system

Abstract

We provide the system. [Solution] A terminal means for inputting user preferences and transmitting the information to a central processing unit, A means of obtaining asset information from a database based on the user's desired conditions, A means for aggregating surrounding environment information, natural disaster risk information, and reliability information of service providers from acquired asset information and calculating evaluation indicators, A means of ranking properties based on evaluation indicators and sending information to the user's terminal for suggestion, A means of providing information to support the selection of the most suitable property by obtaining local infrastructure information in real time from an external API based on conditions entered by the user, 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, the method 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] Searching for a property generally requires a lot of time and effort, and there is a problem that it is very difficult to simultaneously consider various aspects of information such as the surrounding environment, natural disaster risks, and the reliability of the providing company. In addition, it is not easy to comprehensively evaluate this information and find a dwelling that best suits the user's desired conditions in a short time.

Means for Solving the Problems

[0005] This invention provides a system that includes a terminal and server for acquiring real estate information from a database based on user-inputted preferences, and further integrates and evaluates the acquired information with surrounding environment information, natural disaster risk information, and reliability information of service providers. This makes it possible to calculate evaluation scores, rank properties, and propose the most suitable properties to the user. Specifically, the system considers surrounding environment information such as safety, transportation access, and commercial facilities, and natural disaster risks such as earthquake risk and flood risk, and adopts a mechanism to aggregate this information and reflect it in the evaluation.

[0006] "User preferences" refer to a set of requirements and conditions that users input to select their ideal home, and specifically include location, floor plan, budget, accessibility, etc.

[0007] A "terminal" is a device used by users to input information and exchange information with a server, and includes smartphones, computers, and other similar devices.

[0008] A "server" is a computer system that receives information from users, retrieves data from a real estate database, performs evaluation and analysis processing, and then sends the results to the terminal.

[0009] "Real estate information" refers to detailed data about a property, including its location, price, area, year built, and facilities.

[0010] "Surrounding environment information" refers to information about the area surrounding the property, and specifically includes things like local safety, the convenience of transportation access, and the presence or absence of commercial facilities.

[0011] "Natural disaster risk information" refers to data that assesses the likelihood and impact of natural disasters such as earthquakes and floods in a specific region.

[0012] "Service provider reliability information" refers to information used to evaluate the creditworthiness of construction companies and real estate companies that provide properties, and includes data such as customer reviews and third-party rating sites.

[0013] The "evaluation score" is a numerical representation of the overall suitability of a property, based on the input information and acquired data. It is an indicator of how well a property matches the user's desired conditions.

[0014] A "ranking" is a list that arranges properties in order of suitability based on their evaluation scores, and is intended to suggest the best option for the user. [Brief explanation of the drawing]

[0015] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

[0016] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0019] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0021] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0022] 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."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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".

[0036] One embodiment of the present invention is to provide an information processing system for efficiently finding properties that meet the user's desired housing conditions. This system collects, analyzes, and evaluates appropriate real estate information based on the user's desired conditions and proposes the optimal option.

[0037] First, the user uses their device to enter their desired housing conditions. Specifically, these conditions include the desired location, floor plan, budget, and walking distance from the nearest station. This information is received by the device and sent to the server.

[0038] The server retrieves property data that matches the user's criteria from the real estate database. The database filters properties based on criteria such as location, price range, and floor plan.

[0039] Next, the server investigates the surrounding environment information for the acquired property information. It obtains and analyzes information including safety information, transportation access, and the presence of commercial facilities from external APIs and public databases.

[0040] Furthermore, the server evaluates data on natural disaster risks (earthquake risk, flood risk, etc.). Based on geographical information, it refers to past disaster data and risk maps for the relevant area to perform a risk assessment for each property.

[0041] At the same time, to evaluate the reliability of the construction and real estate companies providing the properties, the server calculates a reliability score based on third-party review sites and user reviews.

[0042] Based on the aggregated information described above, the server calculates an overall evaluation score for each property and determines its suitability to the user's desired conditions. The evaluation scores are quantified and ranked to allow for easy comparison of each property.

[0043] The server then ranks the properties based on their evaluation scores and creates a list of the best properties. This information is sent to the terminal and displayed to the user. The user can then compare evaluation comments and detailed information from the presented properties and select a suitable place to live.

