system

Abstract

We provide the system. [Solution] A method for collecting and normalizing real estate data from across the country, A means of predicting market trends based on the aforementioned collected data, A means of selecting and recommending properties based on the user's desired conditions, A means for generating a report from the aforementioned market forecast and property information, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] With the soaring prices and fluctuating demands in the real estate market, it has become increasingly difficult to make appropriate real estate purchases and investments. When consumers try to investigate the market and select the optimal property by themselves, a huge amount of information and complex analysis are required, which takes a lot of time and effort. Moreover, the information provided by real estate agents has a strong business color, and it is difficult for consumers to obtain objective advice as expected. In such a situation, there is a need for a means for consumers to make efficient and optimal decisions using reliable data.

Means for Solving the Problems

[0005] This invention provides means for collecting and normalizing real estate data nationwide, and means for predicting market trends based on the collected data. Furthermore, it provides a system that includes means for selecting and recommending properties based on user preferences, and means for generating detailed reports based on market forecast results and property information, thereby enabling consumers to easily make optimal real estate purchase and investment decisions. This supports objective and data-driven decision-making, rather than relying solely on subjective advice in traditional real estate consultations.

[0006] "Nationwide real estate data" refers to information about real estate located within Japan, including details such as price, location, transaction history, and surrounding environment.

[0007] "Normalization" is the process of organizing collected information into a specific format in order to improve data consistency and readability.

[0008] "Market trend forecasting" refers to analysis that predicts future changes in prices and demand in the real estate market.

[0009] "User preferences" refer to filtering criteria such as area, budget, and property type set by consumers considering purchasing or investing in real estate.

[0010] "Property recommendation" refers to the process of presenting properties that are deemed most suitable based on the user's desired conditions.

[0011] "Report generation" refers to creating documents and information that users can use as reference in their decision-making, based on analysis results and property details.

[0012] "System" refers to a computer program and its execution environment that integrates the above functions and provides a consistent service to the user. [Brief explanation of the drawing]

[0013] [Figure 1]This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0014] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, a labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, a labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0019] [[ID=I8]] In the following embodiments, a labeled communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), etc.

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] The real estate purchase and investment support system according to the present invention provides users with market trend forecasts and optimal property suggestions through the collection, analysis, and recommendation of real estate data from across the country. This system consists of multiple components.

[0035] First, the server automatically collects data from a wide-ranging real estate database. This data includes information of interest to the user, such as property prices, location, surrounding environment, demographics, and past transaction information. Next, the server uses machine learning algorithms and statistical methods based on this data to predict market trends and stores the resulting predictions in the database.

[0036] Users can use the terminal interface to enter their desired property criteria, including preferred area, budget, and property type. Based on these criteria, the server searches a pre-stored database for the most suitable properties and recommends them to the user. The recommendations are displayed visually in a ranking format, and by clicking on "Details," users can view more detailed information about each property.

[0037] Furthermore, the server generates individual reports based on predicted market trends and property information, and provides these reports to users. These reports include detailed information such as price fluctuation graphs, property investment yields, and area growth potential, which users can use to make decisions about purchasing or investing in real estate.

[0038] For example, if a user is looking for a 3LDK apartment in a certain city, the system will quickly present information on properties that potentially match those criteria, and also provide analysis results regarding price trends and future growth potential in that area. This allows the user to make better decisions based on the information.

[0039] This system could also be useful for real estate agents, as it could streamline customer service and function as a sales support tool, enabling them to provide better service to customers.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The server collects real estate-related data from nationwide real estate databases and APIs from public institutions. This data includes property prices, location information, transaction history, and information about the surrounding living environment.

[0043] Step 2:

[0044] The server normalizes the collected data, removing unnecessary data and imputing missing values ​​to ensure consistency and reliability. This ensures that the information in the database is managed in a state suitable for analysis.

[0045] Step 3:

[0046] The server uses pre-configured data and employs machine learning models and time-series analysis to predict future trends in the real estate market. This prediction generates information on areas where prices are expected to rise and types of properties that will see increased demand.

[0047] Step 4:

[0048] Users input their desired conditions, such as preferred area, price range, and property type, via their device. This allows the system to accurately understand the user's needs.

[0049] Step 5:

[0050] The server uses the user's input preferences to score and rank the most suitable property candidates from a pre-prepared database. This ensures that properties that match the user's preferences are recommended preferentially.

[0051] Step 6:

[0052] The server generates a detailed report for the user regarding market trends and recommended properties. This report includes estimated price fluctuation graphs, investment yields, and the attractiveness of the surrounding environment.

[0053] Step 7:

[0054] The terminal displays the generated report and property details to the user. The user can use this information to make decisions regarding real estate purchases and investments.

[0055] (Example 1)

[0056] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0057] In the real estate market, extracting useful information quickly and accurately from a vast amount of data, and selecting and proposing the most suitable properties for users, is a challenging task. Furthermore, there is a lack of information necessary to predict market changes and make informed real estate investment decisions. There is a need for a system that can effectively address these challenges.

[0058] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0059] In this invention, the server includes means for collecting and normalizing real estate information from across the country, means for predicting market changes based on the collected information, and means for selecting and recommending properties based on user requirements using an information processing algorithm. This enables rapid and accurate property selection based on a vast amount of real estate information, as well as the provision of information based on market change predictions.

[0060] "Nationwide real estate information" refers to detailed information about land and buildings across the entire country, including prices, locations, and transaction history, regardless of specific regions.

[0061] "Normalization" refers to the process of unifying the format of collected information and imputing missing values ​​or correcting inappropriate values ​​in order to maintain data consistency.

[0062] "Predicting market changes" refers to using statistical methods and data analysis models to forecast price trends and demand fluctuations in the real estate market in advance.

[0063] An "information processing algorithm" refers to a set of calculation procedures or processing methods based on input data, and is a means of processing data according to a specific purpose and deriving a result.

[0064] "User requirements" refer to the specific desires and conditions that the user has regarding the property they are looking for, including, for example, price, location, and the condition of the property.

[0065] "Selecting and recommending properties" refers to extracting properties that best match the user's requirements based on information in the database and recommending them to the user.

[0066] "Generating and presenting reports" refers to compiling the results of market and property data analysis into documents or digital formats and providing them to users in an easy-to-understand manner.

[0067] "Transmitting to the customer's device" refers to transferring generated information and reports to the user's electronic device via the internet or other means of communication.

[0068] The real estate purchase and investment support system of this invention is composed of three components: a server, a terminal, and a user.

[0069] First, the server collects real estate information from across the country. During this process, the server retrieves data from various sources via the internet and uses Python scraping libraries (e.g., BeautifulSoup or Selenium) to collect the information. The collected data is then organized through a normalization process and stored in an SQL or NoSQL database. This database contains real estate-related information such as property prices, location, and surrounding environment.

[0070] Next, the server uses data analysis models such as linear regression and random forests to predict market changes based on the stored data. In this process, data analysis software and libraries are utilized to obtain predictions of future price trends and demand, which are then stored in the database.

[0071] Users enter their desired property criteria through an application installed on their device. This application operates in web or mobile app format and collects detailed user preferences such as area, budget, and property type.

[0072] The terminal sends user input information to the server, which then selects and searches for the most suitable properties from its database. The recommended property list is then displayed on the user's screen in a ranked format, and more detailed information can be viewed by clicking on the details of each property. Generated market forecasts and property reports are also presented to the user through the terminal. These reports include property price fluctuation graphs and area growth potential.

[0073] As a concrete example, when a user enters their desired criteria, such as "I'm looking for a 3LDK apartment in that city," into the system, the server searches the database based on those criteria and displays the most suitable properties in ranking order. This allows the user to quickly access information and make purchasing decisions. An example of a prompt sentence to be entered into the generating AI model is, "Tell me the price trends for 3LDK apartments within my budget in a specific area."

[0074] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0075] Step 1:

[0076] The server collects real estate information from across Japan. Specifically, it obtains information from various data sources via the internet. Using a Python scraping library, it automatically collects data on property prices, locations, and surrounding environments. It uses URLs and API access information from each information source as input, and outputs real estate information in raw data format.

[0077] Step 2:

[0078] The server normalizes the collected real estate information. Because the collected data may contain inconsistencies and missing values, it cleans the data and standardizes its format. Specifically, it unifies price information in different units and imputes missing values. It uses raw real estate data as input and generates a clean, standardized database as output.

[0079] Step 3:

[0080] The server predicts market changes based on normalized data. It uses machine learning algorithms to forecast future price trends and demand fluctuations. This process utilizes trained predictive models and employs statistical methods. Normalized real estate data is used as input, and predictive data on market changes is generated as output.

[0081] Step 4:

[0082] The user enters their desired property criteria through an interface installed on their device. These criteria include area, budget, and property type, and are sent to the server. The server uses the user's desired criteria as input and outputs search results.

[0083] Step 5:

[0084] The server selects the most suitable property from the database based on the user's preferences. It executes queries against the database and extracts properties that match the criteria. Using the user's preferences and normalized data as input, it obtains a list of recommended property information as output.

[0085] Step 6:

[0086] The terminal displays a list of recommended properties received from the server to the user. These are displayed on the screen in a ranking format, and links to access detailed information for each property are provided. The input is the recommended property information from the server, and the output is a visual display of the properties on the user's terminal.

[0087] (Application Example 1)

[0088] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0089] When purchasing or investing in real estate, consumers face the challenge of difficulty in quickly and effectively obtaining the information they need to make informed decisions. Furthermore, the information available to consumers during consultations at real estate agencies is limited, highlighting the need for more impactful visual support.

[0090] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0091] In this invention, the server includes means for collecting and normalizing geographic data from across the country, means for predicting market trends based on the collected information, and means for providing information visually in real time using glasses-type display devices. This enables consumers to obtain visual information in real time at physical stores and make informed decisions.

[0092] "Geographic data" refers to a collection of data that includes information about the location of a property.

[0093] "Normalization" refers to the process of converting data expressed in different formats or units into a unified format.

[0094] "Market trend forecasting" is the process of analyzing past data and current conditions to predict future changes in the real estate market.

[0095] "User's desired conditions" refers to the conditions and features that users particularly value when purchasing or investing in real estate.

[0096] "Selection and recommendation" means choosing and recommending properties and information that best suit the user's desired conditions.

[0097] A "report" refers to a detailed informational document generated based on market forecasts and property information.

[0098] A "glasses-type display device" refers to a glasses-like device that can visually display digital information.

[0099] The system for implementing the present invention is composed of a combination of various hardware and software. The main components include a server for processing information, a glasses-type display device for the user to view the information, and a cloud computing environment to support data processing.

[0100] The server first collects geographical data from across the country and normalizes it. This process uses programming languages ​​such as Python to convert data provided in different formats into a unified format. Subsequently, machine learning algorithms are used to predict market trends. Frameworks used include TENSORFLOW® and Scikit-learn, which analyze trends in large amounts of historical data to predict future market trends with high accuracy.

[0101] When a user enters their criteria of interest, the server uses this information to select the most suitable properties and facilities from collected data and transmits the information to a glasses-type display device in real time. AWS (Amazon Web Services) cloud infrastructure is used for high-speed data processing and communication. The glasses-type display device is a device similar to smart glasses; users wear this device in physical stores to visually acquire information and receive support in their decision-making process.

[0102] As a concrete example, consider a family visiting a real estate agency to purchase a home. This family can receive real-time information about potential properties through smart glasses, visually checking price trends, surrounding environment, and future growth forecasts for each property.

[0103] Furthermore, as an example of a prompt message, if the user inputs, "I'm looking for a 3LDK property in Tokyo's 23 wards. My budget is up to 50 million yen, and I prioritize a family-friendly environment. Please tell me about market trends," the server will quickly analyze and recommend properties that meet the relevant conditions. In this way, the present invention becomes a powerful support tool for consumers to make more informed decisions.

[0104] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0105] Step 1:

[0106] The server collects geographic data from an external database across the country. The input data includes property locations, prices, surrounding environment information, and past transaction history. The collected data is normalized and converted into a unified format to ensure data consistency and readability. The output of this step is the normalized geographic data.

