] The "input means by which the user inputs information about the search target" refers to a device or interface that allows the user to input information about the search target.

[0346] "Data collection means for collecting related data from the Internet or databases based on information received from the input means" refers to a function or device for collecting related information from the Internet or various databases based on information from the input means.

[0347] "Data analysis means for analyzing the data collected by the data collection means and identifying search hints and highly likely search points" refers to a function or device for analyzing collected data, and is used to identify information and points that are useful for searching.

[0348] The "result providing means for providing the user with the information identified by the data analysis means" refers to a function or device for conveying the information found by the data analysis means to the user.

[0349] "Emotion recognition means that recognizes the emotional state of a user using emotion recognition technology and adjusts the content presented by the providing means based on the emotional state of the user" refers to a function or device that recognizes emotions using technology that detects the user's emotions and optimizes the information and format of the information to be provided based on the results.

[0350] "Natural language processing technology" refers to technology that enables computers to understand and process human language.

[0351] A "machine learning algorithm" is a method for learning patterns from collected data and making predictions, classifications, and analyses.

[0352] "Map display means" refers to a function or device that provides information in map format to visually display the analysis results.

[0353] "Graph display means" refers to a function or device that provides information in a graph format to visually display the analysis results.

[0354] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The embodiments for carrying out the present invention will be described in detail below.

[0355] This system includes a terminal for users to input information about the search target, a server for collecting related data from the Internet or a database based on that information, and means for analyzing the collected data and providing the results to the user.

[0356] Hardware and Software Configuration

[0357] 1. Terminal

[0358] The terminal is equipped with an input means for the user to input information about the search target, and may be, for example, a smartphone, tablet, or PC. Software for using emotion recognition technology (e.g., emotion recognition API) is installed on this terminal.

[0359] 2. Server

[0360] The server includes the following means:

[0361] Data collection means: Based on the information received from the terminal, relevant data is collected from the Internet and databases. Specifically, HTTP requests are used to obtain the necessary data from various data sources (literature, papers, news articles, etc.).

[0362] Data analysis methods: Analyze the collected data by using natural language processing techniques (e.g., TextBlob) and machine learning algorithms (e.g., IsolationForest) to classify and organize the data and identify search hints and high-confidence search points.

[0363] Result presentation means: The analysis results are presented to the user. The results are adjusted based on the user's emotional state and visually presented to the user using map and graph display means.

[0364] 3. Emotion recognition means

[0365] The emotion recognition means recognizes the emotional state of the user when they input data and attaches that information to the data. This emotion recognition technology utilizes an emotion analysis library such as TextBlob.

[0366] Example of a system

[0367] Example 1: Identifying the discovery location

[0368] The user types into the terminal, "I want you to identify the abnormal part of the machine." At this time, emotion recognition technology is used to analyze the user's emotional state and recognize that the situation is anxious or urgent. The server uses this information to collect related data from the Internet and databases, and identifies the abnormal part using a machine learning algorithm. The results are then presented to the administrator in the form of a map or graph.

[0369] Example 2: Visualization of location information

[0370] The server analyzes the collected data and detects abnormalities in a specific area (e.g., a specific section of a factory). The analysis results are highlighted using map display means or graph display means depending on the manager's emotional state (e.g., if the level of urgency is high).

[0371] Prompt Sentence Examples

[0372] By entering the following prompts into the generative AI model, you can obtain information that will help with system construction and analysis.

[0373] Example prompt:

[0374] Please build a system to collect data within the factory and identify abnormalities as follows:

[0375] Collects sensor data (temperature, vibration, sound, log)

[0376] Uses an anomaly detection algorithm (Isolation Forest)

[0377] Uses Emotion Recognition (TextBlob)

[0378] The above is an embodiment of the present invention, which makes it possible to provide optimal information according to the emotional state of the user, thereby improving the effectiveness and efficiency of search and anomaly detection.

[0379] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0380] Step 1:

[0381] The user inputs information about the search target through the device's input means. For example, the user inputs information such as "Please identify the abnormal part of the machine." At this time, the device uses emotion recognition technology to analyze the user's emotional state and recognizes that the situation is anxious or urgent. The input is in text format, and the analysis result of the emotional state is also output at the same time. This information is sent to the server in the next step.

[0382] Step 2:

[0383] The device sends data to the server based on the information entered in the previous step. The data sent includes the user's input information and emotional state. The server receives this transmitted data and activates the data collection method. Specifically, based on this information, it uses HTTP requests to collect necessary data such as related literature, papers, news articles, and sensor data from the Internet and various databases. The collected data is preprocessed and converted into a format suitable for analysis.

[0384] Step 3:

[0385] The server analyzes the data collected by the data collection method. It uses natural language processing technology (TextBlob) and a machine learning algorithm (IsolationForest) to analyze the data. First, it uses TextBlob to perform text analysis of the collected data, extracting and classifying relevant information. At the same time, it applies IsolationForest to the quantitative data from the sensor to identify abnormalities. The analysis results are output in a format that takes into account the emotion recognition results along with their reliability.

[0386] Step 4:

[0387] The server creates a report to provide to the user based on the results obtained by the data analysis means. Map display means and graph display means are used to provide the results. If the analysis results indicate an abnormality in a specific area or machine, the information is visually displayed in map or graph format. Furthermore, the way in which the results are presented is adjusted according to the user's emotional state (level of urgency or anxiety). For example, if the level of urgency is high, the result is highlighted and a detailed explanation is added.

[0388] Step 5:

[0389] The server sends a final report to the terminal. The terminal receives this report and displays it to the user. The user can then review the displayed report and take any necessary action, such as making arrangements to quickly address any identified abnormalities. This process allows the user to receive optimal information based on their emotional state, improving the efficiency of search and anomaly detection.

[0390] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0391] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0392] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0393] [Second embodiment]

[0394] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0395] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0396] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0397] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0398] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0399] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0400] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0401] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0402] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0403] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0404] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0405] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0406] This invention is a system for efficiently searching for undiscovered buried treasure and unknown ruins and treasures. This system collects and analyzes related data based on information about the search target entered by the user, and provides useful information to the user. Below, the processing of the program of this system is explained in natural language.

[0407] System Overview

[0408] 1. User data entry

[0409] The user provides information about the search target to the system through the input interface of the terminal. For example, the user inputs information such as "Tokugawa's buried treasure" or "Tsuchinoko discovery."

[0410] 2. Data Collection

[0411] The terminal converts the information received from the user into an appropriate format and sends it to the server. The server then collects related data based on the received information. Web crawling and database query techniques are used to collect related information such as literature, papers, news articles, and historical maps from the Internet and various databases.

[0412] 3. Data Analysis

[0413] The server launches an analysis module to analyze the collected data. The analysis module uses natural language processing technology and machine learning algorithms to classify and organize the collected data. It then identifies search locations and clues and calculates their reliability.

[0414] 4. Providing results

[0415] The server compiles the analysis results into a report format and sends it to the terminal, which then visually displays the results to the user in an intuitive, easy-to-understand format, such as using maps or graphs.

[0416] Specific examples

[0417] Example 1: Searching for Tokugawa's buried treasure

[0418] 1. Data Entry

[0419] The user types into the terminal, "Tell me the location of Tokugawa's buried treasure."

[0420] 2. Data Collection

[0421] The terminal sends information to a server, which then collects documents, papers, news articles, and historical maps related to "Tokugawa buried treasure" from the Internet and various databases.

[0422] 3. Data Analysis

[0423] The server's analysis module analyzes the collected data and extracts historical geographical information and records related to buried treasure based on a specific area. For example, the area around a certain temple in Tokyo is identified as a likely search point.

[0424] 4. Providing results

[0425] The server compiles the analysis results into a report and sends it to the device, which then displays to the user, "There is a high possibility that buried treasure is near a certain temple in Tokyo."

[0426] Example 2: Discovering a worm

[0427] 1. Data Entry

[0428] The user types into the terminal, "Tell me where Tsuchinoko appears."

[0429] 2. Data Collection

[0430] The terminal sends information to a server, which then collects information about sightings, legends, and data on the ecology of "Tsuchinoko" from the Internet and various documents.

[0431] 3. Data Analysis

[0432] The server's analysis module analyzes the collected data and identifies areas with a high number of Tsuchinoko sightings. For example, the mountainous areas of Gifu Prefecture are identified as likely areas.

[0433] 4. Providing results

[0434] The server compiles the analysis results into a report and sends it to the device, which then displays to the user, "There have been many reports of Tsuchinoko sightings in the mountainous areas of Gifu Prefecture."

[0435] In this way, the system of the present invention enables users to conduct efficient searches by providing the exact locations and highly accurate search points of undiscovered buried treasures, unknown ruins, and hidden treasures.

[0436] The processing flow will be explained below.

[0437] Step 1: The user operates the device and inputs information about the search target. Specifically, the user enters the name and related information of the buried treasure or legendary treasure they are searching for in the input form, and clicks the submit button.

[0438] Step 2: The terminal receives the information entered by the user, converts this information into an appropriate format (e.g., JSON format), and then sends it to the server.

[0439] Step 3: The server analyzes the information received from the device and extracts keywords related to the search target. These keywords are the basis for data collection.

[0440] Step 4: The server's data collection module starts up and collects relevant information from the Internet and databases, using web crawling techniques and APIs to search data sources such as literature, papers, news articles, and historical maps based on the specified keywords.

[0441] Step 5: The server filters the collected data and retains only the most reliable information, which includes assessing the reliability of the data and removing duplicate data.

[0442] Step 6: The server's data analysis module analyzes the collected data, using natural language processing techniques to classify and organize relevant information, and machine learning algorithms to identify search locations and specific clues.

[0443] Step 7: The server validates the analysis results and calculates a confidence score, which assesses the probability that the identified location is actually related to the search target.

[0444] Step 8: The server compiles the analysis results into a report, which includes the identified locations, hints, and a confidence rating.

[0445] Step 9: The server sends the generated report to the terminal, which then displays the received report to the user.

[0446] Step 10: The terminal visualizes the report and presents it to the user in an intuitive format, such as a map or graph display, so the user can check the results and plan their next exploration.

[0447] The above is the processing flow of the system and the specific operations performed at each step.

[0448] Example 1

[0449] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0450] Currently, the search for undiscovered buried treasures, unknown ruins, and hidden treasures requires a lot of time and resources, and the success rate is low. Traditional methods often rely on manual information collection and analysis, which lack accuracy and efficiency, resulting in a large amount of search effort. This makes it difficult for explorers to quickly and efficiently obtain useful information.

[0451] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0452] In this invention, the server includes an input means for a user to input information about a search target, a format conversion means for converting the information received from the input means into JSON format data and transmitting it to the server, a data collection means for collecting related data from the Internet or a database based on the data generated by the format conversion means, a data analysis means for analyzing the data collected by the data collection means and identifying search hints and highly likely search points using natural language processing technology and machine learning algorithms, and a result provision means for providing the user with the information identified by the data analysis means. This makes it possible to efficiently and accurately collect information about the search target, analyze it, and provide the results, thereby increasing the success rate of the search.

[0453] A "user" is an entity that inputs information about a search target into the system and receives results based on that information.

[0454] "Input means" refers to an interface through which a user inputs information about a search target into the system.

[0455] The "format conversion means" is a function that converts information received from the user into JSON format data and sends it to the server.

[0456] The "data collection means" is a function that collects related information from the Internet and databases based on the data generated by the format conversion means.

[0457] "Data analysis means" is a function that analyzes collected data, organizes and classifies information using natural language processing technology and machine learning algorithms, and identifies search hints and highly likely search points.

[0458] The "result providing means" is a function for providing the information identified by the data analysis means to the user.

[0459] "Natural language processing technology" is a technology for analyzing text data and understanding meaning and relationships.

[0460] A "machine learning algorithm" is an algorithm that learns from large amounts of data, finds patterns, and makes predictions and classifications.

[0461] "Search target" refers to undiscovered buried treasure, unknown ruins, or hidden treasures that the user wishes to discover or identify.

[0462] The "JSON format" is a lightweight data exchange format for representing data in a structured manner.

[0463] The "Internet" is an information and communications network that connects computer networks around the world.

[0464] A "database" is a system for efficiently managing and searching data.

[0465] "Search hints" refer to important clues or information about the subject of the search.

[0466] A "high probability search point" refers to a location that is determined to be highly likely to contain the search target.

[0467] This invention is a system for efficiently searching for undiscovered buried treasures and unknown ruins and treasures. This system collects and analyzes related data based on information about the search target entered by the user, and provides the user with useful information.

[0468] System Overview

[0469] 1. User data entry

[0470] The user provides information about the search target to the system through the terminal's input interface. This interface has keyboard and voice input functions. For example, the user can input information such as "Tokugawa's buried treasure" or "Tsuchinoko discovery."

[0471] 2. Information format conversion

[0472] The terminal converts the information received from the user into an appropriate format and sends it to the server. This format conversion has the function of converting information entered in natural language into JSON format. As a concrete example, the question entered by the user, "Tell me the location of Tokugawa's buried treasure," is converted into the JSON format {"request": "Tell me the location of Tokugawa's buried treasure"}.

[0473] 3. Data Collection

[0474] The server collects relevant data based on the converted data. Data collection methods include web crawling and database querying, and gathers information from the internet and various databases. Specific software used includes Scrapy and Beautiful Soup for web crawling, and SQL and NoSQL query tools for database queries.

[0475] 4. Data Analysis

[0476] The server launches an analysis module to analyze the collected data. This analysis module classifies and organizes the data using natural language processing technology and machine learning algorithms. Specific technologies used are Google Cloud Natural Language and Amazon Comprehend, and machine learning algorithms include TensorFlow and Pandas.

[0477] 5. Providing results

[0478] The server compiles the analysis results into a report and sends it to the device. The device has the functionality to visually display the results. For example, it can use the Google Maps API to display a specific area on a map, providing the results in an easy-to-understand format for the user.

[0479] Specific examples

[0480] Example 1: Searching for Tokugawa's buried treasure

[0481] 1. User data entry

[0482] The user types into the terminal, "Tell me the location of Tokugawa's buried treasure."

[0483] 2. Information format conversion

[0484] The terminal converts this input into the JSON format {"request": "Tell me the location of Tokugawa's buried treasure"}.

[0485] 3. Data Collection

[0486] The server uses Scrapy and SQL queries to collect documents and historical maps related to "Tokugawa buried treasure" from the web and various databases.

[0487] 4. Data Analysis

[0488] The server uses the Google Cloud Natural Language API to analyze literature and data, and identifies "near a certain temple in Tokyo" as a likely search point.

[0489] 5. Providing results

[0490] The server compiles the analysis results into an HTML report and sends it to the device, which then uses the Google Maps API to display the identified location and tell the user, "There is a high possibility that buried treasure is near a certain temple in Tokyo."

[0491] Example 2: Discovering a worm

[0492] 1. User data entry

[0493] The user types into the terminal, "Tell me where Tsuchinoko appears."

[0494] 2. Information format conversion

[0495] The device converts this input into the JSON format {"request": "Tell me where the Tsuchinoko appears"}.

[0496] 3. Data Collection

[0497] The server uses Beautiful Soup and SQL queries to gather information about sightings and legends related to "Tsuchinoko" from the web and databases.

[0498] 4. Data Analysis

[0499] The server uses the Google Cloud Natural Language API to analyze the sightings and identify the "mountainous areas of Gifu Prefecture" as a likely area.

[0500] 5. Providing results

[0501] The server compiles the analysis results into a report and sends it to the device. The device then displays the location identified using the Google Maps API, informing the user that "there have been many reports of Tsuchinoko sightings in the mountainous areas of Gifu Prefecture."

[0502] This allows the user to efficiently and accurately search for buried treasure, unknown ruins, and hidden treasures.