[0044] For example, if a user is looking for a 3LDK apartment in Tokyo priced under 50 million yen, the system will search for suitable properties and recommend those with good surrounding environments and low risk of natural disasters as top-ranked options. This process allows users to find their ideal home in a short amount of time.

[0045] The following describes the processing flow.

[0046] Step 1:

[0047] Users enter their desired housing requirements through a dedicated application or web interface. These requirements include preferred area, floor plan, budget, walking distance from the nearest station, and educational environment for children. The information entered by the user is collected as data by the terminal and sent to the server.

[0048] Step 2:

[0049] The server queries the real estate information database based on the user's requested conditions. It uses queries such as SQL to retrieve property information that matches the specified conditions. At this stage, basic property information (e.g., location, price, area, year built, etc.) is collected.

[0050] Step 3:

[0051] The server accesses external APIs and public databases to aggregate surrounding environment information for each acquired property. This information includes local safety conditions, nearest transportation options, and the presence of nearby commercial facilities and medical institutions. The server analyzes this data to evaluate whether each property has an optimal living environment.

[0052] Step 4:

[0053] The server assesses the natural disaster risk of each property based on its geographical location. It retrieves risk data for earthquakes, floods, tsunamis, etc., from a geographic database and applies it to the location of each property to perform a risk assessment. This information serves as a crucial evaluation criterion when providing information to users.

[0054] Step 5:

[0055] The server collects data from third-party review sites and user reviews to assess the reliability of construction and real estate companies offering properties. This data is then used to calculate a reliability score, which is incorporated into the scoring system as a key factor in property selection.

[0056] Step 6:

[0057] The server calculates an overall evaluation score for each property based on the collected information. Specifically, it integrates basic property information, assessment of the surrounding environment, natural disaster risk assessment, and reliability assessment to perform the scoring. This score quantifies how well the property matches the user's desired conditions.

[0058] Step 7:

[0059] The server ranks properties based on the calculated evaluation score and lists them in descending order of suitability. This ranked property list is then sent to the terminal and prepared for display to the user.

[0060] Step 8:

[0061] The terminal displays the received ranking information to the user. Based on the displayed information, the user can compare each property and view detailed information. The user can select the property that best suits their preferences, taking into account evaluation scores and various other information.

[0062] (Example 1)

[0063] 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."

[0064] In modern society, when buying or renting real estate, users need to select the optimal property from a vast amount of information. However, conventional methods place a heavy burden on users to collect and analyze information themselves, making it difficult to quickly find a property that meets their desired conditions. Furthermore, there is a lack of means to comprehensively judge the surrounding environment, disaster risk, and reliability of service providers of selected properties, which often leaves users feeling anxious. This invention aims to solve the above problems by providing a system that efficiently proposes properties that meet the user's desired conditions.

[0065] 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.

[0066] In this invention, the server includes means for retrieving information on real estate from an information storage device based on the user's desired conditions; means for integrating information on the surrounding environment, risk information regarding natural disasters, and credit information of real estate agents with the retrieved real estate information to generate an evaluation-based index; and means for ranking real estate based on the evaluation-based index and transmitting information to an information terminal device to recommend to the user. As a result, the user can quickly find a property that matches their desired conditions and make a decision while considering the surrounding environment, disaster risks, and the reliability of real estate agents.

[0067] An "information terminal device" is a device that has the function of allowing a user to input their desired conditions and sending that information to a server as communication data.

[0068] A "processing device" is a device that searches for information about real estate based on the user's desired conditions and performs calculations to integrate and evaluate the acquired information.

[0069] An "information storage device" is a storage device used to store information about the property being searched, as well as information about the surrounding environment and risk information.

[0070] An "evaluation-based index" is an index that shows the results of an evaluation that integrates surrounding environmental information, natural disaster risk, and the creditworthiness of the business operator, and presents them in a numerical form.

[0071] "Information regarding the surrounding environment" refers to information such as the safety and security of the area around the property, access to public transportation, and the presence or absence of commercial facilities.

[0072] "Risk information related to natural disasters" refers to information that indicates the risk in the event of natural disasters such as earthquakes and floods, and is calculated based on past disaster data and risk maps.

[0073] "Contractor credit information" refers to information indicating the reliability of construction companies and real estate agents providing properties, and is a score obtained based on third-party evaluation sites and user reviews.