[0107] Step 2:

[0108] The server predicts market trends using normalized geographical data. It uses historical transaction history and geographical data as input and applies machine learning algorithms. Specifically, it uses Python and frameworks such as TensorFlow to build predictive models. The output obtained here represents future market trends, such as probability distributions and price trend graphs.

[0109] Step 3:

[0110] The user enters their desired criteria through the terminal interface. This information includes the desired area, budget, and property type. Based on this, the server interactively analyzes the criteria and performs filtering. The output is a list of properties that match the filtered criteria.

[0111] Step 4:

[0112] The server sends the filtered property list to the glasses-type display device. By using AWS cloud services to process the data in real time, the information is instantly displayed on the glasses-type display device worn by the user. The input is the filtered property list, and the output is real estate options presented to the user visually.

[0113] Step 5:

[0114] Users use glasses-type display devices to review and compare the information they receive. The device displays property attributes, price trends, and area characteristics, allowing users to view detailed information directly. The output serves to support user decision-making.

[0115] Step 6:

[0116] The server generates detailed market analysis reports for properties that users are interested in. These reports include predicted market trends, property return on investment, and evaluations of surrounding commercial facilities. These reports serve as a reference for users to provide feedback and assist in decision-making. Outputs include detailed PDF reports and interactive data visualizations.

[0117] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0118] The real estate purchase and investment support system according to the present invention, in addition to collecting and analyzing real estate data from across the country, is equipped with an emotion engine that recognizes the user's emotions and reflects them in property recommendations and market trend forecasts. This system combines multiple components to provide personalized suggestions to the user.

[0119] First, the server collects data such as price, location information, transaction history, and surrounding environment information from a nationwide real estate database. This data is normalized and consistently maintained in the database. Next, the server analyzes the data using machine learning algorithms to predict future market trends. This makes it easier to understand fluctuations in real estate prices and demand trends.

[0120] Users input their desired criteria, such as area, budget, and property type, through their device. Furthermore, an emotion engine monitors user interactions and analyzes emotional data from text input and responses. This analyzed emotional data is then used for subsequent property recommendations and report generation, creating personalized suggestions that take into account the user's emotional state and preferences.

[0121] For example, if a user is feeling stressed, the server can detect this using an emotion engine and prioritize selecting and recommending properties located in calmer environments. This increases the likelihood that the user will find a comfortable place to live.

[0122] During the report generation phase, the server considers market trends, property information, and user sentiment to create a detailed report to support decision-making. This report includes price trends, regional attractiveness, and long-term investment value, allowing users to make the best choices based on it.

[0123] Thus, by combining this system with an emotion engine, it provides comprehensive support for real estate purchases and investments that takes into account not only data and algorithms, but also the emotional aspects of the user.

[0124] The following describes the processing flow.

[0125] Step 1:

[0126] The server regularly collects real estate data from nationwide real estate databases and related online resources. The collected data includes property prices, location conditions, past transaction history, and information on local amenities.

[0127] Step 2:

[0128] The server cleanses and normalizes the collected data and stores it in the database. This process maintains data consistency by removing duplicate data, imputing missing values, and standardizing data formats.

[0129] Step 3:

[0130] The server uses pre-configured data to run machine learning models and predict market trends. It analyzes price fluctuations and demand trends in specific regions and stores the results in a database.

[0131] Step 4:

[0132] Users enter their desired conditions into the interface from their device. These conditions include preferred area, budget, property type, and priority equipment requirements.

[0133] Step 5:

[0134] The device uses an emotion engine to analyze the input text and understand the user's emotional state. For example, it identifies positive, negative, and neutral emotions from the input content and chosen actions.

[0135] Step 6:

[0136] The server comprehensively evaluates the user's preferences and emotional state, and selects the most suitable property from the database. In this process, the ranking of recommended properties is adjusted according to the emotional state, generating a personalized list.

[0137] Step 7:

[0138] The terminal visually presents the generated property list to the user. The list includes detailed property information, local characteristics, and predicted market trends, allowing the user to consider properties based on this information.

[0139] Step 8:

[0140] The server generates reports reflecting market trends and sentiment analysis results, and provides them to users via their terminals. These reports include the future investment value of selected properties and the latest market analysis results, serving as valuable information to support user decision-making.

[0141] (Example 2)

[0142] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0143] Traditional real estate purchase and investment support systems lack personalized recommendations that take into account the user's emotional needs, making it difficult to select properties that align with the user's desires and feelings. Furthermore, they sometimes lack sufficient methods for accurately predicting market trends and creating detailed reports.

[0144] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0145] In this invention, the server includes means for collecting and normalizing real estate information from across the country, means for predicting market trends based on the collected information, means for selecting and recommending properties based on the user's desired conditions and sentiment data, and means for generating a detailed report from the market forecast, property information, and user sentiment data. This enables real estate selection that meets the user's sentiments and desires, and allows for the provision of detailed information based on market forecasts.

[0146] "Nationwide real estate information" refers to all data related to real estate, including information such as price, location, transaction history, and surrounding environment.

[0147] "Normalization" refers to the process of making collected data consistent and arranging it in a way that facilitates management and analysis in a database.

[0148] "Means of predicting market trends" refers to algorithms and methods that use current and historical data to predict future market changes and trends.

[0149] "User's desired conditions" refers to the specific conditions and requests that users have when purchasing or investing in real estate, including area, budget, and property type.

[0150] "Emotional data" refers to data representing emotional states extracted from user text input and interactions, and is used for recommendation and report generation.

[0151] "Methods for selecting and recommending properties" refers to a system that takes into account the user's desired conditions and emotional data to select and present appropriate real estate.

[0152] "Means of generating detailed reports" refers to the process of organizing information to support investment decisions based on collected data, market forecasts, and user sentiment data, and providing it in a format that users can utilize.

[0153] This invention relates to an information system intended to support real estate purchase and investment. The system primarily consists of a server, terminals, and users, and provides users with personalized real estate information.

[0154] The server collects real estate information from across the country and normalizes it based on specific criteria. This includes data collection using databases and APIs. Furthermore, machine learning techniques are used to accurately predict market trends from the collected data. For this purpose, open-source machine learning libraries such as TensorFlow are utilized. For example, by analyzing historical data, future trends in real estate prices in specific regions can be predicted.

[0155] Users access the system via a terminal and input their desired conditions. Furthermore, emotional data is analyzed from the user's input data and interactions. The emotional analysis incorporates a mechanism that uses natural language processing technology to extract and analyze emotions from the user's input text.

[0156] The server selects and recommends properties suitable for the user based on their preferences and sentiment data. This is done by a pre-built recommendation engine. Specifically, if a user expresses a desire to "live in a quiet place surrounded by nature," the system will prioritize recommending properties with suitable environmental conditions based on that information.

[0157] The generated results are displayed on the terminal and also provided as a detailed report. This report includes analysis results based on real estate market trends, individual property information, and user sentiment, providing strong support for users' investment decisions.

[0158] For example, the following prompt statements can be used in a generative AI model:

[0159] "What are the characteristics of properties recommended by the real estate support system for users looking for properties in a quiet environment?"

[0160] With the above configuration, the present invention is capable of providing users with more detailed and personalized information than conventional real estate information provision systems, thereby supporting decision-making regarding real estate investment and purchase.

[0161] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0162] Step 1:

[0163] The server collects real estate information. It uses APIs to retrieve information such as price, location, transaction history, and surrounding environment from a nationwide real estate database. Input is from an external data source, and output is normalized data. Specifically, the server uses scripts such as Python to format the retrieved data into a consistent format.

[0164] Step 2:

[0165] The server uses normalized data as input to a machine learning model to predict market trends. This utilizes a machine learning algorithm powered by TensorFlow. The input is normalized data, and the output is a prediction of future market trends. Specifically, it analyzes historical data to predict future price fluctuations.

[0166] Step 3:

[0167] The user enters their desired conditions through a terminal. These conditions include area, budget, and property type. The output is sent to the server. Specifically, the user enters data into an input form via the user interface, and this data is sent to the server.

[0168] Step 4:

[0169] The server analyzes user input and emotional data. Emotional data is collected using natural language processing (NLP) techniques based on the input text data. The input is text data from the user, and the output is the analyzed emotional state. Specifically, NLP techniques are used to extract emotions such as positive and negative from the user's input.

[0170] Step 5:

[0171] The server selects and recommends properties based on the analysis results. It combines the user's desired conditions and sentiment data to select the most suitable properties and generate a list. The input is the user's conditions and sentiment data, and the output is a list of recommended properties. Specifically, it searches the property database based on the user's request and creates a list of selected properties.

[0172] Step 6:

[0173] The server generates a detailed report and sends it to the terminal. The report is created considering factors such as real estate market forecasts, property information best suited to the user, and sentiment data. Inputs include market forecasts, property information, and sentiment data, while output is the report. Specifically, the server formats the generated information in LaTeX or PDF format and prepares it for transmission to the user.

[0174] (Application Example 2)

[0175] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0176] This invention aims to solve the problem that conventional real estate information systems do not adequately consider the user's emotions, making it difficult to recommend the most suitable property for the user. Furthermore, it aims to support more user-friendly real estate purchase and investment decision-making by enabling dynamic property recommendations based on the user's emotional state.

[0177] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0178] In this invention, the server includes means for collecting and normalizing asset information from across the country, means for analyzing emotions from data acquired by an information processing device, and means for selecting and recommending assets based on the user's desired conditions and the results of the emotion analysis. This enables personalized asset recommendations that take into account the user's emotional state.

[0179] "Asset information" refers to attribute data such as price, location, transaction history, and surrounding environment related to properties such as real estate.

[0180] "Means for analyzing emotions" refers to a device or method that has the function of analyzing a user's emotional state from data and performing a process of quantifying emotions as numerical values ​​or categories.

[0181] "Desired conditions" refer to the requests and requirements specified by the user when selecting a particular property, including price range, geographical conditions, property type, etc.

[0182] "Means for generating reports" refers to a device or method that has the function of compiling and presenting information for users to make decisions based on market trend forecasts, asset information, and the emotional state of users.

[0183] A "computational model" is a mathematical method used to predict market changes based on data, and includes machine learning algorithms and statistical models.

[0184] The system for realizing this invention includes a server, a terminal, and a user interface as its main components. It collects asset information such as real estate and analyzes the user's emotions to recommend assets that meet individual needs.

[0185] The server retrieves nationwide asset information from a database and normalizes the data to maintain consistency. It uses Apache® Kafka to process and provide real estate information in real time. Furthermore, the server uses computational models with machine learning algorithms to predict market trends. PostgreSQL is used for database management to efficiently organize and store the collected data.

[0186] The device receives desired conditions and emotional data from the user. The software used to analyze the user's emotions employs natural language processing technologies such as OpenAI's GPT model to extract and analyze emotions from the user's input information.

[0187] When a user searches for a property, the device sends a request to the server based on collected emotional data and desired criteria. The server then analyzes market trends and asset information to generate a report recommending the most suitable property for the user, which is then provided to the user via the device. Based on this report, the user can make a more emotionally and logically optimal decision.

[0188] For example, if a user is looking for a property in a waterfront area with the intention of relaxing on the weekend, the device sends conditions such as "quiet location close to the sea" and "a relaxing environment" to the server. The server receives this information and, considering market trends, property information, and sentiment analysis results, generates a list of optimal properties and presents it to the user.

[0189] An example of a prompt message would be, "Please suggest the optimal housing environment based on the user's current emotions," which would then be used to instruct the generative AI model.

[0190] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0191] Step 1:

[0192] The server retrieves property information from a database across the country. The input is a database of real estate properties, and the output is detailed property information. The data is normalized via Apache Kafka and stored in the database.

[0193] Step 2:

[0194] The terminal receives the user's desired conditions. The conditions entered by the user through the interface are treated as input, and the terminal prepares this information for transmission to the server. The output is data formatted as the user's desired information.

[0195] Step 3:

[0196] The device acquires text information from the user and uses a generative AI model for sentiment analysis. The input is the user's text information, and the output is data indicating the emotional state. Natural language is analyzed using OpenAI's GPT model, and emotions are quantified.

[0197] Step 4:

[0198] The server predicts market trends based on user sentiment data and emotional state data. The input is user sentiment data and sentiment data, and the output is market forecast information. A computational model is used, and machine learning algorithms are employed to simulate market fluctuations.