[0503] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0504] Step 1:

[0505] The terminal receives input from the user. The user enters information about the search target into the terminal's interface. This information is written in natural language and includes phrases such as "Tokugawa's buried treasure" and "Tsuchinoko discovery." The input is raw data that the terminal prepares for the next processing step.

[0506] Step 2:

[0507] The device converts the received user input information into JSON format. For example, the input "Tell me where Tokugawa's buried treasure is" is converted into the JSON format {"request": "Tell me where Tokugawa's buried treasure is"}. This conversion process enables subsequent data collection and analysis to be carried out efficiently. The converted data is then sent to the server via a dedicated API.

[0508] Step 3:

[0509] The server collects related information based on the JSON-formatted data received from the device. The server uses web crawling technologies (such as Scrapy and Beautiful Soup) to collect data from Internet documents, news articles, blog posts, etc. It also uses database query technologies to retrieve related information from various databases. The collected data is primarily text data related to the subject the user is searching for.

[0510] Step 4:

[0511] The server analyzes the data collected by the data collection method. It launches an analysis module (using TensorFlow or Pandas) to classify and organize the collected data. At this stage, natural language processing technology (Google Cloud Natural Language or Amazon Comprehend) is used to extract important keywords and context. For example, "near a certain temple in Tokyo" is identified as the search target. This analysis process provides search hints and identifies highly likely search points.

[0512] Step 5:

[0513] The server creates a report of the analysis results to provide to the user. This report is created in HTML format and includes the locations identified as analysis results and related information. The report is presented to the user using map display means (Google Maps API) and graph display means for easy visual viewing. The server sends the completed report to the terminal.

[0514] Step 6:

[0515] The device displays the report received from the server to the user. For example, it uses the Google Maps API to display the identified area on a map, visually showing the analysis results. It also provides information in text format, such as "There is a high possibility that buried treasure is found near a certain temple in Tokyo." The user can then carry out specific search activities based on this information.

[0516] This system allows users to efficiently and accurately search for undiscovered buried treasures, unknown ruins and hidden treasures.

[0517] (Application example 1)

[0518] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0519] In modern times, searching for undiscovered buried treasures, unknown ruins, and hidden treasures is difficult because it is difficult to find useful information from the vast amount of information. In particular, because the amount of information is vast and diverse, there is a demand for a system that can efficiently and accurately collect and analyze related information. In addition, it is necessary to visually display the analysis results so that users can easily understand them intuitively. The purpose of this invention is to solve these problems.

[0520] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0521] In this invention, the server includes an input means for a user to input information about a search target, a data collection means for collecting related data from the Internet or a database based on the information received from the input means, a data analysis means for analyzing the data collected by the data collection means and identifying search hints and highly likely search points, and a result display means for visually displaying the analysis results using maps and graphs, thereby enabling users to efficiently search and intuitively understand the information they need.

[0522] A "user" is a person who uses a terminal to input search target information into the system and receive the analysis results.

[0523] The "input means" is an interface through which the user provides information about the search target to the system.

[0524] The "data collection means" is a device or function that collects related data from the Internet or a database based on the information received from the input means.

[0525] The "data analysis means" is a device or function that uses and analyzes the data collected by the data collection means to identify search hints and highly accurate search points.

[0526] The "result providing means" is a device or function that provides the user with the information identified by the data analysis means.

[0527] The "result display means" is a device or function that visually displays the analysis results using maps or graphs.

[0528] "Natural language processing technology" is a technology for analyzing collected data and classifying and organizing important information related to the search target.

[0529] A "machine learning algorithm" is a mathematical method or model used to identify locations and relevant information to search for in the data being analyzed.

[0530] The "geographical display means" is a device or function for displaying the analysis results for the identified location on a map.

[0531] The "map display means" is a means for visually displaying the analysis results for the identified location on a map.

[0532] The "graph display means" is a means for visually displaying the analysis results for the identified location in a graph.

[0533] Hereinafter, embodiments of the present invention will be described in detail.

[0534] System Overview

[0535] The system components are:

[0536] An input means for the user to input information about the search target

[0537] A data collection means for collecting related data from the Internet or a database based on the information received from the input means.

[0538] a data analysis means for analyzing the data collected by the data collection means and identifying search hints and highly accurate search points;

[0539] a result providing means for providing the information identified by the data analysis means to a user;

[0540] A means of visually displaying analysis results using maps and graphs

[0541] User Data Entry

[0542] Users input information about the search target into the system through the smartphone interface. For example, they might enter a search target such as "Tell me the location of Tokugawa's buried treasure" or "Tell me where Tsuchinoko appears."

[0543] Data collection

[0544] The device sends the input information to a server, which then uses that information to collect relevant data from the internet and databases. Techniques used include HTTP requests using the requests library and web scraping using BeautifulSoup. This allows the server to collect data such as related literature, news articles, and historical maps.

[0545] Data analysis

[0546] The server launches a module to analyze the collected data. This module uses natural language processing techniques and machine learning algorithms (e.g., geographic information analysis using the geopy library and classification and organization using the scikit-learn library) to analyze the collected data. Specifically, it classifies the locations and clues to be explored and identifies specific areas.

[0547] Results provision and display

[0548] The analysis results are displayed visually. The server compiles the analysis results into a report format and sends it to the terminal. The terminal then displays the results to the user visually in the form of a map or graph, using the folium library to display a map and indicate related search points. This allows the user to intuitively understand the analysis results.

[0549] Specific examples

[0550] Example 1: Searching for Tokugawa's buried treasure

[0551] 1. User data entry

[0552] A user types in "Tell me the location of Tokugawa's buried treasure."

[0553] 2. Data Collection

[0554] The terminal sends the information to a server, which collects documents and historical maps related to the "Tokugawa buried treasure."

[0555] 3. Data Analysis

[0556] The server's analysis module analyzes the data and identifies the location as "near a certain temple in Tokyo."

[0557] 4. Providing and displaying results

[0558] The server sends the analysis results, and the device displays a map saying, "There is a high possibility that buried treasure is near a certain temple in Tokyo."

[0559] Example 2: Discovery of the earthworm

[0560] 1. User data entry

[0561] The user types, "Tell me where Tsuchinoko appears."

[0562] 2. Data Collection

[0563] The terminal sends information to a server, which collects sightings and legends about "Tsuchinoko."

[0564] 3. Data Analysis

[0565] The server's analysis module analyzes the data and identifies the "mountainous area of ​​Gifu Prefecture."

[0566] 4. Providing and displaying results

[0567] The server sends the analysis results, and the device displays on a map, "There have been many sightings of Tsuchinoko in the mountainous areas of Gifu Prefecture."

[0568] Prompt Sentence Examples

[0569] "Design a system to collect and analyze information on the search for undiscovered buried treasure and unknown ruins and treasures, and identify search points. Also, provide an interface that users can use easily on their smartphones."

[0570] The above is a specific embodiment for carrying out the present invention. This system allows the user to perform searches efficiently and intuitively.

[0571] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0572] Step 1:

[0573] The user enters information about the search target.

[0574] Input: The user enters the search target (e.g., "Tokugawa's buried treasure" or "Tsuchinoko") into the smartphone's input interface.

[0575] Output: User input is received and prepared at the terminal.

[0576] Step 2:

[0577] The terminal transmits the input information to the server.

[0578] Input: The device uses the search target information entered by the user.

[0579] Output: The server starts collecting data based on the information received from the device.

[0580] Step 3:

[0581] The server collects the relevant data.

[0582] Input: The server gathers data from the internet and databases based on user input, using the requests library to send web requests and BeautifulSoup to parse web pages.

[0583] Output: Collected data (literatures, papers, news articles, historical maps, etc.) are stored on a server.

[0584] Step 4:

[0585] The server analyzes the data.

[0586] Input: Based on the collected data, the server uses natural language processing techniques and machine learning algorithms (e.g. geopy and scikit-learn).

[0587] Output: As a result of the analysis, the server identifies search hints and highly likely search points.

[0588] Step 5:

[0589] The server compiles the analysis results into a report.

[0590] Input: Use the results of data analysis (e.g., identified locations and hints).

[0591] Output: Analysis results are generated in a report format (text, images, maps, etc.).

[0592] Step 6:

[0593] The server sends the analysis results to the device.

[0594] Input: Use the analysis results summarized in report format.

[0595] Output: The analysis results are sent to the terminal.

[0596] Step 7:

[0597] The device visually displays the analysis results.

[0598] Input: Use the analysis results (report format) received from the server.

[0599] Output: The terminal visually displays the analysis results on maps and graphs, providing them to the user in an intuitive manner. Specifically, the folium library is used to display markers indicating specific points on a map.

[0600] The above are the processing steps of the program for the system that realizes the application example of the present invention.

[0601] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0602] This invention is a system for efficiently searching for undiscovered buried treasure and unknown ruins and treasures. This system collects and analyzes related data based on information about the search target entered by the user, provides useful information to the user, and optimizes the way results are presented by recognizing the user's emotional state. The program processing of this system is explained below in natural language.

[0603] System Overview

[0604] 1. User data entry

[0605] The user provides information about the search target to the system through the input interface of the terminal. For example, they input information such as "Tokugawa's buried treasure" or "Tsuchinoko discovery." The system uses an emotion engine to recognize the user's emotional state at the time of input and associates it with the data.

[0606] 2. Data Collection

[0607] The terminal converts the information received from the user into an appropriate format and sends it to the server. The server then collects relevant data based on the received information. Web crawling and database query techniques are used to collect data, searching data sources such as literature, papers, news articles, and historical maps based on specified keywords.

[0608] 3. Data Analysis

[0609] The server launches an analysis module to analyze the collected data. The analysis module uses natural language processing technology and machine learning algorithms to classify and organize the collected data. It then identifies search locations and hints and calculates their reliability. The analysis results are provided taking into account the user's emotional state.

[0610] 4. Providing results

[0611] The server compiles the analysis results into a report and sends it to the device. The device then presents the results visually to the user in an intuitively understandable format, such as a map or graph. The device can also adjust the way the results are presented depending on the user's emotional state.

[0612] Specific examples

[0613] Example 1: Searching for Tokugawa's buried treasure

[0614] 1. Data Entry

[0615] A user types into a terminal, "Tell me the location of Tokugawa's buried treasure." At this time, the emotion engine recognizes the user's emotional state (e.g., excitement, anticipation, anxiety, etc.) and attaches it to the data.

[0616] 2. Data Collection

[0617] The terminal sends information to a server, which then collects documents, papers, news articles, and historical maps related to "Tokugawa buried treasure" from the Internet and various databases.

[0618] 3. Data Analysis

[0619] The server's analysis module analyzes the collected data and extracts historical geographical information and records related to buried treasure based on a specific area. For example, the area around a certain temple in Tokyo is identified as a likely search point.

[0620] The system takes into account the user's emotional state and adjusts the reliability of the data and the order in which the analysis results are presented.

[0621] 4. Providing results

[0622] The server compiles the analysis results into a report and sends it to the device, which then displays to the user, "There is a high possibility that buried treasure is near a certain temple in Tokyo." The display method is adjusted based on the user's emotional state.

[0623] Example 2: Discovering a worm

[0624] 1. Data Entry

[0625] The user types into the terminal, "Tell me where Tsuchinoko appears." At this time, the emotion engine recognizes the user's emotional state and attaches it to the data.

[0626] 2. Data Collection

[0627] The terminal sends information to a server, which then collects information about sightings, legends, and data on the ecology of "Tsuchinoko" from the Internet and various documents.

[0628] 3. Data Analysis

[0629] The server's analysis module analyzes the collected data and identifies areas with a high number of Tsuchinoko sightings. For example, the mountainous areas of Gifu Prefecture are identified as likely areas.

[0630] The emotional state of the user is taken into account and influenced to effectively present the analysis results.

[0631] 4. Providing results

[0632] The server compiles the analysis results into a report and sends it to the device. The device then displays to the user, "There have been many reports of Tsuchinoko sightings in the mountainous areas of Gifu Prefecture." The optimal display method is selected based on the user's emotional state.

[0633] In this way, the system of the present invention not only provides the exact location and highly accurate search points of undiscovered buried treasures, unknown ruins, and hidden treasures, but also recognizes the user's emotional state and optimizes the way results are presented, thereby increasing the motivation and efficiency of the search.

[0634] The processing flow will be explained below.

[0635] Step 1: The user operates the device and inputs information about the search target. Specifically, the user enters the name of the search target and related information, such as "Tokugawa's buried treasure" or "Tsuchinoko discovery," into the device's input interface, and clicks the send button.

[0636] Step 2: The device receives the information entered by the user. At the same time, the device activates an emotion engine to recognize the user's emotional state. For example, it analyzes the text, facial expressions, and voice of the user when they enter information to determine whether they are excited or anxious.

[0637] Step 3: The device converts the received information and the recognized emotional state into an appropriate format (e.g., JSON format) and then sends it to the server. The transmitted data includes information about the search target and the user's emotional state.

[0638] Step 4: The server analyzes the information received from the device and extracts keywords related to the search target. Specifically, it activates the data collection module based on keywords such as "Tokugawa buried treasure" and "Tsuchinoko."

[0639] Step 5: The server's data collection module collects relevant information from the Internet and databases, using web crawling techniques and APIs to search data sources such as literature, papers, news articles, and historical maps.

[0640] Step 6: The server filters the collected data and keeps only the most reliable information, which includes assessing the reliability of the data and removing duplicate data.

[0641] Step 7: The server's data analysis module analyzes the collected data. This analysis involves using natural language processing techniques to categorize and organize relevant information, and machine learning algorithms to identify locations and specific clues to explore. The user's emotional state is also reflected as part of the analysis.

[0642] Step 8: The server validates the analysis results and calculates a confidence score, which evaluates the probability that the identified location is actually related to the search target. The server also takes into account the user's emotional state and adjusts the response, for example by providing more detailed information if the user is excited.

[0643] Step 9: The server compiles the analysis results into a report, which includes the identified locations, hints, and a confidence rating.

[0644] Step 10: The server sends the generated report to the terminal, which then displays the received report to the user.

[0645] Step 11: The device visualizes the report and presents it to the user in an intuitive format, such as through maps or graphs. Based on the user's emotional state, the presentation of the results is adjusted accordingly. For example, if the user is feeling anxious, additional information to reassure them is added.

[0646] The above is the processing flow of the system that combines the emotion engine and the specific operations that are performed at each step.

[0647] Example 2

[0648] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0649] This invention relates to a system for efficiently searching for undiscovered buried treasures and unknown ruins and treasures. Traditionally, the process of inputting search information, collecting related data, analyzing it, and providing results has often been done manually, which requires time and effort. Furthermore, the results provided may not be appropriate based on the user's emotional state, potentially reducing the user's motivation. Therefore, a system is needed that automates the search process and optimizes the way results are presented based on the user's emotional state.

[0650] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes an input means through which a user inputs information about a search target, a data collection means that collects related data from the Internet or a database based on the information received from the input means, a data analysis means that analyzes the data collected by the data collection means and identifies search hints and highly likely search points, a result providing means that provides the user with the information identified by the data analysis means, and an emotion recognition means that recognizes the user's emotional state and optimizes the method of presenting results accordingly. This makes it possible to automate the search process and provide optimal results according to the user's emotional state.

[0651] The "input means" is a means by which a user inputs information about a search target into the system.

[0652] "Data collection means" refers to a means of collecting related data from the Internet or a database based on the input information.

[0653] "Data analysis means" is a means for analyzing collected data and identifying search hints and highly likely search points.

[0654] The "result providing means" is a means for providing the information identified by the data analysis means to the user.

[0655] An "emotion recognition means" is a means for recognizing the user's emotional state and optimizing the way results are presented accordingly.

[0656] This invention is a system for efficiently searching for undiscovered buried treasures and unknown ruins and treasures. A specific method for implementing this system will be described.

[0657] System configuration

[0658] This system consists of a user terminal and a server.