[0074] "Recommended information" refers to information necessary for users to select a property, such as a list of properties ranked based on evaluation-based indicators.

[0075] This invention is an information processing system for efficiently suggesting properties that meet the user's desired housing conditions. The system mainly includes an information terminal device, a processing device, and an information storage device.

[0076] Users input their desired housing requirements using an information terminal device. The information terminal device formats this information as digital data and transmits it to a server via the internet. This transmitted data includes details such as the desired area, floor plan, budget, and distance from the nearest public transportation.

[0077] The server searches for real estate information stored in the information storage device and identifies properties that match the criteria entered by the user. The processing unit then uses this data to first collect information about the surrounding environment. This is done by using external public databases and APIs to obtain information on public safety, security, public transportation access, and commercial facilities.

[0078] Subsequently, the server evaluates risk information related to natural disasters. This risk assessment is performed by analyzing the risk of earthquakes and floods by referring to geographical data. This information is based on past disaster data and risk maps.

[0079] Furthermore, the server evaluates the reliability of property providers. It collects information from third-party review sites and user reviews, and calculates a reliability score using an AI model. This makes it possible to evaluate the risks and reliability associated with each property.

[0080] All aggregated information is calculated on the server as evaluation-based metrics, and properties are ranked based on their scores. Finally, the server sends the ranked property list to the information terminal device and presents it to the user. By referring to this list, users can easily choose a home that meets their needs.

[0081] As a concrete example, a user can input a prompt into the AI ​​model such as, "Please suggest the best property for me to find a safe 3LDK apartment in Tokyo for under 50 million yen." This process allows the user to find their ideal property without spending a lot of time.

[0082] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0083] Step 1:

[0084] The user inputs their desired housing conditions using an information terminal device. The input data includes desired area, floor plan, budget, and distance from the nearest station. The terminal receives this information, formats it as digital data, and sends it to the server. The input is the raw desired conditions, and the output is data in a digital format that the server can receive.

[0085] Step 2:

[0086] The server searches for real estate information stored in the information storage device. The input is a request to the server based on the user's desired conditions, generating a search query for the database. The database is queried, and data for properties matching the conditions is output. At this stage, a database management system is used to efficiently extract the information.

[0087] Step 3:

[0088] The server collects information about the surrounding environment based on the acquired property data. The input is property data, and requests are sent to external APIs and public databases. The acquired security information, transportation access information, and commercial facility information are integrated to generate surrounding environment information as output. Specifically, the server accesses RESTful APIs to obtain the necessary data and integrates it on the server.

[0089] Step 4:

[0090] The server assesses the natural disaster risk for each property. The input is the property's geographical location. The server uses this information to refer to a natural disaster database and evaluates the risk of earthquakes and floods as output. By performing data analysis using historical disaster data and risk maps, it calculates a risk score.

[0091] Step 5:

[0092] The server collects credit information on businesses and calculates a reliability score. The input is information on businesses providing properties, collected by scraping third-party review sites and user reviews. The output is a reliability score calculated for each business. A generative AI model is used to statistically assess the reliability of the collected information.

[0093] Step 6:

[0094] The server generates evaluation-based indices using surrounding environment information, natural disaster risk assessments, and reliability scores for each property. The inputs are the information obtained in the previous step, and the calculations are performed by weighting multiple evaluation criteria. The output is the scored evaluation index for each property, and a ranking score is generated.

[0095] Step 7:

[0096] The server ranks properties based on their evaluation scores. The input is all evaluation scores, and the system sorts the properties based on these scores. The output is a ranking list with numerical evaluations. An algorithm is used to determine the optimal property ranking.

[0097] Step 8:

[0098] The server sends a list of ranked properties to the information terminal device. The output is a list displayed in a user-viewable format, providing information to help the user make the best choice. Based on this information, the user can select the most suitable property.

[0099] (Application Example 1)

[0100] 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."

[0101] As cities develop, it is crucial for residents to find the optimal housing not only by providing property information, but also by utilizing real-time local infrastructure and environmental data. However, currently, there is no system in place to efficiently provide such comprehensive information. A mechanism is needed that leverages the framework of smart cities to rapidly collect and analyze diverse information relevant to housing selection.

[0102] 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.