[0199] Step 5:

[0200] The server combines market trends and asset information to select suitable assets for the user and generate a recommendation list. The input is market forecast information and asset data, and the output is a list of recommended properties.

[0201] Step 6:

[0202] The terminal receives a list of recommendations from the server and displays it to the user. The input is the list of recommendations from the server, and the output is the property information displayed on the user's terminal. The user makes a decision based on this information.

[0203] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0204] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0205] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0206] [Second Embodiment]

[0207] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0208] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0209] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0210] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0211] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0212] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0213] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0214] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0215] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0216] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0217] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0218] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0219] The real estate purchase and investment support system according to the present invention provides users with market trend forecasts and optimal property suggestions through the collection, analysis, and recommendation of real estate data from across the country. This system consists of multiple components.

[0220] First, the server automatically collects data from a wide-ranging real estate database. This data includes information of interest to the user, such as property prices, location, surrounding environment, demographics, and past transaction information. Next, the server uses machine learning algorithms and statistical methods based on this data to predict market trends and stores the resulting predictions in the database.

[0221] Users can use the terminal interface to enter their desired property criteria, including preferred area, budget, and property type. Based on these criteria, the server searches a pre-stored database for the most suitable properties and recommends them to the user. The recommendations are displayed visually in a ranking format, and by clicking on "Details," users can view more detailed information about each property.

[0222] Furthermore, the server generates individual reports based on predicted market trends and property information, and provides these reports to users. These reports include detailed information such as price fluctuation graphs, property investment yields, and area growth potential, which users can use to make decisions about purchasing or investing in real estate.

[0223] For example, if a user is looking for a 3LDK apartment in a certain city, the system will quickly present information on properties that potentially match those criteria, and also provide analysis results regarding price trends and future growth potential in that area. This allows the user to make better decisions based on the information.

[0224] This system could also be useful for real estate agents, as it could streamline customer service and function as a sales support tool, enabling them to provide better service to customers.

[0225] The following describes the processing flow.

[0226] Step 1:

[0227] The server collects real estate-related data from nationwide real estate databases and APIs from public institutions. This data includes property prices, location information, transaction history, and information about the surrounding living environment.

[0228] Step 2:

[0229] The server normalizes the collected data, removing unnecessary data and imputing missing values ​​to ensure consistency and reliability. This ensures that the information in the database is managed in a state suitable for analysis.

[0230] Step 3:

[0231] The server uses pre-configured data and employs machine learning models and time-series analysis to predict future trends in the real estate market. This prediction generates information on areas where prices are expected to rise and types of properties that will see increased demand.

[0232] Step 4:

[0233] Users input their desired conditions, such as preferred area, price range, and property type, via their device. This allows the system to accurately understand the user's needs.

[0234] Step 5:

[0235] The server uses the user's input preferences to score and rank the most suitable property candidates from a pre-prepared database. This ensures that properties that match the user's preferences are recommended preferentially.

[0236] Step 6:

[0237] The server generates a detailed report for the user regarding market trends and recommended properties. This report includes estimated price fluctuation graphs, investment yields, and the attractiveness of the surrounding environment.

[0238] Step 7:

[0239] The terminal displays the generated report and property details to the user. The user can use this information to make decisions regarding real estate purchases and investments.

[0240] (Example 1)

[0241] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0242] In the real estate market, extracting useful information quickly and accurately from a vast amount of data, and selecting and proposing the most suitable properties for users, is a challenging task. Furthermore, there is a lack of information necessary to predict market changes and make informed real estate investment decisions. There is a need for a system that can effectively address these challenges.

[0243] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0244] In this invention, the server includes means for collecting and normalizing real estate information from across the country, means for predicting market changes based on the collected information, and means for selecting and recommending properties based on user requirements using an information processing algorithm. This enables rapid and accurate property selection based on a vast amount of real estate information, as well as the provision of information based on market change predictions.

[0245] "Nationwide real estate information" refers to detailed information about land and buildings across the entire country, including prices, locations, and transaction history, regardless of specific regions.

[0246] "Normalization" refers to the process of unifying the format of collected information and imputing missing values ​​or correcting inappropriate values ​​in order to maintain data consistency.

[0247] "Predicting market changes" refers to using statistical methods and data analysis models to forecast price trends and demand fluctuations in the real estate market in advance.

[0248] An "information processing algorithm" refers to a set of calculation procedures or processing methods based on input data, and is a means of processing data according to a specific purpose and deriving a result.

[0249] "User requirements" refer to the specific desires and conditions that the user has regarding the property they are looking for, including, for example, price, location, and the condition of the property.

[0250] "Selecting and recommending properties" refers to extracting properties that best match the user's requirements based on information in the database and recommending them to the user.

[0251] "Generating and presenting reports" refers to compiling the results of market and property data analysis into documents or digital formats and providing them to users in an easy-to-understand manner.

[0252] "Transmitting to the customer's device" refers to transferring generated information and reports to the user's electronic device via the internet or other means of communication.

[0253] The real estate purchase and investment support system of this invention is composed of three components: a server, a terminal, and a user.

[0254] First, the server collects real estate information from across the country. During this process, the server retrieves data from various sources via the internet and uses Python scraping libraries (e.g., BeautifulSoup or Selenium) to collect the information. The collected data is then organized through a normalization process and stored in an SQL or NoSQL database. This database contains real estate-related information such as property prices, location, and surrounding environment.

[0255] Next, the server uses data analysis models such as linear regression and random forests to predict market changes based on the stored data. In this process, data analysis software and libraries are utilized to obtain predictions of future price trends and demand, which are then stored in the database.

[0256] Users enter their desired property criteria through an application installed on their device. This application operates in web or mobile app format and collects detailed user preferences such as area, budget, and property type.

[0257] The terminal sends user input information to the server, which then selects and searches for the most suitable properties from its database. The recommended property list is then displayed on the user's screen in a ranked format, and more detailed information can be viewed by clicking on the details of each property. Generated market forecasts and property reports are also presented to the user through the terminal. These reports include property price fluctuation graphs and area growth potential.

[0258] As a concrete example, when a user enters their desired criteria, such as "I'm looking for a 3LDK apartment in that city," into the system, the server searches the database based on those criteria and displays the most suitable properties in ranking order. This allows the user to quickly access information and make purchasing decisions. An example of a prompt sentence to be entered into the generating AI model is, "Tell me the price trends for 3LDK apartments within my budget in a specific area."

[0259] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0260] Step 1:

[0261] The server collects real estate information from across Japan. Specifically, it obtains information from various data sources via the internet. Using a Python scraping library, it automatically collects data on property prices, locations, and surrounding environments. It uses URLs and API access information from each information source as input, and outputs real estate information in raw data format.

[0262] Step 2:

[0263] The server normalizes the collected real estate information. Because the collected data may contain inconsistencies and missing values, it cleans the data and standardizes its format. Specifically, it unifies price information in different units and imputes missing values. It uses raw real estate data as input and generates a clean, standardized database as output.

[0264] Step 3:

[0265] The server predicts market changes based on normalized data. It uses machine learning algorithms to forecast future price trends and demand fluctuations. This process utilizes trained predictive models and employs statistical methods. Normalized real estate data is used as input, and predictive data on market changes is generated as output.

[0266] Step 4:

[0267] The user enters their desired property criteria through an interface installed on their device. These criteria include area, budget, and property type, and are sent to the server. The server uses the user's desired criteria as input and outputs search results.

[0268] Step 5:

[0269] The server selects the most suitable property from the database based on the user's preferences. It executes queries against the database and extracts properties that match the criteria. Using the user's preferences and normalized data as input, it obtains a list of recommended property information as output.

[0270] Step 6:

[0271] The terminal displays a list of recommended properties received from the server to the user. These are displayed on the screen in a ranking format, and links to access detailed information for each property are provided. The input is the recommended property information from the server, and the output is a visual display of the properties on the user's terminal.

[0272] (Application Example 1)

[0273] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0274] When purchasing or investing in real estate, consumers face the challenge of difficulty in quickly and effectively obtaining the information they need to make informed decisions. Furthermore, the information available to consumers during consultations at real estate agencies is limited, highlighting the need for more impactful visual support.

[0275] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0276] In this invention, the server includes means for collecting and normalizing geographic data from across the country, means for predicting market trends based on the collected information, and means for providing information visually in real time using glasses-type display devices. This enables consumers to obtain visual information in real time at physical stores and make informed decisions.

[0277] "Geographic data" refers to a collection of data that includes information about the location of a property.

[0278] "Normalization" refers to the process of converting data expressed in different formats or units into a unified format.

[0279] "Market trend forecasting" is the process of analyzing past data and current conditions to predict future changes in the real estate market.

[0280] "User's desired conditions" refers to the conditions and features that users particularly value when purchasing or investing in real estate.

[0281] "Selection and recommendation" means choosing and recommending properties and information that best suit the user's desired conditions.

[0282] The "report" refers to a detailed information document generated based on market forecasts and property information.

[0283] The "eyeglass-type display device" refers to a device in the shape of glasses that can visually display digital information.

[0284] The system for implementing the present invention is configured by combining various hardware and software. The main components include a server for processing information, an eyeglass-type display device for users to check information, and a cloud computing environment to support data processing.

[0285] The server first collects geographical data across the country and normalizes it. In this process, data provided in different formats is converted into a unified format using a programming language such as Python. Then, a machine learning algorithm is used to predict market trends. The frameworks used are TensorFlow, Scikit-learn, etc., which analyze the trends of a large amount of past data and accurately predict future market trends.

[0286] When a user inputs conditions of interest, the server selects the optimal properties and facilities from the collected data based on this information and transmits the information to the eyeglass-type display device in real time. Here, the cloud infrastructure of AWS (Amazon Web Services) is utilized to perform high-speed data processing and communication. The eyeglass-type display device is a device such as smart glasses, and the user can visually obtain information by wearing this device in a physical store, which provides support during the decision-making process.

[0287] As a specific example, consider the case where a family visits a store to purchase a house. Through the smart glasses, this family can receive real-time information about the candidate properties and visually confirm the price trends, surrounding environment, future growth predictions, etc. of the properties.

[0288] Furthermore, as an example of a prompt message, if the user inputs, "I'm looking for a 3LDK property in Tokyo's 23 wards. My budget is up to 50 million yen, and I prioritize a family-friendly environment. Please tell me about market trends," the server will quickly analyze and recommend properties that meet the relevant conditions. In this way, the present invention becomes a powerful support tool for consumers to make more informed decisions.

[0289] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0290] Step 1:

[0291] The server collects geographic data from an external database across the country. The input data includes property locations, prices, surrounding environment information, and past transaction history. The collected data is normalized and converted into a unified format to ensure data consistency and readability. The output of this step is the normalized geographic data.

[0292] Step 2:

[0293] The server predicts market trends using normalized geographical data. It uses historical transaction history and geographical data as input and applies machine learning algorithms. Specifically, it uses Python and frameworks such as TensorFlow to build predictive models. The output obtained here represents future market trends, such as probability distributions and price trend graphs.

[0294] Step 3:

[0295] The user enters their desired criteria through the terminal interface. This information includes the desired area, budget, and property type. Based on this, the server interactively analyzes the criteria and performs filtering. The output is a list of properties that match the filtered criteria.

[0296] Step 4:

[0297] The server sends the filtered property list to the glasses-type display device. By using AWS cloud services to process the data in real time, the information is instantly displayed on the glasses-type display device worn by the user. The input is the filtered property list, and the output is real estate options presented to the user visually.

[0298] Step 5:

[0299] Users use glasses-type display devices to review and compare the information they receive. The device displays property attributes, price trends, and area characteristics, allowing users to view detailed information directly. The output serves to support user decision-making.

[0300] Step 6:

[0301] The server generates detailed market analysis reports for properties that users are interested in. These reports include predicted market trends, property return on investment, and evaluations of surrounding commercial facilities. These reports serve as a reference for users to provide feedback and assist in decision-making. Outputs include detailed PDF reports and interactive data visualizations.

[0302] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0303] The real estate purchase and investment support system according to the present invention, in addition to collecting and analyzing real estate data from across the country, is equipped with an emotion engine that recognizes the user's emotions and reflects them in property recommendations and market trend forecasts. This system combines multiple components to provide personalized suggestions to the user.