[0659] 1. User Device

[0660] The user device has an interface for inputting information about the search target. Input methods include a keyboard, touch screen, and voice input. To recognize the user's emotional state, the device is equipped with an emotion engine (e.g., Emotion AI SDK).

[0661] 2. Server

[0662] The server collects data based on the information sent from the user's device, analyzes it, and provides the results. Data collection methods include web scraping tools using Python (e.g., BeautifulSoup, Scrapy) and database query technologies (e.g., SQL). Analysis methods include natural language processing technologies (e.g., NLTK, SpaCy) and machine learning algorithms (e.g., scikit-learn, TensorFlow). Results are provided using map displays (e.g., Leaflet.js) and graph displays (e.g., D3.js).

[0663] System operation explanation

[0664] 1. Data Entry

[0665] The user uses the device's input interface to input information about the search target, for example, "Tell me the location of Tokugawa's buried treasure." At this time, the emotion engine recognizes the user's emotional state at the time of input and adds it to the data.

[0666] 2. Data Collection

[0667] The device converts the information into an appropriate format (e.g., JSON format) and sends it to the server. The server processes the received data and generates queries to collect relevant data. Web crawling techniques (e.g., Scrapy) and database query techniques (e.g., SQL) are used to collect literature, papers, news articles, historical maps, etc. from the Internet and various databases.

[0668] 3. Data Analysis

[0669] The server's analysis module runs and analyzes the collected data. It uses natural language processing techniques (e.g., NLTK, SpaCy) to classify the data, and machine learning algorithms (e.g., scikit-learn, TensorFlow) to identify locations and hints related to the search target and calculate their reliability. Furthermore, it adjusts the order and content of the results based on the user's emotional state.

[0670] 4. Providing results

[0671] The server compiles the analysis results into a report and sends it to the device. The device then presents the results to the user in a visually easy-to-understand format, such as an interactive map (e.g., Leaflet.js) or a graph display (e.g., D3.js). The display method is adjusted according to the user's emotional state to provide an optimal user experience.

[0672] Specific examples

[0673] Example 1: Searching for Tokugawa's buried treasure

[0674] 1. Data Entry

[0675] The user types into the terminal, "Tell me the location of Tokugawa's buried treasure." The emotion engine recognizes the user's emotional state, such as excitement or anticipation, and adds it to the data.

[0676] 2. Data Collection

[0677] The device converts the information into JSON format and sends it to a server that collects documents, papers, news articles, historical maps, and other information related to the "Tokugawa buried treasure."

[0678] 3. Data Analysis

[0679] The server's analysis module tokenizes the data and organizes it into categories. For example, it may identify "near a certain temple in Tokyo" as an analysis result.

[0680] The order and content of the analysis results are adjusted taking into account the user's emotional state.

[0681] 4. Providing results

[0682] The server compiles the analysis results into a report and sends it to the device. The device then displays the results as an interactive map, showing users that "there is a high possibility that buried treasure is near a certain temple in Tokyo." The display style uses bright colors to match the user's expectations.

[0683] Example 2: Discovering a worm

[0684] 1. Data Entry

[0685] The user types into the device, "Tell me where Tsuchinoko appears." The emotion engine recognizes the user's excitement or surprise and adds it to the data.

[0686] 2. Data Collection

[0687] The device converts the information into a suitable format and sends it to a server, which collects data, including sightings and legends, from the internet and literature.

[0688] 3. Data Analysis

[0689] The analysis module analyzes the collected sighting information and identifies the "mountainous areas of Gifu Prefecture" as a likely area.

[0690] The display order and content are adjusted based on the user's emotional state.

[0691] 4. Providing results

[0692] The server compiles the analysis results into a report and sends it to the device. The device then visually displays the results, saying, "There have been many reports of Tsuchinoko sightings in the mountainous areas of Gifu Prefecture." The display style uses a subdued color scheme to avoid amplifying the sense of surprise.

[0693] In this way, the system of the present invention can consistently automate the process from collecting data on search targets to analyzing them and providing the results, and can present the results in a form optimized based on the user's emotional state.

[0694] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0695] Step 1:

[0696] The user inputs information about the search target using the terminal's input interface. The input information includes a prompt sentence, such as "Tell me the location of Tokugawa's buried treasure." The emotion engine uses voice tone and facial expression recognition technology to recognize the user's emotional state (e.g., excitement, anticipation, anxiety) and adds it to the input data. The input at this time is the prompt sentence and emotional information. The output is formed as a single dataset.

[0697] Step 2:

[0698] The device converts the data received from the user into an appropriate format (e.g., JSON) and sends it to the server. A programming language such as Python is used as the conversion method. The input is a dataset containing a prompt sentence and emotion information, and the output is formatted data. Specifically, the format conversion function is called, the data is converted into JSON format, and then sent to the server.

[0699] Step 3:

[0700] The server processes the received formatted data and generates queries to collect relevant data. It uses web crawling techniques (e.g., Scrapy) or database query techniques (e.g., SQL) to collect data. The input is the formatted data, and the output is the collected relevant data. Specifically, it calls the query generation function to collect the required data from the Internet or a database.

[0701] Step 4:

[0702] The server launches an analysis module (e.g., NLTK, scikit-learn) to analyze the collected data. The analysis module tokenizes the collected data using natural language processing techniques and classifies the data using machine learning algorithms. The input is the collected relevant data, and the output is the analysis results. Specific operations include tokenizing text and running a category classification algorithm.

[0703] Step 5:

[0704] The server compiles the analysis results into a report, which includes the location of the search target, related hints, and confidence data. It also configures the content to be presented in an easy-to-interpret manner based on the user's emotional state. The input is the analysis results and the user's emotional information, and the output is data in report format. Specific operations include calling a report generation function, organizing the data, and adjusting the display style according to the user's emotional state.

[0705] Step 6:

[0706] The server sends the analysis results in report format to the terminal. The terminal launches a map display (e.g., Leaflet.js) or graph display (e.g., D3.js) module to visually display the results to the user. The input is data in report format, and the output is the visually displayed results. Specific operations include launching the map display module and graph display module, and selecting the optimal display method based on the user's emotional state.

[0707] Through the above steps, this system efficiently and effectively searches for undiscovered buried treasures and unknown ruins and treasures, and provides optimal information to users.

[0708] (Application example 2)

[0709] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0710] Conventional search systems provide information without considering the user's emotional state, which can result in insufficient results. Furthermore, when detecting anomalies within a factory, providing information without considering the emotional state of the manager can reduce the effectiveness and efficiency of emergency responses. To solve these problems, technology is needed that recognizes the emotional state of the user or manager and optimizes the way information is presented based on that state.

[0711] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0712] In this invention, the server includes an input means for a user to input information about a search target, a data collection means for collecting related data from the Internet or a database based on the information received from the input means, a data analysis means for analyzing the data collected by the data collection means and identifying search hints and highly likely search points, a result provision means for providing the user with the information identified by the data analysis means, and an emotion recognition means for recognizing the user's emotional state using emotion recognition technology and adjusting the content presented by the provision means based on the user's emotional state. This makes it possible to provide optimal information according to the user's emotional state, thereby improving the effectiveness and efficiency of searches and anomaly detection.

[0713] The "input means by which the user inputs information about the search target" refers to a device or interface that allows the user to input information about the search target.

[0714] "Data collection means for collecting related data from the Internet or databases based on information received from the input means" refers to a function or device for collecting related information from the Internet or various databases based on information from the input means.

[0715] "Data analysis means for analyzing the data collected by the data collection means and identifying search hints and highly likely search points" refers to a function or device for analyzing collected data, and is used to identify information and points that are useful for searching.

[0716] The "result providing means for providing the user with the information identified by the data analysis means" refers to a function or device for conveying the information found by the data analysis means to the user.

[0717] "Emotion recognition means that recognizes the emotional state of a user using emotion recognition technology and adjusts the content presented by the providing means based on the emotional state of the user" refers to a function or device that recognizes emotions using technology that detects the user's emotions and optimizes the information and format of the information to be provided based on the results.

[0718] "Natural language processing technology" refers to technology that enables computers to understand and process human language.

[0719] A "machine learning algorithm" is a method for learning patterns from collected data and making predictions, classifications, and analyses.

[0720] "Map display means" refers to a function or device that provides information in map format to visually display the analysis results.

[0721] "Graph display means" refers to a function or device that provides information in a graph format to visually display the analysis results.

[0722] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The embodiments for carrying out the present invention will be described in detail below.

[0723] This system includes a terminal for users to input information about the search target, a server for collecting related data from the Internet or a database based on that information, and means for analyzing the collected data and providing the results to the user.

[0724] Hardware and Software Configuration

[0725] 1. Terminal

[0726] The terminal is equipped with an input means for the user to input information about the search target, and may be, for example, a smartphone, tablet, or PC. Software for using emotion recognition technology (e.g., emotion recognition API) is installed on this terminal.

[0727] 2. Server

[0728] The server includes the following means:

[0729] Data collection means: Based on the information received from the terminal, relevant data is collected from the Internet and databases. Specifically, HTTP requests are used to obtain the necessary data from various data sources (literature, papers, news articles, etc.).

[0730] Data analysis methods: Analyze the collected data by using natural language processing techniques (e.g., TextBlob) and machine learning algorithms (e.g., IsolationForest) to classify and organize the data and identify search hints and high-confidence search points.

[0731] Result presentation means: The analysis results are presented to the user. The results are adjusted based on the user's emotional state and visually presented to the user using map and graph display means.

[0732] 3. Emotion recognition means

[0733] The emotion recognition means recognizes the emotional state of the user when they input data and attaches that information to the data. This emotion recognition technology utilizes an emotion analysis library such as TextBlob.

[0734] Example of a system

[0735] Example 1: Identifying the discovery location

[0736] The user types into the terminal, "I want you to identify the abnormal part of the machine." At this time, emotion recognition technology is used to analyze the user's emotional state and recognize that the situation is anxious or urgent. The server uses this information to collect related data from the Internet and databases, and identifies the abnormal part using a machine learning algorithm. The results are then presented to the administrator in the form of a map or graph.

[0737] Example 2: Visualization of location information

[0738] The server analyzes the collected data and detects abnormalities in a specific area (e.g., a specific section of a factory). The analysis results are highlighted using map display means or graph display means depending on the manager's emotional state (e.g., if the level of urgency is high).

[0739] Prompt Sentence Examples

[0740] By entering the following prompts into the generative AI model, you can obtain information that will help with system construction and analysis.

[0741] Example prompt:

[0742] Please build a system to collect data within the factory and identify abnormalities as follows:

[0743] Collects sensor data (temperature, vibration, sound, log)

[0744] Uses an anomaly detection algorithm (Isolation Forest)

[0745] Uses Emotion Recognition (TextBlob)

[0746] The above is an embodiment of the present invention, which makes it possible to provide optimal information according to the emotional state of the user, thereby improving the effectiveness and efficiency of search and anomaly detection.

[0747] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0748] Step 1:

[0749] The user inputs information about the search target through the device's input means. For example, the user inputs information such as "Please identify the abnormal part of the machine." At this time, the device uses emotion recognition technology to analyze the user's emotional state and recognizes that the situation is anxious or urgent. The input is in text format, and the analysis result of the emotional state is also output at the same time. This information is sent to the server in the next step.

[0750] Step 2:

[0751] The device sends data to the server based on the information entered in the previous step. The data sent includes the user's input information and emotional state. The server receives this transmitted data and activates the data collection method. Specifically, based on this information, it uses HTTP requests to collect necessary data such as related literature, papers, news articles, and sensor data from the Internet and various databases. The collected data is preprocessed and converted into a format suitable for analysis.

[0752] Step 3:

[0753] The server analyzes the data collected by the data collection method. It uses natural language processing technology (TextBlob) and a machine learning algorithm (IsolationForest) to analyze the data. First, it uses TextBlob to perform text analysis of the collected data, extracting and classifying relevant information. At the same time, it applies IsolationForest to the quantitative data from the sensor to identify abnormalities. The analysis results are output in a format that takes into account the emotion recognition results along with their reliability.

[0754] Step 4:

[0755] The server creates a report to provide to the user based on the results obtained by the data analysis means. Map display means and graph display means are used to provide the results. If the analysis results indicate an abnormality in a specific area or machine, the information is visually displayed in map or graph format. Furthermore, the way in which the results are presented is adjusted according to the user's emotional state (level of urgency or anxiety). For example, if the level of urgency is high, the result is highlighted and a detailed explanation is added.

[0756] Step 5:

[0757] The server sends a final report to the terminal. The terminal receives this report and displays it to the user. The user can then review the displayed report and take any necessary action, such as making arrangements to quickly address any identified abnormalities. This process allows the user to receive optimal information based on their emotional state, improving the efficiency of search and anomaly detection.

[0758] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0759] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0760] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0761] [Third embodiment]

[0762] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0763] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0764] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0765] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0766] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0767] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0768] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0769] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0770] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0771] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0772] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0773] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0774] This invention is a system for efficiently searching for undiscovered buried treasure and unknown ruins and treasures. This system collects and analyzes related data based on information about the search target entered by the user, and provides useful information to the user. Below, the processing of the program of this system is explained in natural language.

[0775] System Overview

[0776] 1. User data entry

[0777] The user provides information about the search target to the system through the input interface of the terminal. For example, the user inputs information such as "Tokugawa's buried treasure" or "Tsuchinoko discovery."

[0778] 2. Data Collection

[0779] The terminal converts the information received from the user into an appropriate format and sends it to the server. The server then collects related data based on the received information. Web crawling and database query techniques are used to collect related information such as literature, papers, news articles, and historical maps from the Internet and various databases.

[0780] 3. Data Analysis

[0781] The server launches an analysis module to analyze the collected data. The analysis module uses natural language processing technology and machine learning algorithms to classify and organize the collected data. It then identifies search locations and clues and calculates their reliability.

[0782] 4. Providing results

[0783] The server compiles the analysis results into a report format and sends it to the terminal, which then visually displays the results to the user in an intuitive, easy-to-understand format, such as using maps or graphs.

[0784] Specific examples

[0785] Example 1: Searching for Tokugawa's buried treasure

[0786] 1. Data Entry

[0787] The user types into the terminal, "Tell me the location of Tokugawa's buried treasure."

[0788] 2. Data Collection

[0789] The terminal sends information to a server, which then collects documents, papers, news articles, and historical maps related to "Tokugawa buried treasure" from the Internet and various databases.

[0790] 3. Data Analysis

[0791] The server's analysis module analyzes the collected data and extracts historical geographical information and records related to buried treasure based on a specific area. For example, the area around a certain temple in Tokyo is identified as a likely search point.

[0792] 4. Providing results

[0793] The server compiles the analysis results into a report and sends it to the device, which then displays to the user, "There is a high possibility that buried treasure is near a certain temple in Tokyo."

[0794] Example 2: Discovering a worm

[0795] 1. Data Entry

[0796] The user types into the terminal, "Tell me where Tsuchinoko appears."

[0797] 2. Data Collection

[0798] The terminal sends information to a server, which then collects information about sightings, legends, and data on the ecology of "Tsuchinoko" from the Internet and various documents.

[0799] 3. Data Analysis

[0800] The server's analysis module analyzes the collected data and identifies areas with a high number of Tsuchinoko sightings. For example, the mountainous areas of Gifu Prefecture are identified as likely areas.

[0801] 4. Providing results

[0802] The server compiles the analysis results into a report and sends it to the device, which then displays to the user, "There have been many reports of Tsuchinoko sightings in the mountainous areas of Gifu Prefecture."

[0803] In this way, the system of the present invention enables users to conduct efficient searches by providing the exact locations and highly accurate search points of undiscovered buried treasures, unknown ruins, and hidden treasures.

[0804] The processing flow will be explained below.

[0805] Step 1: The user operates the device and inputs information about the search target. Specifically, the user enters the name and related information of the buried treasure or legendary treasure they are searching for in the input form, and clicks the submit button.