[0103] In this invention, the server includes terminal means for inputting the user's desired conditions and transmitting the information to a central processing unit; means for acquiring regional infrastructure information from an external API in real time based on the conditions entered by the user and providing information to support the selection of the optimal property; and means for aggregating surrounding environment information, natural disaster risk information, and reliability information of service providers from the acquired asset information and calculating an evaluation index. As a result, users will be able to choose a more comfortable and safe home that takes into account the surrounding environment and disaster risks.

[0104] A "user" is someone who wishes to use an information processing system to find a place to live.

[0105] "Desired conditions" refer to the specific requests that users make when selecting a place to live, and include things like location, budget, and floor plan.

[0106] A "central processing unit" is a computer server that acquires, aggregates, and analyzes real estate information and other related information, and provides information that matches the user's desired conditions.

[0107] "Terminal device" refers to an electronic device used by a user to input desired conditions and transmit that information to a central processing unit, and includes smartphones, tablets, personal computers, etc.

[0108] "Asset information" refers to specific real estate property information, including location, price, floor plan, and detailed specifications.

[0109] "Information acquired in real time" refers to the latest regional infrastructure information and environmental data that are collected without time delay and reflect the current state.

[0110] "Surrounding environment information" refers to data on external factors that users should consider when choosing a residence, and includes information on public safety, transportation options, and commercial facilities.

[0111] "Natural disaster risk information" refers to information that assesses geological activity risks and flood risks based on past data and predictions.

[0112] "Service provider reliability information" refers to reliability evaluation data calculated based on indicators such as the creditworthiness of the organization providing the property and user reviews.

[0113] An "evaluation index" is a quantified evaluation result calculated based on acquired information, and serves as a standard to facilitate property selection.

[0114] In implementing this invention, desired conditions are entered from a terminal used by the user, and the information is transmitted to a central processing unit. This terminal is an electronic device such as a smartphone or tablet, and the desired conditions can be entered through a user interface.

[0115] The central processing unit retrieves asset information from the database based on the input information and, if necessary, collects real-time regional infrastructure information using external APIs. This process utilizes a cloud computing environment. Specifically, data aggregation and analysis are performed using Amazon Web Services and Google Cloud Platform.

[0116] The acquired asset information is further aggregated with information on the surrounding environment, natural disaster risks, and the reliability of service providers. Based on this aggregated information, evaluation indicators are calculated, and properties best suited to the user's needs are proposed.

[0117] The terminal displays evaluation results transmitted from the central processing unit, allowing users to view detailed information and compare options. This enables users to choose a home that takes the surrounding environment and safety into consideration.

[0118] For example, if a user enters the conditions "2LDK in Osaka City, within a 10-minute walk from the station, pet-friendly," the central processing unit searches for properties that match these conditions, analyzes transportation access information and local safety information, and suggests the most suitable property.

[0119] Examples of prompt statements for a generative AI model are as follows:

[0120] Based on the user's input criteria, search for properties in Osaka City and suggest the most suitable accommodations. Pay particular attention to transportation access information, surrounding facilities, and neighborhood safety. Pet-friendly properties will be given priority.

[0121] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0122] Step 1:

[0123] The user enters their desired conditions using a terminal. The entered conditions are specific requirements for selecting a residence (e.g., location, budget, floor plan, etc.), and this serves as the initial input for the system. This data is obtained from the terminal's user interface.

[0124] Step 2:

[0125] The terminal transmits the user's entered preferences to the central processing unit. Upon this transmission, the system begins preparing to search for property information based on those preferences. The data is transmitted to the server in a secure manner.

[0126] Step 3:

[0127] The server retrieves property information from the database based on the received request criteria. The retrieved information includes real estate details such as location, price, and floor plan. A database query is then generated to extract properties that match the criteria.

[0128] Step 4:

[0129] The server obtains real-time local infrastructure information via external APIs. This includes traffic conditions, surrounding facilities, and safety information, and the latest data is collected through integration with external systems. In this step, the information is kept up-to-date by obtaining data in real time.

[0130] Step 5:

[0131] The server aggregates the acquired asset information with surrounding environment information (security, transportation access, commercial facilities), natural disaster risk information (geological activity risk, flood risk), and reliability information of service providers, and calculates evaluation indicators. Here, the data is integrated and a comprehensive score is performed through an evaluation algorithm.

[0132] Step 6:

[0133] The server ranks properties based on evaluation metrics and sends the information, organized for easy viewing, to the user's terminal. The ranking process weights particularly important criteria to generate appropriate recommendations.