[0304] First, the server collects data such as price, location information, transaction history, and surrounding environment information from the national real estate database. These data are normalized and consistently stored in the database. Next, the server uses machine learning algorithms to analyze the data and predict future market trends, making it easier to grasp the fluctuations in real estate prices and the flow of demand.

[0305] The user inputs desired conditions, such as area, budget, and property type, through the terminal. Additionally, the emotion engine monitors the user's interactions and analyzes emotion data from text inputs and reactions. The emotion data thus analyzed is then applied to subsequent property recommendations and report generation, resulting in personalized proposals that take into account the user's emotional state and preferences.

[0306] For example, when the user is feeling stressed, the server can sense this with the emotion engine and prioritize and recommend properties located in a peaceful environment, increasing the likelihood of finding a comfortable place to live for the user.

[0307] At the report generation stage, the server creates a detailed report for decision-making support, considering market trends, property information, and the user's emotions. This report includes price trends, the attractiveness of the area, long-term investment value, etc., enabling the user to make an optimal choice based on it.

[0308] In this way, by combining the emotion engine, this system provides comprehensive support for real estate purchase and investment, considering not only data and algorithms but also the emotional elements of the user.

[0309] The following explains the processing flow.

[0310] Step 1:

[0311] The server regularly collects real estate data from nationwide real estate databases and related online resources. The collected data includes property prices, location conditions, past transaction history, and information on local amenities.

[0312] Step 2:

[0313] The server cleanses and normalizes the collected data and stores it in the database. This process maintains data consistency by removing duplicate data, imputing missing values, and standardizing data formats.

[0314] Step 3:

[0315] The server uses pre-configured data to run machine learning models and predict market trends. It analyzes price fluctuations and demand trends in specific regions and stores the results in a database.

[0316] Step 4:

[0317] Users enter their desired conditions into the interface from their device. These conditions include preferred area, budget, property type, and priority equipment requirements.

[0318] Step 5:

[0319] The device uses an emotion engine to analyze the input text and understand the user's emotional state. For example, it identifies positive, negative, and neutral emotions from the input content and chosen actions.

[0320] Step 6:

[0321] The server comprehensively evaluates the user's preferences and emotional state, and selects the most suitable property from the database. In this process, the ranking of recommended properties is adjusted according to the emotional state, generating a personalized list.

[0322] Step 7:

[0323] The terminal visually presents the generated property list to the user. The list includes detailed property information, local characteristics, and predicted market trends, allowing the user to consider properties based on this information.

[0324] Step 8:

[0325] The server generates reports reflecting market trends and sentiment analysis results, and provides them to users via their terminals. These reports include the future investment value of selected properties and the latest market analysis results, serving as valuable information to support user decision-making.

[0326] (Example 2)

[0327] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0328] Traditional real estate purchase and investment support systems lack personalized recommendations that take into account the user's emotional needs, making it difficult to select properties that align with the user's desires and feelings. Furthermore, they sometimes lack sufficient methods for accurately predicting market trends and creating detailed reports.

[0329] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0330] In this invention, the server includes means for collecting and normalizing real estate information from across the country, means for predicting market trends based on the collected information, means for selecting and recommending properties based on the user's desired conditions and sentiment data, and means for generating a detailed report from the market forecast, property information, and user sentiment data. This enables real estate selection that meets the user's sentiments and desires, and allows for the provision of detailed information based on market forecasts.

[0331] "Nationwide real estate information" refers to all data related to real estate, including information such as price, location, transaction history, and surrounding environment.

[0332] "Normalization" refers to the process of making collected data consistent and arranging it in a way that facilitates management and analysis in a database.

[0333] "Means of predicting market trends" refers to algorithms and methods that use current and historical data to predict future market changes and trends.

[0334] "User's desired conditions" refers to the specific conditions and requests that users have when purchasing or investing in real estate, including area, budget, and property type.

[0335] "Emotional data" refers to data representing emotional states extracted from user text input and interactions, and is used for recommendation and report generation.

[0336] "Methods for selecting and recommending properties" refers to a system that takes into account the user's desired conditions and emotional data to select and present appropriate real estate.

[0337] "Means of generating detailed reports" refers to the process of organizing information to support investment decisions based on collected data, market forecasts, and user sentiment data, and providing it in a format that users can utilize.

[0338] This invention relates to an information system intended to support real estate purchase and investment. The system primarily consists of a server, terminals, and users, and provides users with personalized real estate information.

[0339] The server collects real estate information from across the country and normalizes it based on specific criteria. This includes data collection using databases and APIs. Furthermore, machine learning techniques are used to accurately predict market trends from the collected data. For this purpose, open-source machine learning libraries such as TensorFlow are utilized. For example, by analyzing historical data, future trends in real estate prices in specific regions can be predicted.

[0340] Users access the system via a terminal and input their desired conditions. Furthermore, emotional data is analyzed from the user's input data and interactions. The emotional analysis incorporates a mechanism that uses natural language processing technology to extract and analyze emotions from the user's input text.

[0341] The server selects and recommends properties suitable for the user based on their preferences and sentiment data. This is done by a pre-built recommendation engine. Specifically, if a user expresses a desire to "live in a quiet place surrounded by nature," the system will prioritize recommending properties with suitable environmental conditions based on that information.

[0342] The generated results are displayed on the terminal and also provided as a detailed report. This report includes analysis results based on real estate market trends, individual property information, and user sentiment, providing strong support for users' investment decisions.

[0343] For example, the following prompt statements can be used in a generative AI model:

[0344] "What are the characteristics of properties recommended by the real estate support system for users looking for properties in a quiet environment?"

[0345] With the above configuration, the present invention is capable of providing users with more detailed and personalized information than conventional real estate information provision systems, thereby supporting decision-making regarding real estate investment and purchase.

[0346] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0347] Step 1:

[0348] The server collects real estate information. It uses APIs to retrieve information such as price, location, transaction history, and surrounding environment from a nationwide real estate database. Input is from an external data source, and output is normalized data. Specifically, the server uses scripts such as Python to format the retrieved data into a consistent format.

[0349] Step 2:

[0350] The server uses normalized data as input to a machine learning model to predict market trends. This utilizes a machine learning algorithm powered by TensorFlow. The input is normalized data, and the output is a prediction of future market trends. Specifically, it analyzes historical data to predict future price fluctuations.

[0351] Step 3:

[0352] The user enters their desired conditions through a terminal. These conditions include area, budget, and property type. The output is sent to the server. Specifically, the user enters data into an input form via the user interface, and this data is sent to the server.

[0353] Step 4:

[0354] The server analyzes user input and emotional data. Emotional data is collected using natural language processing (NLP) techniques based on the input text data. The input is text data from the user, and the output is the analyzed emotional state. Specifically, NLP techniques are used to extract emotions such as positive and negative from the user's input.

[0355] Step 5:

[0356] The server selects and recommends properties based on the analysis results. It combines the user's desired conditions and sentiment data to select the most suitable properties and generate a list. The input is the user's conditions and sentiment data, and the output is a list of recommended properties. Specifically, it searches the property database based on the user's request and creates a list of selected properties.

[0357] Step 6:

[0358] The server generates a detailed report and sends it to the terminal. The report is created considering factors such as real estate market forecasts, property information best suited to the user, and sentiment data. Inputs include market forecasts, property information, and sentiment data, while output is the report. Specifically, the server formats the generated information in LaTeX or PDF format and prepares it for transmission to the user.

[0359] (Application Example 2)

[0360] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0361] This invention aims to solve the problem that conventional real estate information systems do not adequately consider the user's emotions, making it difficult to recommend the most suitable property for the user. Furthermore, it aims to support more user-friendly real estate purchase and investment decision-making by enabling dynamic property recommendations based on the user's emotional state.

[0362] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0363] In this invention, the server includes means for collecting and normalizing asset information from across the country, means for analyzing emotions from data acquired by an information processing device, and means for selecting and recommending assets based on the user's desired conditions and the results of the emotion analysis. This enables personalized asset recommendations that take into account the user's emotional state.

[0364] "Asset information" refers to attribute data such as price, location, transaction history, and surrounding environment related to properties such as real estate.

[0365] "Means for analyzing emotions" refers to a device or method that has the function of analyzing a user's emotional state from data and performing a process of quantifying emotions as numerical values ​​or categories.

[0366] "Desired conditions" refer to the requests and requirements specified by the user when selecting a particular property, including price range, geographical conditions, property type, etc.

[0367] "Means for generating reports" refers to a device or method that has the function of compiling and presenting information for users to make decisions based on market trend forecasts, asset information, and the emotional state of users.

[0368] A "computational model" is a mathematical method used to predict market changes based on data, and includes machine learning algorithms and statistical models.

[0369] The system for realizing this invention includes a server, a terminal, and a user interface as its main components. It collects asset information such as real estate and analyzes the user's emotions to recommend assets that meet individual needs.

[0370] The server retrieves nationwide asset information from a database and normalizes the data to maintain consistency. It uses Apache Kafka to process and provide real estate information in real time. Furthermore, the server uses computational models with machine learning algorithms to predict market trends. PostgreSQL is used for database management, efficiently organizing and storing the collected data.

[0371] The device receives desired conditions and emotional data from the user. The software used to analyze the user's emotions employs natural language processing techniques such as OpenAI's GPT model to extract and analyze emotions from the user's input information.

[0372] When a user searches for a property, the device sends a request to the server based on collected emotional data and desired criteria. The server then analyzes market trends and asset information to generate a report recommending the most suitable property for the user, which is then provided to the user via the device. Based on this report, the user can make a more emotionally and logically optimal decision.

[0373] For example, if a user is looking for a property in a waterfront area with the intention of relaxing on the weekend, the device sends conditions such as "quiet location close to the sea" and "a relaxing environment" to the server. The server receives this information and, considering market trends, property information, and sentiment analysis results, generates a list of optimal properties and presents it to the user.

[0374] An example of a prompt message would be, "Please suggest the optimal housing environment based on the user's current emotions," which would then be used to instruct the generative AI model.

[0375] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0376] Step 1:

[0377] The server retrieves property information from a database across the country. The input is a database of real estate properties, and the output is detailed property information. The data is normalized via Apache Kafka and stored in the database.

[0378] Step 2:

[0379] The terminal receives the user's desired conditions. The conditions entered by the user through the interface are treated as input, and the terminal prepares this information for transmission to the server. The output is data formatted as the user's desired information.

[0380] Step 3:

[0381] The device acquires text information from the user and uses a generative AI model for sentiment analysis. The input is the user's text information, and the output is data indicating the emotional state. Natural language is analyzed using OpenAI's GPT model, and emotions are quantified.

[0382] Step 4:

[0383] The server predicts market trends based on user sentiment data and emotional state data. The input is user sentiment data and sentiment data, and the output is market forecast information. A computational model is used, and machine learning algorithms are employed to simulate market fluctuations.

[0384] Step 5:

[0385] The server combines market trends and asset information to select suitable assets for the user and generate a recommendation list. The input is market forecast information and asset data, and the output is a list of recommended properties.

[0386] Step 6:

[0387] The terminal receives a list of recommendations from the server and displays it to the user. The input is the list of recommendations from the server, and the output is the property information displayed on the user's terminal. The user makes a decision based on this information.

[0388] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0389] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0390] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0391] [Third Embodiment]

[0392] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0393] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0394] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0395] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0396] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0397] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0398] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0399] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0400] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0401] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0402] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0403] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0404] The real estate purchase and investment support system according to the present invention provides users with market trend forecasts and optimal property suggestions through the collection, analysis, and recommendation of real estate data from across the country. This system consists of multiple components.

[0405] First, the server automatically collects data from a wide-ranging real estate database. This data includes information of interest to the user, such as property prices, location, surrounding environment, demographics, and past transaction information. Next, the server uses machine learning algorithms and statistical methods based on this data to predict market trends and stores the resulting predictions in the database.

[0406] Users can use the terminal interface to enter their desired property criteria, including preferred area, budget, and property type. Based on these criteria, the server searches a pre-stored database for the most suitable properties and recommends them to the user. The recommendations are displayed visually in a ranking format, and by clicking on "Details," users can view more detailed information about each property.