[0806] Step 2: The terminal receives the information entered by the user, converts this information into an appropriate format (e.g., JSON format), and then sends it to the server.

[0807] Step 3: The server analyzes the information received from the device and extracts keywords related to the search target. These keywords are the basis for data collection.

[0808] Step 4: The server's data collection module starts up and collects relevant information from the Internet and databases, using web crawling techniques and APIs to search data sources such as literature, papers, news articles, and historical maps based on the specified keywords.

[0809] Step 5: The server filters the collected data and retains only the most reliable information, which includes assessing the reliability of the data and removing duplicate data.

[0810] Step 6: The server's data analysis module analyzes the collected data, using natural language processing techniques to classify and organize relevant information, and machine learning algorithms to identify search locations and specific clues.

[0811] Step 7: The server validates the analysis results and calculates a confidence score, which assesses the probability that the identified location is actually related to the search target.

[0812] Step 8: The server compiles the analysis results into a report, which includes the identified locations, hints, and a confidence rating.

[0813] Step 9: The server sends the generated report to the terminal, which then displays the received report to the user.

[0814] Step 10: The terminal visualizes the report and presents it to the user in an intuitive format, such as a map or graph display, so the user can check the results and plan their next exploration.

[0815] The above is the processing flow of the system and the specific operations performed at each step.

[0816] Example 1

[0817] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0818] Currently, the search for undiscovered buried treasures, unknown ruins, and hidden treasures requires a lot of time and resources, and the success rate is low. Traditional methods often rely on manual information collection and analysis, which lack accuracy and efficiency, resulting in a large amount of search effort. This makes it difficult for explorers to quickly and efficiently obtain useful information.

[0819] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0820] In this invention, the server includes an input means for a user to input information about a search target, a format conversion means for converting the information received from the input means into JSON format data and transmitting it to the server, a data collection means for collecting related data from the Internet or a database based on the data generated by the format conversion means, a data analysis means for analyzing the data collected by the data collection means and identifying search hints and highly likely search points using natural language processing technology and machine learning algorithms, and a result provision means for providing the user with the information identified by the data analysis means. This makes it possible to efficiently and accurately collect information about the search target, analyze it, and provide the results, thereby increasing the success rate of the search.

[0821] A "user" is an entity that inputs information about a search target into the system and receives results based on that information.

[0822] "Input means" refers to an interface through which a user inputs information about a search target into the system.

[0823] The "format conversion means" is a function that converts information received from the user into JSON format data and sends it to the server.

[0824] The "data collection means" is a function that collects related information from the Internet and databases based on the data generated by the format conversion means.

[0825] "Data analysis means" is a function that analyzes collected data, organizes and classifies information using natural language processing technology and machine learning algorithms, and identifies search hints and highly likely search points.

[0826] The "result providing means" is a function for providing the information identified by the data analysis means to the user.

[0827] "Natural language processing technology" is a technology for analyzing text data and understanding meaning and relationships.

[0828] A "machine learning algorithm" is an algorithm that learns from large amounts of data, finds patterns, and makes predictions and classifications.

[0829] "Search target" refers to undiscovered buried treasure, unknown ruins, or hidden treasures that the user wishes to discover or identify.

[0830] The "JSON format" is a lightweight data exchange format for representing data in a structured manner.

[0831] The "Internet" is an information and communications network that connects computer networks around the world.

[0832] A "database" is a system for efficiently managing and searching data.

[0833] "Search hints" refer to important clues or information about the subject of the search.

[0834] A "high probability search point" refers to a location that is determined to be highly likely to contain the search target.

[0835] This invention is a system for efficiently searching for undiscovered buried treasures and unknown ruins and treasures. This system collects and analyzes related data based on information about the search target entered by the user, and provides the user with useful information.

[0836] System Overview

[0837] 1. User data entry

[0838] The user provides information about the search target to the system through the terminal's input interface. This interface has keyboard and voice input functions. For example, the user can input information such as "Tokugawa's buried treasure" or "Tsuchinoko discovery."

[0839] 2. Information format conversion

[0840] The terminal converts the information received from the user into an appropriate format and sends it to the server. This format conversion has the function of converting information entered in natural language into JSON format. As a concrete example, the question entered by the user, "Tell me the location of Tokugawa's buried treasure," is converted into the JSON format {"request": "Tell me the location of Tokugawa's buried treasure"}.

[0841] 3. Data Collection

[0842] The server collects relevant data based on the converted data. Data collection methods include web crawling and database querying, and gathers information from the internet and various databases. Specific software used includes Scrapy and Beautiful Soup for web crawling, and SQL and NoSQL query tools for database queries.

[0843] 4. Data Analysis

[0844] The server launches an analysis module to analyze the collected data. This analysis module classifies and organizes the data using natural language processing technology and machine learning algorithms. Specific technologies used are Google Cloud Natural Language and Amazon Comprehend, and machine learning algorithms include TensorFlow and Pandas.

[0845] 5. Providing results

[0846] The server compiles the analysis results into a report and sends it to the device. The device has the functionality to visually display the results. For example, it can use the Google Maps API to display a specific area on a map, providing the results in an easy-to-understand format for the user.

[0847] Specific examples

[0848] Example 1: Searching for Tokugawa's buried treasure

[0849] 1. User data entry

[0850] The user types into the terminal, "Tell me the location of Tokugawa's buried treasure."

[0851] 2. Information format conversion

[0852] The terminal converts this input into the JSON format {"request": "Tell me the location of Tokugawa's buried treasure"}.

[0853] 3. Data Collection

[0854] The server uses Scrapy and SQL queries to collect documents and historical maps related to "Tokugawa buried treasure" from the web and various databases.

[0855] 4. Data Analysis

[0856] The server uses the Google Cloud Natural Language API to analyze literature and data, and identifies "near a certain temple in Tokyo" as a likely search point.

[0857] 5. Providing results

[0858] The server compiles the analysis results into an HTML report and sends it to the device, which then uses the Google Maps API to display the identified location and tell the user, "There is a high possibility that buried treasure is near a certain temple in Tokyo."

[0859] Example 2: Discovering a worm

[0860] 1. User data entry

[0861] The user types into the terminal, "Tell me where Tsuchinoko appears."

[0862] 2. Information format conversion

[0863] The device converts this input into the JSON format {"request": "Tell me where the Tsuchinoko appears"}.

[0864] 3. Data Collection

[0865] The server uses Beautiful Soup and SQL queries to gather information about sightings and legends related to "Tsuchinoko" from the web and databases.

[0866] 4. Data Analysis

[0867] The server uses the Google Cloud Natural Language API to analyze the sightings and identify the "mountainous areas of Gifu Prefecture" as a likely area.

[0868] 5. Providing results

[0869] The server compiles the analysis results into a report and sends it to the device. The device then displays the location identified using the Google Maps API, informing the user that "there have been many reports of Tsuchinoko sightings in the mountainous areas of Gifu Prefecture."

[0870] This allows the user to efficiently and accurately search for buried treasure, unknown ruins, and hidden treasures.

[0871] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0872] Step 1:

[0873] The terminal receives input from the user. The user enters information about the search target into the terminal's interface. This information is written in natural language and includes phrases such as "Tokugawa's buried treasure" and "Tsuchinoko discovery." The input is raw data that the terminal prepares for the next processing step.

[0874] Step 2:

[0875] The device converts the received user input information into JSON format. For example, the input "Tell me where Tokugawa's buried treasure is" is converted into the JSON format {"request": "Tell me where Tokugawa's buried treasure is"}. This conversion process enables subsequent data collection and analysis to be carried out efficiently. The converted data is then sent to the server via a dedicated API.

[0876] Step 3:

[0877] The server collects related information based on the JSON-formatted data received from the device. The server uses web crawling technologies (such as Scrapy and Beautiful Soup) to collect data from Internet documents, news articles, blog posts, etc. It also uses database query technologies to retrieve related information from various databases. The collected data is primarily text data related to the subject the user is searching for.

[0878] Step 4:

[0879] The server analyzes the data collected by the data collection method. It launches an analysis module (using TensorFlow or Pandas) to classify and organize the collected data. At this stage, natural language processing technology (Google Cloud Natural Language or Amazon Comprehend) is used to extract important keywords and context. For example, "near a certain temple in Tokyo" is identified as the search target. This analysis process provides search hints and identifies highly likely search points.

[0880] Step 5:

[0881] The server creates a report of the analysis results to provide to the user. This report is created in HTML format and includes the locations identified as analysis results and related information. The report is presented to the user using map display means (Google Maps API) and graph display means for easy visual viewing. The server sends the completed report to the terminal.

[0882] Step 6:

[0883] The device displays the report received from the server to the user. For example, it uses the Google Maps API to display the identified area on a map, visually showing the analysis results. It also provides information in text format, such as "There is a high possibility that buried treasure is found near a certain temple in Tokyo." The user can then carry out specific search activities based on this information.

[0884] This system allows users to efficiently and accurately search for undiscovered buried treasures, unknown ruins and hidden treasures.

[0885] (Application example 1)

[0886] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0887] In modern times, searching for undiscovered buried treasures, unknown ruins, and hidden treasures is difficult because it is difficult to find useful information from the vast amount of information. In particular, because the amount of information is vast and diverse, there is a demand for a system that can efficiently and accurately collect and analyze related information. In addition, it is necessary to visually display the analysis results so that users can easily understand them intuitively. The purpose of this invention is to solve these problems.

[0888] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0889] In this invention, the server includes an input means for a user to input information about a search target, a data collection means for collecting related data from the Internet or a database based on the information received from the input means, a data analysis means for analyzing the data collected by the data collection means and identifying search hints and highly likely search points, and a result display means for visually displaying the analysis results using maps and graphs, thereby enabling users to efficiently search and intuitively understand the information they need.

[0890] A "user" is a person who uses a terminal to input search target information into the system and receive the analysis results.

[0891] The "input means" is an interface through which the user provides information about the search target to the system.

[0892] The "data collection means" is a device or function that collects related data from the Internet or a database based on the information received from the input means.

[0893] The "data analysis means" is a device or function that uses and analyzes the data collected by the data collection means to identify search hints and highly accurate search points.

[0894] The "result providing means" is a device or function that provides the user with the information identified by the data analysis means.

[0895] The "result display means" is a device or function that visually displays the analysis results using maps or graphs.

[0896] "Natural language processing technology" is a technology for analyzing collected data and classifying and organizing important information related to the search target.

[0897] A "machine learning algorithm" is a mathematical method or model used to identify locations and relevant information to search for in the data being analyzed.

[0898] The "geographical display means" is a device or function for displaying the analysis results for the identified location on a map.

[0899] The "map display means" is a means for visually displaying the analysis results for the identified location on a map.

[0900] The "graph display means" is a means for visually displaying the analysis results for the identified location in a graph.

[0901] Hereinafter, embodiments of the present invention will be described in detail.

[0902] System Overview

[0903] The system components are:

[0904] An input means for the user to input information about the search target

[0905] A data collection means for collecting related data from the Internet or a database based on the information received from the input means.

[0906] a data analysis means for analyzing the data collected by the data collection means and identifying search hints and highly accurate search points;

[0907] a result providing means for providing the information identified by the data analysis means to a user;

[0908] A means of visually displaying analysis results using maps and graphs

[0909] User Data Entry

[0910] Users input information about the search target into the system through the smartphone interface. For example, they might enter a search target such as "Tell me the location of Tokugawa's buried treasure" or "Tell me where Tsuchinoko appears."

[0911] Data collection

[0912] The device sends the input information to a server, which then uses that information to collect relevant data from the internet and databases. Techniques used include HTTP requests using the requests library and web scraping using BeautifulSoup. This allows the server to collect data such as related literature, news articles, and historical maps.

[0913] Data analysis

[0914] The server launches a module to analyze the collected data. This module uses natural language processing techniques and machine learning algorithms (e.g., geographic information analysis using the geopy library and classification and organization using the scikit-learn library) to analyze the collected data. Specifically, it classifies the locations and clues to be explored and identifies specific areas.

[0915] Results provision and display

[0916] The analysis results are displayed visually. The server compiles the analysis results into a report format and sends it to the terminal. The terminal then displays the results to the user visually in the form of a map or graph, using the folium library to display a map and indicate related search points. This allows the user to intuitively understand the analysis results.

[0917] Specific examples

[0918] Example 1: Searching for Tokugawa's buried treasure

[0919] 1. User data entry

[0920] A user types in "Tell me the location of Tokugawa's buried treasure."

[0921] 2. Data Collection

[0922] The terminal sends the information to a server, which collects documents and historical maps related to the "Tokugawa buried treasure."

[0923] 3. Data Analysis

[0924] The server's analysis module analyzes the data and identifies the location as "near a certain temple in Tokyo."

[0925] 4. Providing and displaying results

[0926] The server sends the analysis results, and the device displays a map saying, "There is a high possibility that buried treasure is near a certain temple in Tokyo."

[0927] Example 2: Discovery of the earthworm

[0928] 1. User data entry

[0929] The user types, "Tell me where Tsuchinoko appears."

[0930] 2. Data Collection

[0931] The terminal sends information to a server, which collects sightings and legends about "Tsuchinoko."

[0932] 3. Data Analysis

[0933] The server's analysis module analyzes the data and identifies the "mountainous area of ​​Gifu Prefecture."

[0934] 4. Providing and displaying results

[0935] The server sends the analysis results, and the device displays on a map, "There have been many sightings of Tsuchinoko in the mountainous areas of Gifu Prefecture."

[0936] Prompt Sentence Examples

[0937] "Design a system to collect and analyze information on the search for undiscovered buried treasure and unknown ruins and treasures, and identify search points. Also, provide an interface that users can use easily on their smartphones."

[0938] The above is a specific embodiment for carrying out the present invention. This system allows the user to perform searches efficiently and intuitively.

[0939] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0940] Step 1:

[0941] The user enters information about the search target.

[0942] Input: The user enters the search target (e.g., "Tokugawa's buried treasure" or "Tsuchinoko") into the smartphone's input interface.

[0943] Output: User input is received and prepared at the terminal.

[0944] Step 2:

[0945] The terminal transmits the input information to the server.

[0946] Input: The device uses the search target information entered by the user.

[0947] Output: The server starts collecting data based on the information received from the device.

[0948] Step 3:

[0949] The server collects the relevant data.

[0950] Input: The server gathers data from the internet and databases based on user input, using the requests library to send web requests and BeautifulSoup to parse web pages.

[0951] Output: Collected data (literatures, papers, news articles, historical maps, etc.) are stored on a server.

[0952] Step 4:

[0953] The server analyzes the data.

[0954] Input: Based on the collected data, the server uses natural language processing techniques and machine learning algorithms (e.g. geopy and scikit-learn).

[0955] Output: As a result of the analysis, the server identifies search hints and highly likely search points.

[0956] Step 5:

[0957] The server compiles the analysis results into a report.

[0958] Input: Use the results of data analysis (e.g., identified locations and hints).

[0959] Output: Analysis results are generated in a report format (text, images, maps, etc.).

[0960] Step 6:

[0961] The server sends the analysis results to the device.

[0962] Input: Use the analysis results summarized in report format.

[0963] Output: The analysis results are sent to the terminal.

[0964] Step 7:

[0965] The device visually displays the analysis results.

[0966] Input: Use the analysis results (report format) received from the server.

[0967] Output: The terminal visually displays the analysis results on maps and graphs, providing them to the user in an intuitive manner. Specifically, the folium library is used to display markers indicating specific points on a map.

[0968] The above are the processing steps of the program for the system that realizes the application example of the present invention.

[0969] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0970] This invention is a system for efficiently searching for undiscovered buried treasure and unknown ruins and treasures. This system collects and analyzes related data based on information about the search target entered by the user, provides useful information to the user, and optimizes the way results are presented by recognizing the user's emotional state. The program processing of this system is explained below in natural language.

[0971] System Overview

[0972] 1. User data entry

[0973] The user provides information about the search target to the system through the input interface of the terminal. For example, they input information such as "Tokugawa's buried treasure" or "Tsuchinoko discovery." The system uses an emotion engine to recognize the user's emotional state at the time of input and associates it with the data.

[0974] 2. Data Collection

[0975] The terminal converts the information received from the user into an appropriate format and sends it to the server. The server then collects relevant data based on the received information. Web crawling and database query techniques are used to collect data, searching data sources such as literature, papers, news articles, and historical maps based on specified keywords.

[0976] 3. Data Analysis

[0977] The server launches an analysis module to analyze the collected data. The analysis module uses natural language processing technology and machine learning algorithms to classify and organize the collected data. It then identifies search locations and hints and calculates their reliability. The analysis results are provided taking into account the user's emotional state.

[0978] 4. Providing results

[0979] The server compiles the analysis results into a report and sends it to the device. The device then presents the results visually to the user in an intuitively understandable format, such as a map or graph. The device can also adjust the way the results are presented depending on the user's emotional state.

[0980] Specific examples

[0981] Example 1: Searching for Tokugawa's buried treasure

[0982] 1. Data Entry

[0983] A user types into a terminal, "Tell me the location of Tokugawa's buried treasure." At this time, the emotion engine recognizes the user's emotional state (e.g., excitement, anticipation, anxiety, etc.) and attaches it to the data.

[0984] 2. Data Collection

[0985] The terminal sends information to a server, which then collects documents, papers, news articles, and historical maps related to "Tokugawa buried treasure" from the Internet and various databases.

[0986] 3. Data Analysis

[0987] The server's analysis module analyzes the collected data and extracts historical geographical information and records related to buried treasure based on a specific area. For example, the area around a certain temple in Tokyo is identified as a likely search point.

[0988] The system takes into account the user's emotional state and adjusts the reliability of the data and the order in which the analysis results are presented.

[0989] 4. Providing results

[0990] The server compiles the analysis results into a report and sends it to the device, which then displays to the user, "There is a high possibility that buried treasure is near a certain temple in Tokyo." The display method is adjusted based on the user's emotional state.

[0991] Example 2: Discovering a worm

[0992] 1. Data Entry

[0993] The user types into the terminal, "Tell me where Tsuchinoko appears." At this time, the emotion engine recognizes the user's emotional state and attaches it to the data.

[0994] 2. Data Collection

[0995] The terminal sends information to a server, which then collects information about sightings, legends, and data on the ecology of "Tsuchinoko" from the Internet and various documents.

[0996] 3. Data Analysis

[0997] The server's analysis module analyzes the collected data and identifies areas with a high number of Tsuchinoko sightings. For example, the mountainous areas of Gifu Prefecture are identified as likely areas.

[0998] The emotional state of the user is taken into account and influenced to effectively present the analysis results.

[0999] 4. Providing results

[1000] The server compiles the analysis results into a report and sends it to the device. The device then displays to the user, "There have been many reports of Tsuchinoko sightings in the mountainous areas of Gifu Prefecture." The optimal display method is selected based on the user's emotional state.

[1001] In this way, the system of the present invention not only provides the exact location and highly accurate search points of undiscovered buried treasures, unknown ruins, and hidden treasures, but also recognizes the user's emotional state and optimizes the way results are presented, thereby increasing the motivation and efficiency of the search.

[1002] The processing flow will be explained below.

[1003] Step 1: The user operates the device and inputs information about the search target. Specifically, the user enters the name of the search target and related information, such as "Tokugawa's buried treasure" or "Tsuchinoko discovery," into the device's input interface, and clicks the send button.

[1004] Step 2: The device receives the information entered by the user. At the same time, the device activates an emotion engine to recognize the user's emotional state. For example, it analyzes the text, facial expressions, and voice of the user when they enter information to determine whether they are excited or anxious.

[1005] Step 3: The device converts the received information and the recognized emotional state into an appropriate format (e.g., JSON format) and then sends it to the server. The transmitted data includes information about the search target and the user's emotional state.

[1006] Step 4: The server analyzes the information received from the device and extracts keywords related to the search target. Specifically, it activates the data collection module based on keywords such as "Tokugawa buried treasure" and "Tsuchinoko."

[1007] Step 5: The server's data collection module collects relevant information from the Internet and databases, using web crawling techniques and APIs to search data sources such as literature, papers, news articles, and historical maps.

[1008] Step 6: The server filters the collected data and keeps only the most reliable information, which includes assessing the reliability of the data and removing duplicate data.

[1009] Step 7: The server's data analysis module analyzes the collected data. This analysis involves using natural language processing techniques to categorize and organize relevant information, and machine learning algorithms to identify locations and specific clues to explore. The user's emotional state is also reflected as part of the analysis.

[1010] Step 8: The server validates the analysis results and calculates a confidence score, which evaluates the probability that the identified location is actually related to the search target. The server also takes into account the user's emotional state and adjusts the response, for example by providing more detailed information if the user is excited.

[1011] Step 9: The server compiles the analysis results into a report, which includes the identified locations, hints, and a confidence rating.

[1012] Step 10: The server sends the generated report to the terminal, which then displays the received report to the user.

[1013] Step 11: The device visualizes the report and presents it to the user in an intuitive format, such as through maps or graphs. Based on the user's emotional state, the presentation of the results is adjusted accordingly. For example, if the user is feeling anxious, additional information to reassure them is added.

[1014] The above is the processing flow of the system that combines the emotion engine and the specific operations that are performed at each step.

[1015] Example 2

[1016] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1017] This invention relates to a system for efficiently searching for undiscovered buried treasures and unknown ruins and treasures. Traditionally, the process of inputting search information, collecting related data, analyzing it, and providing results has often been done manually, which requires time and effort. Furthermore, the results provided may not be appropriate based on the user's emotional state, potentially reducing the user's motivation. Therefore, a system is needed that automates the search process and optimizes the way results are presented based on the user's emotional state.

[1018] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes an input means through which a user inputs information about a search target, a data collection means that collects related data from the Internet or a database based on the information received from the input means, a data analysis means that analyzes the data collected by the data collection means and identifies search hints and highly likely search points, a result providing means that provides the user with the information identified by the data analysis means, and an emotion recognition means that recognizes the user's emotional state and optimizes the method of presenting results accordingly. This makes it possible to automate the search process and provide optimal results according to the user's emotional state.

[1019] The "input means" is a means by which a user inputs information about a search target into the system.

[1020] "Data collection means" refers to a means of collecting related data from the Internet or a database based on the input information.

[1021] "Data analysis means" is a means for analyzing collected data and identifying search hints and highly likely search points.

[1022] The "result providing means" is a means for providing the information identified by the data analysis means to the user.

[1023] An "emotion recognition means" is a means for recognizing the user's emotional state and optimizing the way results are presented accordingly.

[1024] This invention is a system for efficiently searching for undiscovered buried treasures and unknown ruins and treasures. A specific method for implementing this system will be described.

[1025] System configuration

[1026] This system consists of a user terminal and a server.

[1027] 1. User Device

[1028] The user device has an interface for inputting information about the search target. Input methods include a keyboard, touch screen, and voice input. To recognize the user's emotional state, the device is equipped with an emotion engine (e.g., Emotion AI SDK).

[1029] 2. Server

[1030] The server collects data based on the information sent from the user's device, analyzes it, and provides the results. Data collection methods include web scraping tools using Python (e.g., BeautifulSoup, Scrapy) and database query technologies (e.g., SQL). Analysis methods include natural language processing technologies (e.g., NLTK, SpaCy) and machine learning algorithms (e.g., scikit-learn, TensorFlow). Results are provided using map displays (e.g., Leaflet.js) and graph displays (e.g., D3.js).

[1031] System operation explanation

[1032] 1. Data Entry

[1033] The user uses the device's input interface to input information about the search target, for example, "Tell me the location of Tokugawa's buried treasure." At this time, the emotion engine recognizes the user's emotional state at the time of input and adds it to the data.

[1034] 2. Data Collection

[1035] The device converts the information into an appropriate format (e.g., JSON format) and sends it to the server. The server processes the received data and generates queries to collect relevant data. Web crawling techniques (e.g., Scrapy) and database query techniques (e.g., SQL) are used to collect literature, papers, news articles, historical maps, etc. from the Internet and various databases.

[1036] 3. Data Analysis

[1037] The server's analysis module runs and analyzes the collected data. It uses natural language processing techniques (e.g., NLTK, SpaCy) to classify the data, and machine learning algorithms (e.g., scikit-learn, TensorFlow) to identify locations and hints related to the search target and calculate their reliability. Furthermore, it adjusts the order and content of the results based on the user's emotional state.

[1038] 4. Providing results

[1039] The server compiles the analysis results into a report and sends it to the device. The device then presents the results to the user in a visually easy-to-understand format, such as an interactive map (e.g., Leaflet.js) or a graph display (e.g., D3.js). The display method is adjusted according to the user's emotional state to provide an optimal user experience.

[1040] Specific examples

[1041] Example 1: Searching for Tokugawa's buried treasure

[1042] 1. Data Entry

[1043] The user types into the terminal, "Tell me the location of Tokugawa's buried treasure." The emotion engine recognizes the user's emotional state, such as excitement or anticipation, and adds it to the data.

[1044] 2. Data Collection

[1045] The device converts the information into JSON format and sends it to a server that collects documents, papers, news articles, historical maps, and other information related to the "Tokugawa buried treasure."

[1046] 3. Data Analysis

[1047] The server's analysis module tokenizes the data and organizes it into categories. For example, it may identify "near a certain temple in Tokyo" as an analysis result.

[1048] The order and content of the analysis results are adjusted taking into account the user's emotional state.

[1049] 4. Providing results

[1050] The server compiles the analysis results into a report and sends it to the device. The device then displays the results as an interactive map, showing users that "there is a high possibility that buried treasure is near a certain temple in Tokyo." The display style uses bright colors to match the user's expectations.

[1051] Example 2: Discovering a worm

[1052] 1. Data Entry

[1053] The user types into the device, "Tell me where Tsuchinoko appears." The emotion engine recognizes the user's excitement or surprise and adds it to the data.

[1054] 2. Data Collection

[1055] The device converts the information into a suitable format and sends it to a server, which collects data, including sightings and legends, from the internet and literature.

[1056] 3. Data Analysis

[1057] The analysis module analyzes the collected sighting information and identifies the "mountainous areas of Gifu Prefecture" as a likely area.

[1058] The display order and content are adjusted based on the user's emotional state.

[1059] 4. Providing results

[1060] The server compiles the analysis results into a report and sends it to the device. The device then visually displays the results, saying, "There have been many reports of Tsuchinoko sightings in the mountainous areas of Gifu Prefecture." The display style uses a subdued color scheme to avoid amplifying the sense of surprise.

[1061] In this way, the system of the present invention can consistently automate the process from collecting data on search targets to analyzing them and providing the results, and can present the results in a form optimized based on the user's emotional state.

[1062] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1063] Step 1:

[1064] The user inputs information about the search target using the terminal's input interface. The input information includes a prompt sentence, such as "Tell me the location of Tokugawa's buried treasure." The emotion engine uses voice tone and facial expression recognition technology to recognize the user's emotional state (e.g., excitement, anticipation, anxiety) and adds it to the input data. The input at this time is the prompt sentence and emotional information. The output is formed as a single dataset.

[1065] Step 2:

[1066] The device converts the data received from the user into an appropriate format (e.g., JSON) and sends it to the server. A programming language such as Python is used as the conversion method. The input is a dataset containing a prompt sentence and emotion information, and the output is formatted data. Specifically, the format conversion function is called, the data is converted into JSON format, and then sent to the server.

[1067] Step 3:

[1068] The server processes the received formatted data and generates queries to collect relevant data. It uses web crawling techniques (e.g., Scrapy) or database query techniques (e.g., SQL) to collect data. The input is the formatted data, and the output is the collected relevant data. Specifically, it calls the query generation function to collect the required data from the Internet or a database.

[1069] Step 4:

[1070] The server launches an analysis module (e.g., NLTK, scikit-learn) to analyze the collected data. The analysis module tokenizes the collected data using natural language processing techniques and classifies the data using machine learning algorithms. The input is the collected relevant data, and the output is the analysis results. Specific operations include tokenizing text and running a category classification algorithm.

[1071] Step 5:

[1072] The server compiles the analysis results into a report, which includes the location of the search target, related hints, and confidence data. It also configures the content to be presented in an easy-to-interpret manner based on the user's emotional state. The input is the analysis results and the user's emotional information, and the output is data in report format. Specific operations include calling a report generation function, organizing the data, and adjusting the display style according to the user's emotional state.

[1073] Step 6:

[1074] The server sends the analysis results in report format to the terminal. The terminal launches a map display (e.g., Leaflet.js) or graph display (e.g., D3.js) module to visually display the results to the user. The input is data in report format, and the output is the visually displayed results. Specific operations include launching the map display module and graph display module, and selecting the optimal display method based on the user's emotional state.

[1075] Through the above steps, this system efficiently and effectively searches for undiscovered buried treasures and unknown ruins and treasures, and provides optimal information to users.

[1076] (Application example 2)

[1077] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1078] Conventional search systems provide information without considering the user's emotional state, which can result in insufficient results. Furthermore, when detecting anomalies within a factory, providing information without considering the emotional state of the manager can reduce the effectiveness and efficiency of emergency responses. To solve these problems, technology is needed that recognizes the emotional state of the user or manager and optimizes the way information is presented based on that state.

[1079] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1080] In this invention, the server includes an input means for a user to input information about a search target, a data collection means for collecting related data from the Internet or a database based on the information received from the input means, a data analysis means for analyzing the data collected by the data collection means and identifying search hints and highly likely search points, a result provision means for providing the user with the information identified by the data analysis means, and an emotion recognition means for recognizing the user's emotional state using emotion recognition technology and adjusting the content presented by the provision means based on the user's emotional state. This makes it possible to provide optimal information according to the user's emotional state, thereby improving the effectiveness and efficiency of searches and anomaly detection.

[1081] The "input means by which the user inputs information about the search target" refers to a device or interface that allows the user to input information about the search target.

[1082] "Data collection means for collecting related data from the Internet or databases based on information received from the input means" refers to a function or device for collecting related information from the Internet or various databases based on information from the input means.

[1083] "Data analysis means for analyzing the data collected by the data collection means and identifying search hints and highly likely search points" refers to a function or device for analyzing collected data, and is used to identify information and points that are useful for searching.

[1084] The "result providing means for providing the user with the information identified by the data analysis means" refers to a function or device for conveying the information found by the data analysis means to the user.

[1085] "Emotion recognition means that recognizes the emotional state of a user using emotion recognition technology and adjusts the content presented by the providing means based on the emotional state of the user" refers to a function or device that recognizes emotions using technology that detects the user's emotions and optimizes the information and format of the information to be provided based on the results.

[1086] "Natural language processing technology" refers to technology that enables computers to understand and process human language.

[1087] A "machine learning algorithm" is a method for learning patterns from collected data and making predictions, classifications, and analyses.

[1088] "Map display means" refers to a function or device that provides information in map format to visually display the analysis results.

[1089] "Graph display means" refers to a function or device that provides information in a graph format to visually display the analysis results.

[1090] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The embodiments for carrying out the present invention will be described in detail below.

[1091] This system includes a terminal for users to input information about the search target, a server for collecting related data from the Internet or a database based on that information, and means for analyzing the collected data and providing the results to the user.

[1092] Hardware and Software Configuration

[1093] 1. Terminal

[1094] The terminal is equipped with an input means for the user to input information about the search target, and may be, for example, a smartphone, tablet, or PC. Software for using emotion recognition technology (e.g., emotion recognition API) is installed on this terminal.