[0407] Furthermore, the server generates individual reports based on predicted market trends and property information, and provides these reports to users. These reports include detailed information such as price fluctuation graphs, property investment yields, and area growth potential, which users can use to make decisions about purchasing or investing in real estate.

[0408] For example, if a user is looking for a 3LDK apartment in a certain city, the system will quickly present information on properties that potentially match those criteria, and also provide analysis results regarding price trends and future growth potential in that area. This allows the user to make better decisions based on the information.

[0409] This system could also be useful for real estate agents, as it could streamline customer service and function as a sales support tool, enabling them to provide better service to customers.

[0410] The following describes the processing flow.

[0411] Step 1:

[0412] The server collects real estate-related data from nationwide real estate databases and APIs from public institutions. This data includes property prices, location information, transaction history, and information about the surrounding living environment.

[0413] Step 2:

[0414] The server normalizes the collected data, removing unnecessary data and imputing missing values ​​to ensure consistency and reliability. This ensures that the information in the database is managed in a state suitable for analysis.

[0415] Step 3:

[0416] The server uses pre-configured data and employs machine learning models and time-series analysis to predict future trends in the real estate market. This prediction generates information on areas where prices are expected to rise and types of properties that will see increased demand.

[0417] Step 4:

[0418] Users input their desired conditions, such as preferred area, price range, and property type, via their device. This allows the system to accurately understand the user's needs.

[0419] Step 5:

[0420] The server uses the user's input preferences to score and rank the most suitable property candidates from a pre-prepared database. This ensures that properties that match the user's preferences are recommended preferentially.

[0421] Step 6:

[0422] The server generates a detailed report for the user regarding market trends and recommended properties. This report includes estimated price fluctuation graphs, investment yields, and the attractiveness of the surrounding environment.

[0423] Step 7:

[0424] The terminal displays the generated report and property details to the user. The user can use this information to make decisions regarding real estate purchases and investments.

[0425] (Example 1)

[0426] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0427] In the real estate market, extracting useful information quickly and accurately from a vast amount of data, and selecting and proposing the most suitable properties for users, is a challenging task. Furthermore, there is a lack of information necessary to predict market changes and make informed real estate investment decisions. There is a need for a system that can effectively address these challenges.

[0428] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0429] In this invention, the server includes means for collecting and normalizing real estate information from across the country, means for predicting market changes based on the collected information, and means for selecting and recommending properties based on user requirements using an information processing algorithm. This enables rapid and accurate property selection based on a vast amount of real estate information, as well as the provision of information based on market change predictions.

[0430] "Nationwide real estate information" refers to detailed information about land and buildings across the entire country, including prices, locations, and transaction history, regardless of specific regions.

[0431] "Normalization" refers to the process of unifying the format of collected information and imputing missing values ​​or correcting inappropriate values ​​in order to maintain data consistency.

[0432] "Predicting market changes" refers to using statistical methods and data analysis models to forecast price trends and demand fluctuations in the real estate market in advance.

[0433] An "information processing algorithm" refers to a set of calculation procedures or processing methods based on input data, and is a means of processing data according to a specific purpose and deriving a result.

[0434] "User requirements" refer to the specific desires and conditions that the user has regarding the property they are looking for, including, for example, price, location, and the condition of the property.

[0435] "Selecting and recommending properties" refers to extracting properties that best match the user's requirements based on information in the database and recommending them to the user.

[0436] "Generating and presenting reports" refers to compiling the results of market and property data analysis into documents or digital formats and providing them to users in an easy-to-understand manner.

[0437] "Transmitting to the customer's device" refers to transferring generated information and reports to the user's electronic device via the internet or other means of communication.

[0438] The real estate purchase and investment support system of this invention is composed of three components: a server, a terminal, and a user.

[0439] First, the server collects real estate information from across the country. During this process, the server retrieves data from various sources via the internet and uses Python scraping libraries (e.g., BeautifulSoup or Selenium) to collect the information. The collected data is then organized through a normalization process and stored in an SQL or NoSQL database. This database contains real estate-related information such as property prices, location, and surrounding environment.

[0440] Next, the server uses data analysis models such as linear regression and random forests to predict market changes based on the stored data. In this process, data analysis software and libraries are utilized to obtain predictions of future price trends and demand, which are then stored in the database.

[0441] Users enter their desired property criteria through an application installed on their device. This application operates in web or mobile app format and collects detailed user preferences such as area, budget, and property type.

[0442] The terminal sends user input information to the server, which then selects and searches for the most suitable properties from its database. The recommended property list is then displayed on the user's screen in a ranked format, and more detailed information can be viewed by clicking on the details of each property. Generated market forecasts and property reports are also presented to the user through the terminal. These reports include property price fluctuation graphs and area growth potential.

[0443] As a concrete example, when a user enters their desired criteria, such as "I'm looking for a 3LDK apartment in that city," into the system, the server searches the database based on those criteria and displays the most suitable properties in ranking order. This allows the user to quickly access information and make purchasing decisions. An example of a prompt sentence to be entered into the generating AI model is, "Tell me the price trends for 3LDK apartments within my budget in a specific area."

[0444] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0445] Step 1:

[0446] The server collects real estate information from across Japan. Specifically, it obtains information from various data sources via the internet. Using a Python scraping library, it automatically collects data on property prices, locations, and surrounding environments. It uses URLs and API access information from each information source as input, and outputs real estate information in raw data format.

[0447] Step 2:

[0448] The server normalizes the collected real estate information. Because the collected data may contain inconsistencies and missing values, it cleans the data and standardizes its format. Specifically, it unifies price information in different units and imputes missing values. It uses raw real estate data as input and generates a clean, standardized database as output.

[0449] Step 3:

[0450] The server predicts market changes based on normalized data. It uses machine learning algorithms to forecast future price trends and demand fluctuations. This process utilizes trained predictive models and employs statistical methods. Normalized real estate data is used as input, and predictive data on market changes is generated as output.

[0451] Step 4:

[0452] The user enters their desired property criteria through an interface installed on their device. These criteria include area, budget, and property type, and are sent to the server. The server uses the user's desired criteria as input and outputs search results.

[0453] Step 5:

[0454] The server selects the most suitable property from the database based on the user's preferences. It executes queries against the database and extracts properties that match the criteria. Using the user's preferences and normalized data as input, it obtains a list of recommended property information as output.

[0455] Step 6:

[0456] The terminal displays a list of recommended properties received from the server to the user. These are displayed on the screen in a ranking format, and links to access detailed information for each property are provided. The input is the recommended property information from the server, and the output is a visual display of the properties on the user's terminal.

[0457] (Application Example 1)

[0458] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0459] When purchasing or investing in real estate, consumers face the challenge of difficulty in quickly and effectively obtaining the information they need to make informed decisions. Furthermore, the information available to consumers during consultations at real estate agencies is limited, highlighting the need for more impactful visual support.

[0460] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0461] In this invention, the server includes means for collecting and normalizing geographic data from across the country, means for predicting market trends based on the collected information, and means for providing information visually in real time using glasses-type display devices. This enables consumers to obtain visual information in real time at physical stores and make informed decisions.

[0462] "Geographic data" refers to a collection of data that includes information about the location of a property.

[0463] "Normalization" refers to the process of converting data expressed in different formats or units into a unified format.

[0464] "Market trend forecasting" is the process of analyzing past data and current conditions to predict future changes in the real estate market.

[0465] "User's desired conditions" refers to the conditions and features that users particularly value when purchasing or investing in real estate.

[0466] "Selection and recommendation" means choosing and recommending properties and information that best suit the user's desired conditions.

[0467] A "report" refers to a detailed informational document generated based on market forecasts and property information.

[0468] A "glasses-type display device" refers to a glasses-like device that can visually display digital information.

[0469] The system for implementing the present invention is composed of a combination of various hardware and software. The main components include a server for processing information, a glasses-type display device for the user to view the information, and a cloud computing environment to support data processing.

[0470] The server first collects geographical data from across the country and normalizes it. This process uses programming languages ​​such as Python to convert data provided in different formats into a unified format. Subsequently, machine learning algorithms are used to predict market trends. Frameworks such as TensorFlow and Scikit-learn are used to analyze trends in large amounts of historical data and predict future market trends with high accuracy.

[0471] When a user enters their criteria of interest, the server uses this information to select the most suitable properties and facilities from collected data and transmits the information to a glasses-type display device in real time. AWS (Amazon Web Services) cloud infrastructure is used for high-speed data processing and communication. The glasses-type display device is a device similar to smart glasses; users wear this device in physical stores to visually acquire information and receive support in their decision-making process.

[0472] As a concrete example, consider a family visiting a real estate agency to purchase a home. This family can receive real-time information about potential properties through smart glasses, visually checking price trends, surrounding environment, and future growth forecasts for each property.

[0473] Furthermore, as an example of a prompt message, if the user inputs, "I'm looking for a 3LDK property in Tokyo's 23 wards. My budget is up to 50 million yen, and I prioritize a family-friendly environment. Please tell me about market trends," the server will quickly analyze and recommend properties that meet the relevant conditions. In this way, the present invention becomes a powerful support tool for consumers to make more informed decisions.

[0474] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0475] Step 1:

[0476] The server collects geographic data from an external database across the country. The input data includes property locations, prices, surrounding environment information, and past transaction history. The collected data is normalized and converted into a unified format to ensure data consistency and readability. The output of this step is the normalized geographic data.

[0477] Step 2:

[0478] The server predicts market trends using normalized geographical data. It uses historical transaction history and geographical data as input and applies machine learning algorithms. Specifically, it uses Python and frameworks such as TensorFlow to build predictive models. The output obtained here represents future market trends, such as probability distributions and price trend graphs.

[0479] Step 3:

[0480] The user enters their desired criteria through the terminal interface. This information includes the desired area, budget, and property type. Based on this, the server interactively analyzes the criteria and performs filtering. The output is a list of properties that match the filtered criteria.

[0481] Step 4:

[0482] The server sends the filtered property list to the glasses-type display device. By using AWS cloud services to process the data in real time, the information is instantly displayed on the glasses-type display device worn by the user. The input is the filtered property list, and the output is real estate options presented to the user visually.

[0483] Step 5:

[0484] Users use glasses-type display devices to review and compare the information they receive. The device displays property attributes, price trends, and area characteristics, allowing users to view detailed information directly. The output serves to support user decision-making.

[0485] Step 6:

[0486] The server generates detailed market analysis reports for properties that users are interested in. These reports include predicted market trends, property return on investment, and evaluations of surrounding commercial facilities. These reports serve as a reference for users to provide feedback and assist in decision-making. Outputs include detailed PDF reports and interactive data visualizations.

[0487] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0488] The real estate purchase and investment support system according to the present invention, in addition to collecting and analyzing real estate data from across the country, is equipped with an emotion engine that recognizes the user's emotions and reflects them in property recommendations and market trend forecasts. This system combines multiple components to provide personalized suggestions to the user.

[0489] First, the server collects data such as price, location information, transaction history, and surrounding environment information from a nationwide real estate database. This data is normalized and consistently maintained in the database. Next, the server analyzes the data using machine learning algorithms to predict future market trends. This makes it easier to understand fluctuations in real estate prices and demand trends.

[0490] Users input their desired criteria, such as area, budget, and property type, through their device. Furthermore, an emotion engine monitors user interactions and analyzes emotional data from text input and responses. This analyzed emotional data is then used for subsequent property recommendations and report generation, creating personalized suggestions that take into account the user's emotional state and preferences.

[0491] For example, if a user is feeling stressed, the server can detect this using an emotion engine and prioritize selecting and recommending properties located in calmer environments. This increases the likelihood that the user will find a comfortable place to live.

[0492] During the report generation phase, the server considers market trends, property information, and user sentiment to create a detailed report to support decision-making. This report includes price trends, regional attractiveness, and long-term investment value, allowing users to make the best choices based on it.

[0493] Thus, by combining this system with an emotion engine, it provides comprehensive support for real estate purchases and investments that takes into account not only data and algorithms, but also the emotional aspects of the user.

[0494] The following describes the processing flow.

[0495] Step 1:

[0496] The server regularly collects real estate data from nationwide real estate databases and related online resources. The collected data includes property prices, location conditions, past transaction history, and information on local amenities.