[1095] 2. Server

[1096] The server includes the following means:

[1097] Data collection means: Based on the information received from the terminal, relevant data is collected from the Internet and databases. Specifically, HTTP requests are used to obtain the necessary data from various data sources (literature, papers, news articles, etc.).

[1098] Data analysis methods: Analyze the collected data by using natural language processing techniques (e.g., TextBlob) and machine learning algorithms (e.g., IsolationForest) to classify and organize the data and identify search hints and high-confidence search points.

[1099] Result presentation means: The analysis results are presented to the user. The results are adjusted based on the user's emotional state and visually presented to the user using map and graph display means.

[1100] 3. Emotion recognition means

[1101] The emotion recognition means recognizes the emotional state of the user when they input data and attaches that information to the data. This emotion recognition technology utilizes an emotion analysis library such as TextBlob.

[1102] Example of a system

[1103] Example 1: Identifying the discovery location

[1104] The user types into the terminal, "I want you to identify the abnormal part of the machine." At this time, emotion recognition technology is used to analyze the user's emotional state and recognize that the situation is anxious or urgent. The server uses this information to collect related data from the Internet and databases, and identifies the abnormal part using a machine learning algorithm. The results are then presented to the administrator in the form of a map or graph.

[1105] Example 2: Visualization of location information

[1106] The server analyzes the collected data and detects abnormalities in a specific area (e.g., a specific section of a factory). The analysis results are highlighted using map display means or graph display means depending on the manager's emotional state (e.g., if the level of urgency is high).

[1107] Prompt Sentence Examples

[1108] By entering the following prompts into the generative AI model, you can obtain information that will help with system construction and analysis.

[1109] Example prompt:

[1110] Please build a system to collect data within the factory and identify abnormalities as follows:

[1111] Collects sensor data (temperature, vibration, sound, log)

[1112] Uses an anomaly detection algorithm (Isolation Forest)

[1113] Uses Emotion Recognition (TextBlob)

[1114] The above is an embodiment of the present invention, which makes it possible to provide optimal information according to the emotional state of the user, thereby improving the effectiveness and efficiency of search and anomaly detection.

[1115] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1116] Step 1:

[1117] The user inputs information about the search target through the device's input means. For example, the user inputs information such as "Please identify the abnormal part of the machine." At this time, the device uses emotion recognition technology to analyze the user's emotional state and recognizes that the situation is anxious or urgent. The input is in text format, and the analysis result of the emotional state is also output at the same time. This information is sent to the server in the next step.

[1118] Step 2:

[1119] The device sends data to the server based on the information entered in the previous step. The data sent includes the user's input information and emotional state. The server receives this transmitted data and activates the data collection method. Specifically, based on this information, it uses HTTP requests to collect necessary data such as related literature, papers, news articles, and sensor data from the Internet and various databases. The collected data is preprocessed and converted into a format suitable for analysis.

[1120] Step 3:

[1121] The server analyzes the data collected by the data collection method. It uses natural language processing technology (TextBlob) and a machine learning algorithm (IsolationForest) to analyze the data. First, it uses TextBlob to perform text analysis of the collected data, extracting and classifying relevant information. At the same time, it applies IsolationForest to the quantitative data from the sensor to identify abnormalities. The analysis results are output in a format that takes into account the emotion recognition results along with their reliability.

[1122] Step 4:

[1123] The server creates a report to provide to the user based on the results obtained by the data analysis means. Map display means and graph display means are used to provide the results. If the analysis results indicate an abnormality in a specific area or machine, the information is visually displayed in map or graph format. Furthermore, the way in which the results are presented is adjusted according to the user's emotional state (level of urgency or anxiety). For example, if the level of urgency is high, the result is highlighted and a detailed explanation is added.

[1124] Step 5:

[1125] The server sends a final report to the terminal. The terminal receives this report and displays it to the user. The user can then review the displayed report and take any necessary action, such as making arrangements to quickly address any identified abnormalities. This process allows the user to receive optimal information based on their emotional state, improving the efficiency of search and anomaly detection.

[1126] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1127] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1128] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1129] [Fourth embodiment]

[1130] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1131] 7, a 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.

[1132] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1133] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1134] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1136] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1137] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1138] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1139] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[1140] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1141] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1142] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1143] This invention is a system for efficiently searching for undiscovered buried treasure and unknown ruins and treasures. This system collects and analyzes related data based on information about the search target entered by the user, and provides useful information to the user. Below, the processing of the program of this system is explained in natural language.

[1144] System Overview

[1145] 1. User data entry

[1146] The user provides information about the search target to the system through the input interface of the terminal. For example, the user inputs information such as "Tokugawa's buried treasure" or "Tsuchinoko discovery."

[1147] 2. Data Collection

[1148] The terminal converts the information received from the user into an appropriate format and sends it to the server. The server then collects related data based on the received information. Web crawling and database query techniques are used to collect related information such as literature, papers, news articles, and historical maps from the Internet and various databases.

[1149] 3. Data Analysis

[1150] The server launches an analysis module to analyze the collected data. The analysis module uses natural language processing technology and machine learning algorithms to classify and organize the collected data. It then identifies search locations and clues and calculates their reliability.

[1151] 4. Providing results

[1152] The server compiles the analysis results into a report format and sends it to the terminal, which then visually displays the results to the user in an intuitive, easy-to-understand format, such as using maps or graphs.

[1153] Specific examples

[1154] Example 1: Searching for Tokugawa's buried treasure

[1155] 1. Data Entry

[1156] The user types into the terminal, "Tell me the location of Tokugawa's buried treasure."

[1157] 2. Data Collection

[1158] The terminal sends information to a server, which then collects documents, papers, news articles, and historical maps related to "Tokugawa buried treasure" from the Internet and various databases.

[1159] 3. Data Analysis

[1160] The server's analysis module analyzes the collected data and extracts historical geographical information and records related to buried treasure based on a specific area. For example, the area around a certain temple in Tokyo is identified as a likely search point.

[1161] 4. Providing results

[1162] The server compiles the analysis results into a report and sends it to the device, which then displays to the user, "There is a high possibility that buried treasure is near a certain temple in Tokyo."

[1163] Example 2: Discovering a worm

[1164] 1. Data Entry

[1165] The user types into the terminal, "Tell me where Tsuchinoko appears."

[1166] 2. Data Collection

[1167] The terminal sends information to a server, which then collects information about sightings, legends, and data on the ecology of "Tsuchinoko" from the Internet and various documents.

[1168] 3. Data Analysis

[1169] The server's analysis module analyzes the collected data and identifies areas with a high number of Tsuchinoko sightings. For example, the mountainous areas of Gifu Prefecture are identified as likely areas.

[1170] 4. Providing results

[1171] The server compiles the analysis results into a report and sends it to the device, which then displays to the user, "There have been many reports of Tsuchinoko sightings in the mountainous areas of Gifu Prefecture."

[1172] In this way, the system of the present invention enables users to conduct efficient searches by providing the exact locations and highly accurate search points of undiscovered buried treasures, unknown ruins, and hidden treasures.

[1173] The processing flow will be explained below.

[1174] Step 1: The user operates the device and inputs information about the search target. Specifically, the user enters the name and related information of the buried treasure or legendary treasure they are searching for in the input form, and clicks the submit button.

[1175] Step 2: The terminal receives the information entered by the user, converts this information into an appropriate format (e.g., JSON format), and then sends it to the server.

[1176] Step 3: The server analyzes the information received from the device and extracts keywords related to the search target. These keywords are the basis for data collection.

[1177] Step 4: The server's data collection module starts up and collects relevant information from the Internet and databases, using web crawling techniques and APIs to search data sources such as literature, papers, news articles, and historical maps based on the specified keywords.

[1178] Step 5: The server filters the collected data and retains only the most reliable information, which includes assessing the reliability of the data and removing duplicate data.

[1179] Step 6: The server's data analysis module analyzes the collected data, using natural language processing techniques to classify and organize relevant information, and machine learning algorithms to identify search locations and specific clues.

[1180] Step 7: The server validates the analysis results and calculates a confidence score, which assesses the probability that the identified location is actually related to the search target.

[1181] Step 8: The server compiles the analysis results into a report, which includes the identified locations, hints, and a confidence rating.

[1182] Step 9: The server sends the generated report to the terminal, which then displays the received report to the user.

[1183] Step 10: The terminal visualizes the report and presents it to the user in an intuitive format, such as a map or graph display, so the user can check the results and plan their next exploration.

[1184] The above is the processing flow of the system and the specific operations performed at each step.

[1185] Example 1

[1186] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1187] Currently, the search for undiscovered buried treasures, unknown ruins, and hidden treasures requires a lot of time and resources, and the success rate is low. Traditional methods often rely on manual information collection and analysis, which lack accuracy and efficiency, resulting in a large amount of search effort. This makes it difficult for explorers to quickly and efficiently obtain useful information.

[1188] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1189] In this invention, the server includes an input means for a user to input information about a search target, a format conversion means for converting the information received from the input means into JSON format data and transmitting it to the server, a data collection means for collecting related data from the Internet or a database based on the data generated by the format conversion means, a data analysis means for analyzing the data collected by the data collection means and identifying search hints and highly likely search points using natural language processing technology and machine learning algorithms, and a result provision means for providing the user with the information identified by the data analysis means. This makes it possible to efficiently and accurately collect information about the search target, analyze it, and provide the results, thereby increasing the success rate of the search.

[1190] A "user" is an entity that inputs information about a search target into the system and receives results based on that information.

[1191] "Input means" refers to an interface through which a user inputs information about a search target into the system.

[1192] The "format conversion means" is a function that converts information received from the user into JSON format data and sends it to the server.

[1193] The "data collection means" is a function that collects related information from the Internet and databases based on the data generated by the format conversion means.

[1194] "Data analysis means" is a function that analyzes collected data, organizes and classifies information using natural language processing technology and machine learning algorithms, and identifies search hints and highly likely search points.

[1195] The "result providing means" is a function for providing the information identified by the data analysis means to the user.

[1196] "Natural language processing technology" is a technology for analyzing text data and understanding meaning and relationships.

[1197] A "machine learning algorithm" is an algorithm that learns from large amounts of data, finds patterns, and makes predictions and classifications.

[1198] "Search target" refers to undiscovered buried treasure, unknown ruins, or hidden treasures that the user wishes to discover or identify.

[1199] The "JSON format" is a lightweight data exchange format for representing data in a structured manner.

[1200] The "Internet" is an information and communications network that connects computer networks around the world.

[1201] A "database" is a system for efficiently managing and searching data.

[1202] "Search hints" refer to important clues or information about the subject of the search.

[1203] A "high probability search point" refers to a location that is determined to be highly likely to contain the search target.

[1204] This invention is a system for efficiently searching for undiscovered buried treasures and unknown ruins and treasures. This system collects and analyzes related data based on information about the search target entered by the user, and provides the user with useful information.

[1205] System Overview

[1206] 1. User data entry

[1207] The user provides information about the search target to the system through the terminal's input interface. This interface has keyboard and voice input functions. For example, the user can input information such as "Tokugawa's buried treasure" or "Tsuchinoko discovery."

[1208] 2. Information format conversion

[1209] The terminal converts the information received from the user into an appropriate format and sends it to the server. This format conversion has the function of converting information entered in natural language into JSON format. As a concrete example, the question entered by the user, "Tell me the location of Tokugawa's buried treasure," is converted into the JSON format {"request": "Tell me the location of Tokugawa's buried treasure"}.

[1210] 3. Data Collection

[1211] The server collects relevant data based on the converted data. Data collection methods include web crawling and database querying, and gathers information from the internet and various databases. Specific software used includes Scrapy and Beautiful Soup for web crawling, and SQL and NoSQL query tools for database queries.

[1212] 4. Data Analysis

[1213] The server launches an analysis module to analyze the collected data. This analysis module classifies and organizes the data using natural language processing technology and machine learning algorithms. Specific technologies used are Google Cloud Natural Language and Amazon Comprehend, and machine learning algorithms include TensorFlow and Pandas.

[1214] 5. Providing results

[1215] The server compiles the analysis results into a report and sends it to the device. The device has the functionality to visually display the results. For example, it can use the Google Maps API to display a specific area on a map, providing the results in an easy-to-understand format for the user.

[1216] Specific examples

[1217] Example 1: Searching for Tokugawa's buried treasure

[1218] 1. User data entry

[1219] The user types into the terminal, "Tell me the location of Tokugawa's buried treasure."

[1220] 2. Information format conversion

[1221] The terminal converts this input into the JSON format {"request": "Tell me the location of Tokugawa's buried treasure"}.

[1222] 3. Data Collection

[1223] The server uses Scrapy and SQL queries to collect documents and historical maps related to "Tokugawa buried treasure" from the web and various databases.

[1224] 4. Data Analysis

[1225] The server uses the Google Cloud Natural Language API to analyze literature and data, and identifies "near a certain temple in Tokyo" as a likely search point.

[1226] 5. Providing results

[1227] The server compiles the analysis results into an HTML report and sends it to the device, which then uses the Google Maps API to display the identified location and tell the user, "There is a high possibility that buried treasure is near a certain temple in Tokyo."

[1228] Example 2: Discovering a worm

[1229] 1. User data entry

[1230] The user types into the terminal, "Tell me where Tsuchinoko appears."

[1231] 2. Information format conversion

[1232] The device converts this input into the JSON format {"request": "Tell me where the Tsuchinoko appears"}.

[1233] 3. Data Collection

[1234] The server uses Beautiful Soup and SQL queries to gather information about sightings and legends related to "Tsuchinoko" from the web and databases.

[1235] 4. Data Analysis

[1236] The server uses the Google Cloud Natural Language API to analyze the sightings and identify the "mountainous areas of Gifu Prefecture" as a likely area.

[1237] 5. Providing results

[1238] The server compiles the analysis results into a report and sends it to the device. The device then displays the location identified using the Google Maps API, informing the user that "there have been many reports of Tsuchinoko sightings in the mountainous areas of Gifu Prefecture."

[1239] This allows the user to efficiently and accurately search for buried treasure, unknown ruins, and hidden treasures.

[1240] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1241] Step 1:

[1242] The terminal receives input from the user. The user enters information about the search target into the terminal's interface. This information is written in natural language and includes phrases such as "Tokugawa's buried treasure" and "Tsuchinoko discovery." The input is raw data that the terminal prepares for the next processing step.

[1243] Step 2:

[1244] The device converts the received user input information into JSON format. For example, the input "Tell me where Tokugawa's buried treasure is" is converted into the JSON format {"request": "Tell me where Tokugawa's buried treasure is"}. This conversion process enables subsequent data collection and analysis to be carried out efficiently. The converted data is then sent to the server via a dedicated API.

[1245] Step 3:

[1246] The server collects related information based on the JSON-formatted data received from the device. The server uses web crawling technologies (such as Scrapy and Beautiful Soup) to collect data from Internet documents, news articles, blog posts, etc. It also uses database query technologies to retrieve related information from various databases. The collected data is primarily text data related to the subject the user is searching for.

[1247] Step 4:

[1248] The server analyzes the data collected by the data collection method. It launches an analysis module (using TensorFlow or Pandas) to classify and organize the collected data. At this stage, natural language processing technology (Google Cloud Natural Language or Amazon Comprehend) is used to extract important keywords and context. For example, "near a certain temple in Tokyo" is identified as the search target. This analysis process provides search hints and identifies highly likely search points.

[1249] Step 5:

[1250] The server creates a report of the analysis results to provide to the user. This report is created in HTML format and includes the locations identified as analysis results and related information. The report is presented to the user using map display means (Google Maps API) and graph display means for easy visual viewing. The server sends the completed report to the terminal.

[1251] Step 6:

[1252] The device displays the report received from the server to the user. For example, it uses the Google Maps API to display the identified area on a map, visually showing the analysis results. It also provides information in text format, such as "There is a high possibility that buried treasure is found near a certain temple in Tokyo." The user can then carry out specific search activities based on this information.