[0497] Step 2:

[0498] The server cleanses and normalizes the collected data and stores it in the database. This process maintains data consistency by removing duplicate data, imputing missing values, and standardizing data formats.

[0499] Step 3:

[0500] The server uses pre-configured data to run machine learning models and predict market trends. It analyzes price fluctuations and demand trends in specific regions and stores the results in a database.

[0501] Step 4:

[0502] Users enter their desired conditions into the interface from their device. These conditions include preferred area, budget, property type, and priority equipment requirements.

[0503] Step 5:

[0504] The device uses an emotion engine to analyze the input text and understand the user's emotional state. For example, it identifies positive, negative, and neutral emotions from the input content and chosen actions.

[0505] Step 6:

[0506] The server comprehensively evaluates the user's preferences and emotional state, and selects the most suitable property from the database. In this process, the ranking of recommended properties is adjusted according to the emotional state, generating a personalized list.

[0507] Step 7:

[0508] The terminal visually presents the generated property list to the user. The list includes detailed property information, local characteristics, and predicted market trends, allowing the user to consider properties based on this information.

[0509] Step 8:

[0510] The server generates reports reflecting market trends and sentiment analysis results, and provides them to users via their terminals. These reports include the future investment value of selected properties and the latest market analysis results, serving as valuable information to support user decision-making.

[0511] (Example 2)

[0512] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0513] Traditional real estate purchase and investment support systems lack personalized recommendations that take into account the user's emotional needs, making it difficult to select properties that align with the user's desires and feelings. Furthermore, they sometimes lack sufficient methods for accurately predicting market trends and creating detailed reports.

[0514] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0515] In this invention, the server includes means for collecting and normalizing real estate information from across the country, means for predicting market trends based on the collected information, means for selecting and recommending properties based on the user's desired conditions and sentiment data, and means for generating a detailed report from the market forecast, property information, and user sentiment data. This enables real estate selection that meets the user's sentiments and desires, and allows for the provision of detailed information based on market forecasts.

[0516] "Nationwide real estate information" refers to all data related to real estate, including information such as price, location, transaction history, and surrounding environment.

[0517] "Normalization" refers to the process of making collected data consistent and arranging it in a way that facilitates management and analysis in a database.

[0518] "Means of predicting market trends" refers to algorithms and methods that use current and historical data to predict future market changes and trends.

[0519] "User's desired conditions" refers to the specific conditions and requests that users have when purchasing or investing in real estate, including area, budget, and property type.

[0520] "Emotional data" refers to data representing emotional states extracted from user text input and interactions, and is used for recommendation and report generation.

[0521] "Methods for selecting and recommending properties" refers to a system that takes into account the user's desired conditions and emotional data to select and present appropriate real estate.

[0522] "Means of generating detailed reports" refers to the process of organizing information to support investment decisions based on collected data, market forecasts, and user sentiment data, and providing it in a format that users can utilize.

[0523] This invention relates to an information system intended to support real estate purchase and investment. The system primarily consists of a server, terminals, and users, and provides users with personalized real estate information.

[0524] The server collects real estate information from across the country and normalizes it based on specific criteria. This includes data collection using databases and APIs. Furthermore, machine learning techniques are used to accurately predict market trends from the collected data. For this purpose, open-source machine learning libraries such as TensorFlow are utilized. For example, by analyzing historical data, future trends in real estate prices in specific regions can be predicted.

[0525] Users access the system via a terminal and input their desired conditions. Furthermore, emotional data is analyzed from the user's input data and interactions. The emotional analysis incorporates a mechanism that uses natural language processing technology to extract and analyze emotions from the user's input text.

[0526] The server selects and recommends properties suitable for the user based on their preferences and sentiment data. This is done by a pre-built recommendation engine. Specifically, if a user expresses a desire to "live in a quiet place surrounded by nature," the system will prioritize recommending properties with suitable environmental conditions based on that information.

[0527] The generated results are displayed on the terminal and also provided as a detailed report. This report includes analysis results based on real estate market trends, individual property information, and user sentiment, providing strong support for users' investment decisions.

[0528] For example, the following prompt statements can be used in a generative AI model:

[0529] "What are the characteristics of properties recommended by the real estate support system for users looking for properties in a quiet environment?"

[0530] With the above configuration, the present invention is capable of providing users with more detailed and personalized information than conventional real estate information provision systems, thereby supporting decision-making regarding real estate investment and purchase.

[0531] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0532] Step 1:

[0533] The server collects real estate information. It uses APIs to retrieve information such as price, location, transaction history, and surrounding environment from a nationwide real estate database. Input is from an external data source, and output is normalized data. Specifically, the server uses scripts such as Python to format the retrieved data into a consistent format.

[0534] Step 2:

[0535] The server uses normalized data as input to a machine learning model to predict market trends. This utilizes a machine learning algorithm powered by TensorFlow. The input is normalized data, and the output is a prediction of future market trends. Specifically, it analyzes historical data to predict future price fluctuations.

[0536] Step 3:

[0537] The user enters their desired conditions through a terminal. These conditions include area, budget, and property type. The output is sent to the server. Specifically, the user enters data into an input form via the user interface, and this data is sent to the server.

[0538] Step 4:

[0539] The server analyzes user input and emotional data. Emotional data is collected using natural language processing (NLP) techniques based on the input text data. The input is text data from the user, and the output is the analyzed emotional state. Specifically, NLP techniques are used to extract emotions such as positive and negative from the user's input.

[0540] Step 5:

[0541] The server selects and recommends properties based on the analysis results. It combines the user's desired conditions and sentiment data to select the most suitable properties and generate a list. The input is the user's conditions and sentiment data, and the output is a list of recommended properties. Specifically, it searches the property database based on the user's request and creates a list of selected properties.

[0542] Step 6:

[0543] The server generates a detailed report and sends it to the terminal. The report is created considering factors such as real estate market forecasts, property information best suited to the user, and sentiment data. Inputs include market forecasts, property information, and sentiment data, while output is the report. Specifically, the server formats the generated information in LaTeX or PDF format and prepares it for transmission to the user.

[0544] (Application Example 2)

[0545] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0546] This invention aims to solve the problem that conventional real estate information systems do not adequately consider the user's emotions, making it difficult to recommend the most suitable property for the user. Furthermore, it aims to support more user-friendly real estate purchase and investment decision-making by enabling dynamic property recommendations based on the user's emotional state.

[0547] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0548] In this invention, the server includes means for collecting and normalizing asset information from across the country, means for analyzing emotions from data acquired by an information processing device, and means for selecting and recommending assets based on the user's desired conditions and the results of the emotion analysis. This enables personalized asset recommendations that take into account the user's emotional state.

[0549] "Asset information" refers to attribute data such as price, location, transaction history, and surrounding environment related to properties such as real estate.

[0550] "Means for analyzing emotions" refers to a device or method that has the function of analyzing a user's emotional state from data and performing a process of quantifying emotions as numerical values ​​or categories.

[0551] "Desired conditions" refer to the requests and requirements specified by the user when selecting a particular property, including price range, geographical conditions, property type, etc.

[0552] "Means for generating reports" refers to a device or method that has the function of compiling and presenting information for users to make decisions based on market trend forecasts, asset information, and the emotional state of users.

[0553] A "computational model" is a mathematical method used to predict market changes based on data, and includes machine learning algorithms and statistical models.

[0554] The system for realizing this invention includes a server, a terminal, and a user interface as its main components. It collects asset information such as real estate and analyzes the user's emotions to recommend assets that meet individual needs.

[0555] The server retrieves nationwide asset information from a database and normalizes the data to maintain consistency. It uses Apache Kafka to process and provide real estate information in real time. Furthermore, the server uses computational models with machine learning algorithms to predict market trends. PostgreSQL is used for database management, efficiently organizing and storing the collected data.

[0556] The device receives desired conditions and emotional data from the user. The software used to analyze the user's emotions employs natural language processing techniques such as OpenAI's GPT model to extract and analyze emotions from the user's input information.

[0557] When a user searches for a property, the device sends a request to the server based on collected emotional data and desired criteria. The server then analyzes market trends and asset information to generate a report recommending the most suitable property for the user, which is then provided to the user via the device. Based on this report, the user can make a more emotionally and logically optimal decision.

[0558] For example, if a user is looking for a property in a waterfront area with the intention of relaxing on the weekend, the device sends conditions such as "quiet location close to the sea" and "a relaxing environment" to the server. The server receives this information and, considering market trends, property information, and sentiment analysis results, generates a list of optimal properties and presents it to the user.

[0559] An example of a prompt message would be, "Please suggest the optimal housing environment based on the user's current emotions," which would then be used to instruct the generative AI model.

[0560] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0561] Step 1:

[0562] The server retrieves property information from a database across the country. The input is a database of real estate properties, and the output is detailed property information. The data is normalized via Apache Kafka and stored in the database.

[0563] Step 2:

[0564] The terminal receives the user's desired conditions. The conditions entered by the user through the interface are treated as input, and the terminal prepares this information for transmission to the server. The output is data formatted as the user's desired information.

[0565] Step 3:

[0566] The device acquires text information from the user and uses a generative AI model for sentiment analysis. The input is the user's text information, and the output is data indicating the emotional state. Natural language is analyzed using OpenAI's GPT model, and emotions are quantified.

[0567] Step 4:

[0568] The server predicts market trends based on user sentiment data and emotional state data. The input is user sentiment data and sentiment data, and the output is market forecast information. A computational model is used, and machine learning algorithms are employed to simulate market fluctuations.

[0569] Step 5:

[0570] The server combines market trends and asset information to select suitable assets for the user and generate a recommendation list. The input is market forecast information and asset data, and the output is a list of recommended properties.

[0571] Step 6:

[0572] The terminal receives a list of recommendations from the server and displays it to the user. The input is the list of recommendations from the server, and the output is the property information displayed on the user's terminal. The user makes a decision based on this information.

[0573] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0574] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0575] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0576] [Fourth Embodiment]

[0577] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0578] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0579] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0580] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0581] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0582] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0583] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0584] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0585] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0586] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0587] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0588] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0589] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0590] The real estate purchase and investment support system according to the present invention provides users with market trend forecasts and optimal property suggestions through the collection, analysis, and recommendation of real estate data from across the country. This system consists of multiple components.

[0591] First, the server automatically collects data from a wide-ranging real estate database. This data includes information of interest to the user, such as property prices, location, surrounding environment, demographics, and past transaction information. Next, the server uses machine learning algorithms and statistical methods based on this data to predict market trends and stores the resulting predictions in the database.

[0592] Users can use the terminal interface to enter their desired property criteria, including preferred area, budget, and property type. Based on these criteria, the server searches a pre-stored database for the most suitable properties and recommends them to the user. The recommendations are displayed visually in a ranking format, and by clicking on "Details," users can view more detailed information about each property.

[0593] Furthermore, the server generates individual reports based on predicted market trends and property information, and provides these reports to users. These reports include detailed information such as price fluctuation graphs, property investment yields, and area growth potential, which users can use to make decisions about purchasing or investing in real estate.

[0594] For example, if a user is looking for a 3LDK apartment in a certain city, the system will quickly present information on properties that potentially match those criteria, and also provide analysis results regarding price trends and future growth potential in that area. This allows the user to make better decisions based on the information.

[0595] This system could also be useful for real estate agents, as it could streamline customer service and function as a sales support tool, enabling them to provide better service to customers.

[0596] The following describes the processing flow.

[0597] Step 1:

[0598] The server collects real estate-related data from nationwide real estate databases and APIs from public institutions. This data includes property prices, location information, transaction history, and information about the surrounding living environment.

[0599] Step 2:

[0600] The server normalizes the collected data, removing unnecessary data and imputing missing values ​​to ensure consistency and reliability. This ensures that the information in the database is managed in a state suitable for analysis.

[0601] Step 3:

[0602] The server uses pre-configured data and employs machine learning models and time-series analysis to predict future trends in the real estate market. This prediction generates information on areas where prices are expected to rise and types of properties that will see increased demand.

[0603] Step 4:

[0604] Users input their desired conditions, such as preferred area, price range, and property type, via their device. This allows the system to accurately understand the user's needs.

[0605] Step 5:

[0606] The server uses the user's input preferences to score and rank the most suitable property candidates from a pre-prepared database. This ensures that properties that match the user's preferences are recommended preferentially.

[0607] Step 6:

[0608] The server generates a detailed report for the user regarding market trends and recommended properties. This report includes estimated price fluctuation graphs, investment yields, and the attractiveness of the surrounding environment.

[0609] Step 7:

[0610] The terminal displays the generated report and property details to the user. The user can use this information to make decisions regarding real estate purchases and investments.

[0611] (Example 1)

[0612] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0613] In the real estate market, extracting useful information quickly and accurately from a vast amount of data, and selecting and proposing the most suitable properties for users, is a challenging task. Furthermore, there is a lack of information necessary to predict market changes and make informed real estate investment decisions. There is a need for a system that can effectively address these challenges.

[0614] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0615] In this invention, the server includes means for collecting and normalizing real estate information from across the country, means for predicting market changes based on the collected information, and means for selecting and recommending properties based on user requirements using an information processing algorithm. This enables rapid and accurate property selection based on a vast amount of real estate information, as well as the provision of information based on market change predictions.

[0616] "Nationwide real estate information" refers to detailed information about land and buildings across the entire country, including prices, locations, and transaction history, regardless of specific regions.

[0617] "Normalization" refers to the process of unifying the format of collected information and imputing missing values ​​or correcting inappropriate values ​​in order to maintain data consistency.

[0618] "Predicting market changes" refers to using statistical methods and data analysis models to forecast price trends and demand fluctuations in the real estate market in advance.

[0619] An "information processing algorithm" refers to a set of calculation procedures or processing methods based on input data, and is a means of processing data according to a specific purpose and deriving a result.

[0620] "User requirements" refer to the specific desires and conditions that the user has regarding the property they are looking for, including, for example, price, location, and the condition of the property.

[0621] "Selecting and recommending properties" refers to extracting properties that best match the user's requirements based on information in the database and recommending them to the user.

[0622] "Generating and presenting reports" refers to compiling the results of market and property data analysis into documents or digital formats and providing them to users in an easy-to-understand manner.

[0623] "Transmitting to the customer's device" refers to transferring generated information and reports to the user's electronic device via the internet or other means of communication.

[0624] The real estate purchase and investment support system of this invention is composed of three components: a server, a terminal, and a user.

[0625] First, the server collects real estate information from across the country. During this process, the server retrieves data from various sources via the internet and uses Python scraping libraries (e.g., BeautifulSoup or Selenium) to collect the information. The collected data is then organized through a normalization process and stored in an SQL or NoSQL database. This database contains real estate-related information such as property prices, location, and surrounding environment.

[0626] Next, the server uses data analysis models such as linear regression and random forests to predict market changes based on the stored data. In this process, data analysis software and libraries are utilized to obtain predictions of future price trends and demand, which are then stored in the database.

[0627] Users enter their desired property criteria through an application installed on their device. This application operates in web or mobile app format and collects detailed user preferences such as area, budget, and property type.

[0628] The terminal sends user input information to the server, which then selects and searches for the most suitable properties from its database. The recommended property list is then displayed on the user's screen in a ranked format, and more detailed information can be viewed by clicking on the details of each property. Generated market forecasts and property reports are also presented to the user through the terminal. These reports include property price fluctuation graphs and area growth potential.

[0629] As a concrete example, when a user enters their desired criteria, such as "I'm looking for a 3LDK apartment in that city," into the system, the server searches the database based on those criteria and displays the most suitable properties in ranking order. This allows the user to quickly access information and make purchasing decisions. An example of a prompt sentence to be entered into the generating AI model is, "Tell me the price trends for 3LDK apartments within my budget in a specific area."

[0630] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0631] Step 1:

[0632] The server collects real estate information from across Japan. Specifically, it obtains information from various data sources via the internet. Using a Python scraping library, it automatically collects data on property prices, locations, and surrounding environments. It uses URLs and API access information from each information source as input, and outputs real estate information in raw data format.

[0633] Step 2:

[0634] The server normalizes the collected real estate information. Because the collected data may contain inconsistencies and missing values, it cleans the data and standardizes its format. Specifically, it unifies price information in different units and imputes missing values. It uses raw real estate data as input and generates a clean, standardized database as output.

[0635] Step 3:

[0636] The server predicts market changes based on normalized data. It uses machine learning algorithms to forecast future price trends and demand fluctuations. This process utilizes trained predictive models and employs statistical methods. Normalized real estate data is used as input, and predictive data on market changes is generated as output.

[0637] Step 4:

[0638] The user enters their desired property criteria through an interface installed on their device. These criteria include area, budget, and property type, and are sent to the server. The server uses the user's desired criteria as input and outputs search results.

[0639] Step 5:

[0640] The server selects the most suitable property from the database based on the user's preferences. It executes queries against the database and extracts properties that match the criteria. Using the user's preferences and normalized data as input, it obtains a list of recommended property information as output.

[0641] Step 6:

[0642] The terminal displays a list of recommended properties received from the server to the user. These are displayed on the screen in a ranking format, and links to access detailed information for each property are provided. The input is the recommended property information from the server, and the output is a visual display of the properties on the user's terminal.

[0643] (Application Example 1)

[0644] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0645] When purchasing or investing in real estate, consumers face the challenge of difficulty in quickly and effectively obtaining the information they need to make informed decisions. Furthermore, the information available to consumers during consultations at real estate agencies is limited, highlighting the need for more impactful visual support.

[0646] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0647] In this invention, the server includes means for collecting and normalizing geographic data from across the country, means for predicting market trends based on the collected information, and means for providing information visually in real time using glasses-type display devices. This enables consumers to obtain visual information in real time at physical stores and make informed decisions.

[0648] "Geographic data" refers to a collection of data that includes information about the location of a property.

[0649] "Normalization" refers to the process of converting data expressed in different formats or units into a unified format.

[0650] "Market trend forecasting" is the process of analyzing past data and current conditions to predict future changes in the real estate market.

[0651] "User's desired conditions" refers to the conditions and features that users particularly value when purchasing or investing in real estate.

[0652] "Selection and recommendation" means choosing and recommending properties and information that best suit the user's desired conditions.

[0653] A "report" refers to a detailed informational document generated based on market forecasts and property information.

[0654] A "glasses-type display device" refers to a glasses-like device that can visually display digital information.

[0655] The system for implementing the present invention is composed of a combination of various hardware and software. The main components include a server for processing information, a glasses-type display device for the user to view the information, and a cloud computing environment to support data processing.

[0656] The server first collects geographical data from across the country and normalizes it. This process uses programming languages ​​such as Python to convert data provided in different formats into a unified format. Subsequently, machine learning algorithms are used to predict market trends. Frameworks such as TensorFlow and Scikit-learn are used to analyze trends in large amounts of historical data and predict future market trends with high accuracy.

[0657] When a user enters their criteria of interest, the server uses this information to select the most suitable properties and facilities from collected data and transmits the information to a glasses-type display device in real time. AWS (Amazon Web Services) cloud infrastructure is used for high-speed data processing and communication. The glasses-type display device is a device similar to smart glasses; users wear this device in physical stores to visually acquire information and receive support in their decision-making process.

[0658] As a concrete example, consider a family visiting a real estate agency to purchase a home. This family can receive real-time information about potential properties through smart glasses, visually checking price trends, surrounding environment, and future growth forecasts for each property.

[0659] Furthermore, as an example of a prompt message, if the user inputs, "I'm looking for a 3LDK property in Tokyo's 23 wards. My budget is up to 50 million yen, and I prioritize a family-friendly environment. Please tell me about market trends," the server will quickly analyze and recommend properties that meet the relevant conditions. In this way, the present invention becomes a powerful support tool for consumers to make more informed decisions.

[0660] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0661] Step 1:

[0662] The server collects geographic data from an external database across the country. The input data includes property locations, prices, surrounding environment information, and past transaction history. The collected data is normalized and converted into a unified format to ensure data consistency and readability. The output of this step is the normalized geographic data.

[0663] Step 2:

[0664] The server predicts market trends using normalized geographical data. It uses historical transaction history and geographical data as input and applies machine learning algorithms. Specifically, it uses Python and frameworks such as TensorFlow to build predictive models. The output obtained here represents future market trends, such as probability distributions and price trend graphs.

[0665] Step 3:

[0666] The user enters their desired criteria through the terminal interface. This information includes the desired area, budget, and property type. Based on this, the server interactively analyzes the criteria and performs filtering. The output is a list of properties that match the filtered criteria.

[0667] Step 4:

[0668] The server sends the filtered property list to the glasses-type display device. By using AWS cloud services to process the data in real time, the information is instantly displayed on the glasses-type display device worn by the user. The input is the filtered property list, and the output is real estate options presented to the user visually.

[0669] Step 5:

[0670] Users use glasses-type display devices to review and compare the information they receive. The device displays property attributes, price trends, and area characteristics, allowing users to view detailed information directly. The output serves to support user decision-making.

[0671] Step 6:

[0672] The server generates detailed market analysis reports for properties that users are interested in. These reports include predicted market trends, property return on investment, and evaluations of surrounding commercial facilities. These reports serve as a reference for users to provide feedback and assist in decision-making. Outputs include detailed PDF reports and interactive data visualizations.

[0673] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0674] The real estate purchase and investment support system according to the present invention, in addition to collecting and analyzing real estate data from across the country, is equipped with an emotion engine that recognizes the user's emotions and reflects them in property recommendations and market trend forecasts. This system combines multiple components to provide personalized suggestions to the user.

[0675] First, the server collects data such as price, location information, transaction history, and surrounding environment information from a nationwide real estate database. This data is normalized and consistently maintained in the database. Next, the server analyzes the data using machine learning algorithms to predict future market trends. This makes it easier to understand fluctuations in real estate prices and demand trends.

[0676] Users input their desired criteria, such as area, budget, and property type, through their device. Furthermore, an emotion engine monitors user interactions and analyzes emotional data from text input and responses. This analyzed emotional data is then used for subsequent property recommendations and report generation, creating personalized suggestions that take into account the user's emotional state and preferences.

[0677] For example, if a user is feeling stressed, the server can detect this using an emotion engine and prioritize selecting and recommending properties located in calmer environments. This increases the likelihood that the user will find a comfortable place to live.

[0678] During the report generation phase, the server considers market trends, property information, and user sentiment to create a detailed report to support decision-making. This report includes price trends, regional attractiveness, and long-term investment value, allowing users to make the best choices based on it.

[0679] Thus, by combining this system with an emotion engine, it provides comprehensive support for real estate purchases and investments that takes into account not only data and algorithms, but also the emotional aspects of the user.

[0680] The following describes the processing flow.

[0681] Step 1:

[0682] The server regularly collects real estate data from nationwide real estate databases and related online resources. The collected data includes property prices, location conditions, past transaction history, and information on local amenities.

[0683] Step 2:

[0684] The server cleanses and normalizes the collected data and stores it in the database. This process maintains data consistency by removing duplicate data, imputing missing values, and standardizing data formats.

[0685] Step 3:

[0686] The server uses pre-configured data to run machine learning models and predict market trends. It analyzes price fluctuations and demand trends in specific regions and stores the results in a database.

[0687] Step 4:

[0688] Users enter their desired conditions into the interface from their device. These conditions include preferred area, budget, property type, and priority equipment requirements.

[0689] Step 5:

[0690] The device uses an emotion engine to analyze the input text and understand the user's emotional state. For example, it identifies positive, negative, and neutral emotions from the input content and chosen actions.

[0691] Step 6:

[0692] The server comprehensively evaluates the user's preferences and emotional state, and selects the most suitable property from the database. In this process, the ranking of recommended properties is adjusted according to the emotional state, generating a personalized list.

[0693] Step 7:

[0694] The terminal visually presents the generated property list to the user. The list includes detailed property information, local characteristics, and predicted market trends, allowing the user to consider properties based on this information.

[0695] Step 8:

[0696] The server generates reports reflecting market trends and sentiment analysis results, and provides them to users via their terminals. These reports include the future investment value of selected properties and the latest market analysis results, serving as valuable information to support user decision-making.

[0697] (Example 2)

[0698] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0699] Traditional real estate purchase and investment support systems lack personalized recommendations that take into account the user's emotional needs, making it difficult to select properties that align with the user's desires and feelings. Furthermore, they sometimes lack sufficient methods for accurately predicting market trends and creating detailed reports.