[1253] This system allows users to efficiently and accurately search for undiscovered buried treasures, unknown ruins and hidden treasures.

[1254] (Application example 1)

[1255] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1256] In modern times, searching for undiscovered buried treasures, unknown ruins, and hidden treasures is difficult because it is difficult to find useful information from the vast amount of information. In particular, because the amount of information is vast and diverse, there is a demand for a system that can efficiently and accurately collect and analyze related information. In addition, it is necessary to visually display the analysis results so that users can easily understand them intuitively. The purpose of this invention is to solve these problems.

[1257] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1258] In this invention, the server includes an input means for a user to input information about a search target, a data collection means for collecting related data from the Internet or a database based on the information received from the input means, a data analysis means for analyzing the data collected by the data collection means and identifying search hints and highly likely search points, and a result display means for visually displaying the analysis results using maps and graphs, thereby enabling users to efficiently search and intuitively understand the information they need.

[1259] A "user" is a person who uses a terminal to input search target information into the system and receive the analysis results.

[1260] The "input means" is an interface through which the user provides information about the search target to the system.

[1261] The "data collection means" is a device or function that collects related data from the Internet or a database based on the information received from the input means.

[1262] The "data analysis means" is a device or function that uses and analyzes the data collected by the data collection means to identify search hints and highly accurate search points.

[1263] The "result providing means" is a device or function that provides the user with the information identified by the data analysis means.

[1264] The "result display means" is a device or function that visually displays the analysis results using maps or graphs.

[1265] "Natural language processing technology" is a technology for analyzing collected data and classifying and organizing important information related to the search target.

[1266] A "machine learning algorithm" is a mathematical method or model used to identify locations and relevant information to search for in the data being analyzed.

[1267] The "geographical display means" is a device or function for displaying the analysis results for the identified location on a map.

[1268] The "map display means" is a means for visually displaying the analysis results for the identified location on a map.

[1269] The "graph display means" is a means for visually displaying the analysis results for the identified location in a graph.

[1270] Hereinafter, embodiments of the present invention will be described in detail.

[1271] System Overview

[1272] The system components are:

[1273] An input means for the user to input information about the search target

[1274] A data collection means for collecting related data from the Internet or a database based on the information received from the input means.

[1275] a data analysis means for analyzing the data collected by the data collection means and identifying search hints and highly accurate search points;

[1276] a result providing means for providing the information identified by the data analysis means to a user;

[1277] A means of visually displaying analysis results using maps and graphs

[1278] User Data Entry

[1279] Users input information about the search target into the system through the smartphone interface. For example, they might enter a search target such as "Tell me the location of Tokugawa's buried treasure" or "Tell me where Tsuchinoko appears."

[1280] Data collection

[1281] The device sends the input information to a server, which then uses that information to collect relevant data from the internet and databases. Techniques used include HTTP requests using the requests library and web scraping using BeautifulSoup. This allows the server to collect data such as related literature, news articles, and historical maps.

[1282] Data analysis

[1283] The server launches a module to analyze the collected data. This module uses natural language processing techniques and machine learning algorithms (e.g., geographic information analysis using the geopy library and classification and organization using the scikit-learn library) to analyze the collected data. Specifically, it classifies the locations and clues to be explored and identifies specific areas.

[1284] Results provision and display

[1285] The analysis results are displayed visually. The server compiles the analysis results into a report format and sends it to the terminal. The terminal then displays the results to the user visually in the form of a map or graph, using the folium library to display a map and indicate related search points. This allows the user to intuitively understand the analysis results.

[1286] Specific examples

[1287] Example 1: Searching for Tokugawa's buried treasure

[1288] 1. User data entry

[1289] A user types in "Tell me the location of Tokugawa's buried treasure."

[1290] 2. Data Collection

[1291] The terminal sends the information to a server, which collects documents and historical maps related to the "Tokugawa buried treasure."

[1292] 3. Data Analysis

[1293] The server's analysis module analyzes the data and identifies the location as "near a certain temple in Tokyo."

[1294] 4. Providing and displaying results

[1295] The server sends the analysis results, and the device displays a map saying, "There is a high possibility that buried treasure is near a certain temple in Tokyo."

[1296] Example 2: Discovery of the earthworm

[1297] 1. User data entry

[1298] The user types, "Tell me where Tsuchinoko appears."

[1299] 2. Data Collection

[1300] The terminal sends information to a server, which collects sightings and legends about "Tsuchinoko."

[1301] 3. Data Analysis

[1302] The server's analysis module analyzes the data and identifies the "mountainous area of ​​Gifu Prefecture."

[1303] 4. Providing and displaying results

[1304] The server sends the analysis results, and the device displays on a map, "There have been many sightings of Tsuchinoko in the mountainous areas of Gifu Prefecture."

[1305] Prompt Sentence Examples

[1306] "Design a system to collect and analyze information on the search for undiscovered buried treasure and unknown ruins and treasures, and identify search points. Also, provide an interface that users can use easily on their smartphones."

[1307] The above is a specific embodiment for carrying out the present invention. This system allows the user to perform searches efficiently and intuitively.

[1308] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1309] Step 1:

[1310] The user enters information about the search target.

[1311] Input: The user enters the search target (e.g., "Tokugawa's buried treasure" or "Tsuchinoko") into the smartphone's input interface.

[1312] Output: User input is received and prepared at the terminal.

[1313] Step 2:

[1314] The terminal transmits the input information to the server.

[1315] Input: The device uses the search target information entered by the user.

[1316] Output: The server starts collecting data based on the information received from the device.

[1317] Step 3:

[1318] The server collects the relevant data.

[1319] Input: The server gathers data from the internet and databases based on user input, using the requests library to send web requests and BeautifulSoup to parse web pages.

[1320] Output: Collected data (literatures, papers, news articles, historical maps, etc.) are stored on a server.

[1321] Step 4:

[1322] The server analyzes the data.

[1323] Input: Based on the collected data, the server uses natural language processing techniques and machine learning algorithms (e.g. geopy and scikit-learn).

[1324] Output: As a result of the analysis, the server identifies search hints and highly likely search points.

[1325] Step 5:

[1326] The server compiles the analysis results into a report.

[1327] Input: Use the results of data analysis (e.g., identified locations and hints).

[1328] Output: Analysis results are generated in a report format (text, images, maps, etc.).

[1329] Step 6:

[1330] The server sends the analysis results to the device.

[1331] Input: Use the analysis results summarized in report format.

[1332] Output: The analysis results are sent to the terminal.

[1333] Step 7:

[1334] The device visually displays the analysis results.

[1335] Input: Use the analysis results (report format) received from the server.

[1336] Output: The terminal visually displays the analysis results on maps and graphs, providing them to the user in an intuitive manner. Specifically, the folium library is used to display markers indicating specific points on a map.

[1337] The above are the processing steps of the program for the system that realizes the application example of the present invention.

[1338] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1339] This invention is a system for efficiently searching for undiscovered buried treasure and unknown ruins and treasures. This system collects and analyzes related data based on information about the search target entered by the user, provides useful information to the user, and optimizes the way results are presented by recognizing the user's emotional state. The program processing of this system is explained below in natural language.

[1340] System Overview

[1341] 1. User data entry

[1342] The user provides information about the search target to the system through the input interface of the terminal. For example, they input information such as "Tokugawa's buried treasure" or "Tsuchinoko discovery." The system uses an emotion engine to recognize the user's emotional state at the time of input and associates it with the data.

[1343] 2. Data Collection

[1344] The terminal converts the information received from the user into an appropriate format and sends it to the server. The server then collects relevant data based on the received information. Web crawling and database query techniques are used to collect data, searching data sources such as literature, papers, news articles, and historical maps based on specified keywords.

[1345] 3. Data Analysis

[1346] The server launches an analysis module to analyze the collected data. The analysis module uses natural language processing technology and machine learning algorithms to classify and organize the collected data. It then identifies search locations and hints and calculates their reliability. The analysis results are provided taking into account the user's emotional state.

[1347] 4. Providing results

[1348] The server compiles the analysis results into a report and sends it to the device. The device then presents the results visually to the user in an intuitively understandable format, such as a map or graph. The device can also adjust the way the results are presented depending on the user's emotional state.

[1349] Specific examples

[1350] Example 1: Searching for Tokugawa's buried treasure

[1351] 1. Data Entry

[1352] A user types into a terminal, "Tell me the location of Tokugawa's buried treasure." At this time, the emotion engine recognizes the user's emotional state (e.g., excitement, anticipation, anxiety, etc.) and attaches it to the data.

[1353] 2. Data Collection

[1354] The terminal sends information to a server, which then collects documents, papers, news articles, and historical maps related to "Tokugawa buried treasure" from the Internet and various databases.

[1355] 3. Data Analysis

[1356] The server's analysis module analyzes the collected data and extracts historical geographical information and records related to buried treasure based on a specific area. For example, the area around a certain temple in Tokyo is identified as a likely search point.

[1357] The system takes into account the user's emotional state and adjusts the reliability of the data and the order in which the analysis results are presented.

[1358] 4. Providing results

[1359] The server compiles the analysis results into a report and sends it to the device, which then displays to the user, "There is a high possibility that buried treasure is near a certain temple in Tokyo." The display method is adjusted based on the user's emotional state.

[1360] Example 2: Discovering a worm

[1361] 1. Data Entry

[1362] The user types into the terminal, "Tell me where Tsuchinoko appears." At this time, the emotion engine recognizes the user's emotional state and attaches it to the data.

[1363] 2. Data Collection

[1364] The terminal sends information to a server, which then collects information about sightings, legends, and data on the ecology of "Tsuchinoko" from the Internet and various documents.

[1365] 3. Data Analysis

[1366] The server's analysis module analyzes the collected data and identifies areas with a high number of Tsuchinoko sightings. For example, the mountainous areas of Gifu Prefecture are identified as likely areas.

[1367] The emotional state of the user is taken into account and influenced to effectively present the analysis results.

[1368] 4. Providing results

[1369] The server compiles the analysis results into a report and sends it to the device. The device then displays to the user, "There have been many reports of Tsuchinoko sightings in the mountainous areas of Gifu Prefecture." The optimal display method is selected based on the user's emotional state.

[1370] In this way, the system of the present invention not only provides the exact location and highly accurate search points of undiscovered buried treasures, unknown ruins, and hidden treasures, but also recognizes the user's emotional state and optimizes the way results are presented, thereby increasing the motivation and efficiency of the search.

[1371] The processing flow will be explained below.

[1372] Step 1: The user operates the device and inputs information about the search target. Specifically, the user enters the name of the search target and related information, such as "Tokugawa's buried treasure" or "Tsuchinoko discovery," into the device's input interface, and clicks the send button.

[1373] Step 2: The device receives the information entered by the user. At the same time, the device activates an emotion engine to recognize the user's emotional state. For example, it analyzes the text, facial expressions, and voice of the user when they enter information to determine whether they are excited or anxious.

[1374] Step 3: The device converts the received information and the recognized emotional state into an appropriate format (e.g., JSON format) and then sends it to the server. The transmitted data includes information about the search target and the user's emotional state.

[1375] Step 4: The server analyzes the information received from the device and extracts keywords related to the search target. Specifically, it activates the data collection module based on keywords such as "Tokugawa buried treasure" and "Tsuchinoko."

[1376] Step 5: The server's data collection module collects relevant information from the Internet and databases, using web crawling techniques and APIs to search data sources such as literature, papers, news articles, and historical maps.

[1377] Step 6: The server filters the collected data and keeps only the most reliable information, which includes assessing the reliability of the data and removing duplicate data.

[1378] Step 7: The server's data analysis module analyzes the collected data. This analysis involves using natural language processing techniques to categorize and organize relevant information, and machine learning algorithms to identify locations and specific clues to explore. The user's emotional state is also reflected as part of the analysis.

[1379] Step 8: The server validates the analysis results and calculates a confidence score, which evaluates the probability that the identified location is actually related to the search target. The server also takes into account the user's emotional state and adjusts the response, for example by providing more detailed information if the user is excited.

[1380] Step 9: The server compiles the analysis results into a report, which includes the identified locations, hints, and a confidence rating.

[1381] Step 10: The server sends the generated report to the terminal, which then displays the received report to the user.

[1382] Step 11: The device visualizes the report and presents it to the user in an intuitive format, such as through maps or graphs. Based on the user's emotional state, the presentation of the results is adjusted accordingly. For example, if the user is feeling anxious, additional information to reassure them is added.

[1383] The above is the processing flow of the system that combines the emotion engine and the specific operations that are performed at each step.

[1384] Example 2

[1385] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1386] This invention relates to a system for efficiently searching for undiscovered buried treasures and unknown ruins and treasures. Traditionally, the process of inputting search information, collecting related data, analyzing it, and providing results has often been done manually, which requires time and effort. Furthermore, the results provided may not be appropriate based on the user's emotional state, potentially reducing the user's motivation. Therefore, a system is needed that automates the search process and optimizes the way results are presented based on the user's emotional state.

[1387] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes an input means through which a user inputs information about a search target, a data collection means that collects related data from the Internet or a database based on the information received from the input means, a data analysis means that analyzes the data collected by the data collection means and identifies search hints and highly likely search points, a result providing means that provides the user with the information identified by the data analysis means, and an emotion recognition means that recognizes the user's emotional state and optimizes the method of presenting results accordingly. This makes it possible to automate the search process and provide optimal results according to the user's emotional state.

[1388] The "input means" is a means by which a user inputs information about a search target into the system.

[1389] "Data collection means" refers to a means of collecting related data from the Internet or a database based on the input information.

[1390] "Data analysis means" is a means for analyzing collected data and identifying search hints and highly likely search points.

[1391] The "result providing means" is a means for providing the information identified by the data analysis means to the user.

[1392] An "emotion recognition means" is a means for recognizing the user's emotional state and optimizing the way results are presented accordingly.

[1393] This invention is a system for efficiently searching for undiscovered buried treasures and unknown ruins and treasures. A specific method for implementing this system will be described.

[1394] System configuration

[1395] This system consists of a user terminal and a server.

[1396] 1. User Device

[1397] The user device has an interface for inputting information about the search target. Input methods include a keyboard, touch screen, and voice input. To recognize the user's emotional state, the device is equipped with an emotion engine (e.g., Emotion AI SDK).

[1398] 2. Server

[1399] The server collects data based on the information sent from the user's device, analyzes it, and provides the results. Data collection methods include web scraping tools using Python (e.g., BeautifulSoup, Scrapy) and database query technologies (e.g., SQL). Analysis methods include natural language processing technologies (e.g., NLTK, SpaCy) and machine learning algorithms (e.g., scikit-learn, TensorFlow). Results are provided using map displays (e.g., Leaflet.js) and graph displays (e.g., D3.js).

[1400] System operation explanation

[1401] 1. Data Entry

[1402] The user uses the device's input interface to input information about the search target, for example, "Tell me the location of Tokugawa's buried treasure." At this time, the emotion engine recognizes the user's emotional state at the time of input and adds it to the data.

[1403] 2. Data Collection

[1404] The device converts the information into an appropriate format (e.g., JSON format) and sends it to the server. The server processes the received data and generates queries to collect relevant data. Web crawling techniques (e.g., Scrapy) and database query techniques (e.g., SQL) are used to collect literature, papers, news articles, historical maps, etc. from the Internet and various databases.

[1405] 3. Data Analysis

[1406] The server's analysis module runs and analyzes the collected data. It uses natural language processing techniques (e.g., NLTK, SpaCy) to classify the data, and machine learning algorithms (e.g., scikit-learn, TensorFlow) to identify locations and hints related to the search target and calculate their reliability. Furthermore, it adjusts the order and content of the results based on the user's emotional state.

[1407] 4. Providing results

[1408] The server compiles the analysis results into a report and sends it to the device. The device then presents the results to the user in a visually easy-to-understand format, such as an interactive map (e.g., Leaflet.js) or a graph display (e.g., D3.js). The display method is adjusted according to the user's emotional state to provide an optimal user experience.

[1409] Specific examples

[1410] Example 1: Searching for Tokugawa's buried treasure

[1411] 1. Data Entry

[1412] The user types into the terminal, "Tell me the location of Tokugawa's buried treasure." The emotion engine recognizes the user's emotional state, such as excitement or anticipation, and adds it to the data.

[1413] 2. Data Collection

[1414] The device converts the information into JSON format and sends it to a server that collects documents, papers, news articles, historical maps, and other information related to the "Tokugawa buried treasure."

[1415] 3. Data Analysis

[1416] The server's analysis module tokenizes the data and organizes it into categories. For example, it may identify "near a certain temple in Tokyo" as an analysis result.

[1417] The order and content of the analysis results are adjusted taking into account the user's emotional state.

[1418] 4. Providing results

[1419] The server compiles the analysis results into a report and sends it to the device. The device then displays the results as an interactive map, showing users that "there is a high possibility that buried treasure is near a certain temple in Tokyo." The display style uses bright colors to match the user's expectations.

[1420] Example 2: Discovering a worm

[1421] 1. Data Entry

[1422] The user types into the device, "Tell me where Tsuchinoko appears." The emotion engine recognizes the user's excitement or surprise and adds it to the data.

[1423] 2. Data Collection

[1424] The device converts the information into a suitable format and sends it to a server, which collects data, including sightings and legends, from the internet and literature.

[1425] 3. Data Analysis

[1426] The analysis module analyzes the collected sighting information and identifies the "mountainous areas of Gifu Prefecture" as a likely area.

[1427] The display order and content are adjusted based on the user's emotional state.

[1428] 4. Providing results

[1429] The server compiles the analysis results into a report and sends it to the device. The device then visually displays the results, saying, "There have been many reports of Tsuchinoko sightings in the mountainous areas of Gifu Prefecture." The display style uses a subdued color scheme to avoid amplifying the sense of surprise.

[1430] In this way, the system of the present invention can consistently automate the process from collecting data on search targets to analyzing them and providing the results, and can present the results in a form optimized based on the user's emotional state.

[1431] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1432] Step 1:

[1433] The user inputs information about the search target using the terminal's input interface. The input information includes a prompt sentence, such as "Tell me the location of Tokugawa's buried treasure." The emotion engine uses voice tone and facial expression recognition technology to recognize the user's emotional state (e.g., excitement, anticipation, anxiety) and adds it to the input data. The input at this time is the prompt sentence and emotional information. The output is formed as a single dataset.

[1434] Step 2:

[1435] The device converts the data received from the user into an appropriate format (e.g., JSON) and sends it to the server. A programming language such as Python is used as the conversion method. The input is a dataset containing a prompt sentence and emotion information, and the output is formatted data. Specifically, the format conversion function is called, the data is converted into JSON format, and then sent to the server.

[1436] Step 3:

[1437] The server processes the received formatted data and generates queries to collect relevant data. It uses web crawling techniques (e.g., Scrapy) or database query techniques (e.g., SQL) to collect data. The input is the formatted data, and the output is the collected relevant data. Specifically, it calls the query generation function to collect the required data from the Internet or a database.

[1438] Step 4:

[1439] The server launches an analysis module (e.g., NLTK, scikit-learn) to analyze the collected data. The analysis module tokenizes the collected data using natural language processing techniques and classifies the data using machine learning algorithms. The input is the collected relevant data, and the output is the analysis results. Specific operations include tokenizing text and running a category classification algorithm.

[1440] Step 5:

[1441] The server compiles the analysis results into a report, which includes the location of the search target, related hints, and confidence data. It also configures the content to be presented in an easy-to-interpret manner based on the user's emotional state. The input is the analysis results and the user's emotional information, and the output is data in report format. Specific operations include calling a report generation function, organizing the data, and adjusting the display style according to the user's emotional state.

[1442] Step 6:

[1443] The server sends the analysis results in report format to the terminal. The terminal launches a map display (e.g., Leaflet.js) or graph display (e.g., D3.js) module to visually display the results to the user. The input is data in report format, and the output is the visually displayed results. Specific operations include launching the map display module and graph display module, and selecting the optimal display method based on the user's emotional state.

[1444] Through the above steps, this system efficiently and effectively searches for undiscovered buried treasures and unknown ruins and treasures, and provides optimal information to users.

[1445] (Application example 2)

[1446] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1447] Conventional search systems provide information without considering the user's emotional state, which can result in insufficient results. Furthermore, when detecting anomalies within a factory, providing information without considering the emotional state of the manager can reduce the effectiveness and efficiency of emergency responses. To solve these problems, technology is needed that recognizes the emotional state of the user or manager and optimizes the way information is presented based on that state.

[1448] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1449] In this invention, the server includes an input means for a user to input information about a search target, a data collection means for collecting related data from the Internet or a database based on the information received from the input means, a data analysis means for analyzing the data collected by the data collection means and identifying search hints and highly likely search points, a result provision means for providing the user with the information identified by the data analysis means, and an emotion recognition means for recognizing the user's emotional state using emotion recognition technology and adjusting the content presented by the provision means based on the user's emotional state. This makes it possible to provide optimal information according to the user's emotional state, thereby improving the effectiveness and efficiency of searches and anomaly detection.

[1450] The "input means by which the user inputs information about the search target" refers to a device or interface that allows the user to input information about the search target.

[1451] "Data collection means for collecting related data from the Internet or databases based on information received from the input means" refers to a function or device for collecting related information from the Internet or various databases based on information from the input means.

[1452] "Data analysis means for analyzing the data collected by the data collection means and identifying search hints and highly likely search points" refers to a function or device for analyzing collected data, and is used to identify information and points that are useful for searching.

[1453] The "result providing means for providing the user with the information identified by the data analysis means" refers to a function or device for conveying the information found by the data analysis means to the user.

[1454] "Emotion recognition means that recognizes the emotional state of a user using emotion recognition technology and adjusts the content presented by the providing means based on the emotional state of the user" refers to a function or device that recognizes emotions using technology that detects the user's emotions and optimizes the information and format of the information to be provided based on the results.

[1455] "Natural language processing technology" refers to technology that enables computers to understand and process human language.

[1456] A "machine learning algorithm" is a method for learning patterns from collected data and making predictions, classifications, and analyses.

[1457] "Map display means" refers to a function or device that provides information in map format to visually display the analysis results.

[1458] "Graph display means" refers to a function or device that provides information in a graph format to visually display the analysis results.

[1459] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The embodiments for carrying out the present invention will be described in detail below.

[1460] This system includes a terminal for users to input information about the search target, a server for collecting related data from the Internet or a database based on that information, and means for analyzing the collected data and providing the results to the user.

[1461] Hardware and Software Configuration

[1462] 1. Terminal

[1463] The terminal is equipped with an input means for the user to input information about the search target, and may be, for example, a smartphone, tablet, or PC. Software for using emotion recognition technology (e.g., emotion recognition API) is installed on this terminal.

[1464] 2. Server

[1465] The server includes the following means:

[1466] Data collection means: Based on the information received from the terminal, relevant data is collected from the Internet and databases. Specifically, HTTP requests are used to obtain the necessary data from various data sources (literature, papers, news articles, etc.).

[1467] Data analysis methods: Analyze the collected data by using natural language processing techniques (e.g., TextBlob) and machine learning algorithms (e.g., IsolationForest) to classify and organize the data and identify search hints and high-confidence search points.

[1468] Result presentation means: The analysis results are presented to the user. The results are adjusted based on the user's emotional state and visually presented to the user using map and graph display means.

[1469] 3. Emotion recognition means

[1470] The emotion recognition means recognizes the emotional state of the user when they input data and attaches that information to the data. This emotion recognition technology utilizes an emotion analysis library such as TextBlob.

[1471] Example of a system

[1472] Example 1: Identifying the discovery location

[1473] The user types into the terminal, "I want you to identify the abnormal part of the machine." At this time, emotion recognition technology is used to analyze the user's emotional state and recognize that the situation is anxious or urgent. The server uses this information to collect related data from the Internet and databases, and identifies the abnormal part using a machine learning algorithm. The results are then presented to the administrator in the form of a map or graph.

[1474] Example 2: Visualization of location information

[1475] The server analyzes the collected data and detects abnormalities in a specific area (e.g., a specific section of a factory). The analysis results are highlighted using map display means or graph display means depending on the manager's emotional state (e.g., if the level of urgency is high).

[1476] Prompt Sentence Examples

[1477] By entering the following prompts into the generative AI model, you can obtain information that will help with system construction and analysis.

[1478] Example prompt:

[1479] Please build a system to collect data within the factory and identify abnormalities as follows:

[1480] Collects sensor data (temperature, vibration, sound, log)

[1481] Uses an anomaly detection algorithm (Isolation Forest)

[1482] Uses Emotion Recognition (TextBlob)

[1483] The above is an embodiment of the present invention, which makes it possible to provide optimal information according to the emotional state of the user, thereby improving the effectiveness and efficiency of search and anomaly detection.

[1484] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1485] Step 1:

[1486] The user inputs information about the search target through the device's input means. For example, the user inputs information such as "Please identify the abnormal part of the machine." At this time, the device uses emotion recognition technology to analyze the user's emotional state and recognizes that the situation is anxious or urgent. The input is in text format, and the analysis result of the emotional state is also output at the same time. This information is sent to the server in the next step.

[1487] Step 2:

[1488] The device sends data to the server based on the information entered in the previous step. The data sent includes the user's input information and emotional state. The server receives this transmitted data and activates the data collection method. Specifically, based on this information, it uses HTTP requests to collect necessary data such as related literature, papers, news articles, and sensor data from the Internet and various databases. The collected data is preprocessed and converted into a format suitable for analysis.

[1489] Step 3:

[1490] The server analyzes the data collected by the data collection method. It uses natural language processing technology (TextBlob) and a machine learning algorithm (IsolationForest) to analyze the data. First, it uses TextBlob to perform text analysis of the collected data, extracting and classifying relevant information. At the same time, it applies IsolationForest to the quantitative data from the sensor to identify abnormalities. The analysis results are output in a format that takes into account the emotion recognition results along with their reliability.

[1491] Step 4:

[1492] The server creates a report to provide to the user based on the results obtained by the data analysis means. Map display means and graph display means are used to provide the results. If the analysis results indicate an abnormality in a specific area or machine, the information is visually displayed in map or graph format. Furthermore, the way in which the results are presented is adjusted according to the user's emotional state (level of urgency or anxiety). For example, if the level of urgency is high, the result is highlighted and a detailed explanation is added.

[1493] Step 5:

[1494] The server sends a final report to the terminal. The terminal receives this report and displays it to the user. The user can then review the displayed report and take any necessary action, such as making arrangements to quickly address any identified abnormalities. This process allows the user to receive optimal information based on their emotional state, improving the efficiency of search and anomaly detection.

[1495] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1496] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1497] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1498] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1499] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1500] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1501] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1502] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1503] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1504] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1505] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1506] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1507] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1508] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1509] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1510] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1511] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1512] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1513] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1514] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1515] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1516] The following is further disclosed regarding the above embodiment.

[1517] (Claim 1)

[1518] an input means for a user to input information about a search target;

[1519] a data collection means for collecting related data from the Internet or a database based on the information received from the input means;

[1520] data analysis means for analyzing the data collected by the data collection means and identifying search hints and highly accurate search points;

[1521] a result providing means for providing the information identified by the data analysis means to a user;

[1522] A system including:

[1523] (Claim 2)

[1524] The system according to claim 1, characterized in that the data analysis means classifies and organizes related information using natural language processing technology and machine learning algorithms, and identifies the location to be searched.

[1525] (Claim 3)

[1526] 2. The system according to claim 1, wherein the result providing means includes a map display means and a graph display means for visually displaying the analysis results relating to the identified location.

[1527] "Example 1"

[1528] (Claim 1)

[1529] an input means for a user to input information about a search target;

[1530] a format conversion means for converting the information received from the input means into JSON format data and transmitting the data to a server;

[1531] a data collection means for collecting related data from the Internet or a database based on the data generated by the format conversion means;

[1532] a data analysis means for analyzing the data collected by the data collection means and identifying search hints and highly accurate search points using natural language processing technology and machine learning algorithms;

[1533] a result providing means for providing the information identified by the data analysis means to a user;

[1534] A system including:

[1535] (Claim 2)

[1536] The system according to claim 1, characterized in that the data analysis means classifies and organizes related information using natural language processing technology and machine learning algorithms, and identifies the location to be searched.

[1537] (Claim 3)

[1538] 2. The system according to claim 1, wherein the result providing means includes a map display means and a graph display means for visually displaying the analysis results relating to the identified location.

[1539] "Application Example 1"

[1540] (Claim 1)

[1541] an input means for a user to input information about a search target;

[1542] a data collection means for collecting related data from the Internet or a database based on the information received from the input means;

[1543] data analysis means for analyzing the data collected by the data collection means and identifying search hints and highly accurate search points;

[1544] a result providing means for providing the information identified by the data analysis means to a user;

[1545] a result display means for visually displaying the analysis results using maps and graphs;

[1546] A system including:

[1547] (Claim 2)

[1548] The system according to claim 1, characterized in that the data analysis means classifies and organizes related information using natural language processing technology and machine learning algorithms, and identifies the location to be searched.

[1549] (Claim 3)

[1550] 2. The system according to claim 1, wherein the result display means includes a geographic display means for displaying the analysis results relating to the identified location as geographic information.

[1551] "Example 2: Combining Emotion Engines"

[1552] (Claim 1)

[1553] an input means for a user to input information about a search target;

[1554] a data collection means for collecting related data from the Internet or a database based on the information received from the input means;

[1555] data analysis means for analyzing the data collected by the data collection means and identifying search hints and highly accurate search points;

[1556] a result providing means for providing the information identified by the data analysis means to a user;

[1557] emotion recognition means for recognizing the emotional state of the user and optimizing the presentation of results accordingly;

[1558] A system including:

[1559] (Claim 2)

[1560] The system according to claim 1, characterized in that the data analysis means classifies and organizes related information using natural language processing technology and machine learning algorithms, and identifies the location to be searched.

[1561] (Claim 3)

[1562] 2. The system according to claim 1, wherein the result providing means includes a map display means and a graph display means for visually displaying the analysis results relating to the identified location.

[1563] "Application example 2 when combining emotion engines"

[1564] (Claim 1)

[1565] an input means for a user to input information about a search target;

[1566] a data collection means for collecting related data from the Internet or a database based on the information received from the input means;

[1567] data analysis means for analyzing the data collected by the data collection means and identifying search hints and highly accurate search points;

[1568] a result providing means for providing the information identified by the data analysis means to a user;

[1569] an emotion recognition means for recognizing an emotional state of a user using an emotion recognition technology and adjusting the content presented by the providing means based on the emotional state of the user;

[1570] A system including:

[1571] (Claim 2)

[1572] The system according to claim 1, characterized in that the data analysis means classifies and organizes related information using natural language processing technology and machine learning algorithms, and identifies the location to be searched.

[1573] (Claim 3)

[1574] 2. The system according to claim 1, wherein the result providing means includes a map display means and a graph display means for visually displaying the analysis results relating to the identified location. [Explanation of symbols]

[1575] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. an input means for a user to input information about a search target; a data collection means for collecting related data from the Internet or a database based on the information received from the input means; data analysis means for analyzing the data collected by the data collection means and identifying search hints and highly accurate search points; a result providing means for providing the information identified by the data analysis means to a user; A system including:

2. The system according to claim 1, wherein the data analysis means uses natural language processing technology and machine learning algorithms to classify and organize related information and identify locations to be searched.

3. 2. The system according to claim 1, wherein the result providing means includes map display means and graph display means for visually displaying the analysis results relating to the identified location.

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