[0700] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0701] In this invention, the server includes means for collecting and normalizing real estate information from across the country, means for predicting market trends based on the collected information, means for selecting and recommending properties based on the user's desired conditions and sentiment data, and means for generating a detailed report from the market forecast, property information, and user sentiment data. This enables real estate selection that meets the user's sentiments and desires, and allows for the provision of detailed information based on market forecasts.

[0702] "Nationwide real estate information" refers to all data related to real estate, including information such as price, location, transaction history, and surrounding environment.

[0703] "Normalization" refers to the process of making collected data consistent and arranging it in a way that facilitates management and analysis in a database.

[0704] "Means of predicting market trends" refers to algorithms and methods that use current and historical data to predict future market changes and trends.

[0705] "User's desired conditions" refers to the specific conditions and requests that users have when purchasing or investing in real estate, including area, budget, and property type.

[0706] "Emotional data" refers to data representing emotional states extracted from user text input and interactions, and is used for recommendation and report generation.

[0707] "Methods for selecting and recommending properties" refers to a system that takes into account the user's desired conditions and emotional data to select and present appropriate real estate.

[0708] "Means of generating detailed reports" refers to the process of organizing information to support investment decisions based on collected data, market forecasts, and user sentiment data, and providing it in a format that users can utilize.

[0709] This invention relates to an information system intended to support real estate purchase and investment. The system primarily consists of a server, terminals, and users, and provides users with personalized real estate information.

[0710] The server collects real estate information from across the country and normalizes it based on specific criteria. This includes data collection using databases and APIs. Furthermore, machine learning techniques are used to accurately predict market trends from the collected data. For this purpose, open-source machine learning libraries such as TensorFlow are utilized. For example, by analyzing historical data, future trends in real estate prices in specific regions can be predicted.

[0711] Users access the system via a terminal and input their desired conditions. Furthermore, emotional data is analyzed from the user's input data and interactions. The emotional analysis incorporates a mechanism that uses natural language processing technology to extract and analyze emotions from the user's input text.

[0712] The server selects and recommends properties suitable for the user based on their preferences and sentiment data. This is done by a pre-built recommendation engine. Specifically, if a user expresses a desire to "live in a quiet place surrounded by nature," the system will prioritize recommending properties with suitable environmental conditions based on that information.

[0713] The generated results are displayed on the terminal and also provided as a detailed report. This report includes analysis results based on real estate market trends, individual property information, and user sentiment, providing strong support for users' investment decisions.

[0714] For example, the following prompt statements can be used in a generative AI model:

[0715] "What are the characteristics of properties recommended by the real estate support system for users looking for properties in a quiet environment?"

[0716] With the above configuration, the present invention is capable of providing users with more detailed and personalized information than conventional real estate information provision systems, thereby supporting decision-making regarding real estate investment and purchase.

[0717] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0718] Step 1:

[0719] The server collects real estate information. It uses APIs to retrieve information such as price, location, transaction history, and surrounding environment from a nationwide real estate database. Input is from an external data source, and output is normalized data. Specifically, the server uses scripts such as Python to format the retrieved data into a consistent format.

[0720] Step 2:

[0721] The server uses normalized data as input to a machine learning model to predict market trends. This utilizes a machine learning algorithm powered by TensorFlow. The input is normalized data, and the output is a prediction of future market trends. Specifically, it analyzes historical data to predict future price fluctuations.

[0722] Step 3:

[0723] The user enters their desired conditions through a terminal. These conditions include area, budget, and property type. The output is sent to the server. Specifically, the user enters data into an input form via the user interface, and this data is sent to the server.

[0724] Step 4:

[0725] The server analyzes user input and emotional data. Emotional data is collected using natural language processing (NLP) techniques based on the input text data. The input is text data from the user, and the output is the analyzed emotional state. Specifically, NLP techniques are used to extract emotions such as positive and negative from the user's input.

[0726] Step 5:

[0727] The server selects and recommends properties based on the analysis results. It combines the user's desired conditions and sentiment data to select the most suitable properties and generate a list. The input is the user's conditions and sentiment data, and the output is a list of recommended properties. Specifically, it searches the property database based on the user's request and creates a list of selected properties.

[0728] Step 6:

[0729] The server generates a detailed report and sends it to the terminal. The report is created considering factors such as real estate market forecasts, property information best suited to the user, and sentiment data. Inputs include market forecasts, property information, and sentiment data, while output is the report. Specifically, the server formats the generated information in LaTeX or PDF format and prepares it for transmission to the user.

[0730] (Application Example 2)

[0731] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0732] This invention aims to solve the problem that conventional real estate information systems do not adequately consider the user's emotions, making it difficult to recommend the most suitable property for the user. Furthermore, it aims to support more user-friendly real estate purchase and investment decision-making by enabling dynamic property recommendations based on the user's emotional state.

[0733] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0734] In this invention, the server includes means for collecting and normalizing asset information from across the country, means for analyzing emotions from data acquired by an information processing device, and means for selecting and recommending assets based on the user's desired conditions and the results of the emotion analysis. This enables personalized asset recommendations that take into account the user's emotional state.

[0735] "Asset information" refers to attribute data such as price, location, transaction history, and surrounding environment related to properties such as real estate.

[0736] "Means for analyzing emotions" refers to a device or method that has the function of analyzing a user's emotional state from data and performing a process of quantifying emotions as numerical values ​​or categories.

[0737] "Desired conditions" refer to the requests and requirements specified by the user when selecting a particular property, including price range, geographical conditions, property type, etc.

[0738] "Means for generating reports" refers to a device or method that has the function of compiling and presenting information for users to make decisions based on market trend forecasts, asset information, and the emotional state of users.

[0739] A "computational model" is a mathematical method used to predict market changes based on data, and includes machine learning algorithms and statistical models.

[0740] The system for realizing this invention includes a server, a terminal, and a user interface as its main components. It collects asset information such as real estate and analyzes the user's emotions to recommend assets that meet individual needs.

[0741] The server retrieves nationwide asset information from a database and normalizes the data to maintain consistency. It uses Apache Kafka to process and provide real estate information in real time. Furthermore, the server uses computational models with machine learning algorithms to predict market trends. PostgreSQL is used for database management, efficiently organizing and storing the collected data.

[0742] The device receives desired conditions and emotional data from the user. The software used to analyze the user's emotions employs natural language processing techniques such as OpenAI's GPT model to extract and analyze emotions from the user's input information.

[0743] When a user searches for a property, the device sends a request to the server based on collected emotional data and desired criteria. The server then analyzes market trends and asset information to generate a report recommending the most suitable property for the user, which is then provided to the user via the device. Based on this report, the user can make a more emotionally and logically optimal decision.

[0744] For example, if a user is looking for a property in a waterfront area with the intention of relaxing on the weekend, the device sends conditions such as "quiet location close to the sea" and "a relaxing environment" to the server. The server receives this information and, considering market trends, property information, and sentiment analysis results, generates a list of optimal properties and presents it to the user.

[0745] An example of a prompt message would be, "Please suggest the optimal housing environment based on the user's current emotions," which would then be used to instruct the generative AI model.

[0746] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0747] Step 1:

[0748] The server retrieves property information from a database across the country. The input is a database of real estate properties, and the output is detailed property information. The data is normalized via Apache Kafka and stored in the database.

[0749] Step 2:

[0750] The terminal receives the user's desired conditions. The conditions entered by the user through the interface are treated as input, and the terminal prepares this information for transmission to the server. The output is data formatted as the user's desired information.

[0751] Step 3:

[0752] The device acquires text information from the user and uses a generative AI model for sentiment analysis. The input is the user's text information, and the output is data indicating the emotional state. Natural language is analyzed using OpenAI's GPT model, and emotions are quantified.

[0753] Step 4:

[0754] The server predicts market trends based on user sentiment data and emotional state data. The input is user sentiment data and sentiment data, and the output is market forecast information. A computational model is used, and machine learning algorithms are employed to simulate market fluctuations.

[0755] Step 5:

[0756] The server combines market trends and asset information to select suitable assets for the user and generate a recommendation list. The input is market forecast information and asset data, and the output is a list of recommended properties.

[0757] Step 6:

[0758] The terminal receives a list of recommendations from the server and displays it to the user. The input is the list of recommendations from the server, and the output is the property information displayed on the user's terminal. The user makes a decision based on this information.

[0759] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0760] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0761] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0762] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0763] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0764] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0765] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0766] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0767] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0768] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0769] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0770] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0771] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0772] 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.

[0773] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0774] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0775] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0776] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0777] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0778] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0779] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0780] The following is further disclosed regarding the embodiments described above.

[0781] (Claim 1)

[0782] A method for collecting and normalizing real estate data from across the country,

[0783] A means of predicting market trends based on the aforementioned collected data,

[0784] A means of selecting and recommending properties based on the user's desired conditions,

[0785] A means for generating a report from the aforementioned market forecast and property information,

[0786] A system that includes this.

[0787] (Claim 2)

[0788] The system according to claim 1, comprising an interface for receiving user-inputted desired conditions.

[0789] (Claim 3)

[0790] The system according to claim 1, wherein the market trend forecasting means uses a machine learning algorithm.

[0791] "Example 1"

[0792] (Claim 1)

[0793] A means of collecting and normalizing real estate information from across the country,

[0794] A means of predicting market changes based on the information collected above,

[0795] A means of selecting and recommending properties based on user requirements using an information processing algorithm,

[0796] A means for generating and presenting a report based on the aforementioned market change forecast and property information,

[0797] A means of sending the generated report to the customer's terminal,

[0798] A system that includes this.

[0799] (Claim 2)

[0800] The system according to claim 1, comprising an information display device that accepts user inputs for request conditions.

[0801] (Claim 3)

[0802] The system according to claim 1, wherein the market change prediction means uses a data analysis model.

[0803] "Application Example 1"

[0804] (Claim 1)

[0805] Methods for collecting and standardizing geographic data from across the country,

[0806] A means of predicting market trends based on the information collected above,

[0807] A means of selecting and recommending facilities based on the user's desired conditions,

[0808] A means for generating a report from the aforementioned market forecast and facility information,

[0809] A means of providing information visually in real time using glasses-type display devices,

[0810] A system that includes this.

[0811] (Claim 2)

[0812] The system according to claim 1, comprising a dialogue means for receiving desired conditions entered by the user.

[0813] (Claim 3)

[0814] The system according to claim 1, wherein the market trend forecasting means uses a machine learning method.

[0815] "Example 2 of combining an emotion engine"

[0816] (Claim 1)

[0817] A means of collecting and normalizing real estate information from across the country,

[0818] A means of predicting market trends based on the information collected above,

[0819] A method for selecting and recommending properties based on the user's desired conditions and emotional data,

[0820] A means for generating a detailed report from the aforementioned market forecast, property information, and user sentiment data,

[0821] A system that includes this.

[0822] (Claim 2)

[0823] The system according to claim 1, which includes an interface for the user to input desired conditions and analyzes the emotional state.

[0824] (Claim 3)

[0825] The system according to claim 1, wherein the market trend forecasting means uses a machine learning method.

[0826] "Application example 2 when combining with an emotional engine"

[0827] (Claim 1)

[0828] A means of collecting and normalizing asset information from across the country,

[0829] A means of predicting market trends based on the information collected above,

[0830] A means of analyzing emotions from data acquired by an information processing device,

[0831] A means of selecting and recommending assets based on the user's desired conditions and sentiment analysis results,

[0832] A means for generating a report from the aforementioned market forecast and asset information,

[0833] A system that includes this.

[0834] (Claim 2)

[0835] The system according to claim 1, comprising an interface for receiving user requests via an information processing device.

[0836] (Claim 3)

[0837] The system according to claim 1, wherein the market trend forecasting means uses a computational model. [Explanation of symbols]

[0838] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A method for collecting and normalizing real estate data from across the country, A means of predicting market trends based on the aforementioned collected data, A means of selecting and recommending properties based on the user's desired conditions, A means for generating a report from the aforementioned market forecast and property information, A system that includes this.

2. The system according to claim 1, comprising an interface for receiving desired conditions entered by a user.

3. The system according to claim 1, wherein the market trend forecasting means uses a machine learning algorithm.

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