[0134] Step 7:

[0135] The evaluation results sent from the server are displayed on the user's device. The display is designed to make it easy for the user to compare property details, allowing them to choose the most suitable home. A visual user interface is used in this step.

[0136] 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.

[0137] One embodiment of the present invention is an information processing system for efficiently finding properties that meet the user's desired housing conditions, incorporating an emotion engine that recognizes the user's emotions. This system can collect, analyze, and evaluate optimal property information based on the user's desired conditions, and further make suggestions that take into account the user's emotional state.

[0138] First, the user uses a terminal to input their ideal housing requirements. These requirements include specific elements such as desired area, floor plan, budget, commute time, and school district. The terminal then sends the user's input information to the server.

[0139] The server efficiently retrieves properties that match the user's desired criteria from the real estate database. The retrieved property information includes not only basic property data but also information on the surrounding environment and natural disaster risk.

[0140] Next, the server uses an emotion engine to analyze the user's emotional state from their input data and past selection history. The emotion engine recognizes the user's emotional state at the time of input (e.g., excitement, confusion, satisfaction, etc.) and adjusts the property recommendations to meet the user's needs and expectations.

[0141] For example, if a user is experiencing stress while choosing a property, the emotion engine will recognize this emotion and adjust the amount and complexity of information presented to make the properties more intuitive and easier to understand. Similarly, if a user has expectations or a positive feeling towards a particular area, prioritizing the presentation of property information in that area can increase user satisfaction.

[0142] In this way, the server adjusts the order and content of property suggestions based on feedback from the emotion engine, ultimately generating an optimal property list. This list is sent to the terminal and displayed to the user. Based on the displayed information, the user can review the details and select the most suitable home.

[0143] This system allows users to not only receive property information but also personalized information tailored to their individual emotional state, which is expected to significantly improve the traditional property selection process.

[0144] The following describes the processing flow.

[0145] Step 1:

[0146] The user uses a terminal to input their desired housing conditions. These conditions include specific areas, floor plans, budget, walking distance from the station, and proximity to educational facilities. This sets the specific criteria the user is looking for. The terminal processes this information and sends it to the server.

[0147] Step 2:

[0148] The server creates a search query for the real estate database based on the user's requested criteria. This query is used to retrieve property information filtered by location, price, floor plan, etc. The server then uses the results to identify properties that match the user's needs.

[0149] Step 3:

[0150] The server then collects additional information on the surrounding environment and natural disaster risk for the acquired property. This information includes local safety, transportation access, and the presence of commercial facilities, while the natural disaster risk information includes data on earthquakes and floods. This information is obtained via public databases and external APIs.

[0151] Step 4:

[0152] The server takes in additional data entered from the terminal to understand the user's emotional state and analyzes it using an emotion engine. Based on the input information and past user selection history, the emotion engine determines the user's current emotion (e.g., excitement, tension, anxiety, etc.).

[0153] Step 5:

[0154] The server adjusts the property recommendations based on the analysis results of the emotion engine. If the user is feeling stressed, the recommendations are simplified and information is presented more intuitively; if the user is satisfied, detailed and comprehensive information is prioritized.

[0155] Step 6:

[0156] The server optimizes the ranking of properties based on the user's emotional state, performs scoring, and generates a ranking that aligns with the user's desired criteria. The generated ranking, reflecting the overall evaluation score of the properties, is then sent to the terminal.

[0157] Step 7:

[0158] The terminal displays a ranked list of properties to the user, showing each property's rating, detailed information, and images. Based on this information, the user can compare properties and choose the most suitable home.

[0159] Step 8:

[0160] Users review the presented information and select properties that interest them to further explore the details. Furthermore, they can complete their selection by being satisfied with the optimized suggestions provided by the emotion engine.

[0161] (Example 2)

[0162] 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 will be referred to as the "terminal."

[0163] Traditional real estate information systems had the problem of placing an excessive burden on users and lowering their satisfaction by mechanically presenting property information without considering the emotional stress or personal expectations of the users. Furthermore, the effort required to provide property information tailored to each user's preferences and desires created a need for improved user experience.

[0164] 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.

[0165] In this invention, the server includes an input means for inputting the user's desired conditions and transmitting the information, a processing means for acquiring asset information based on the user's desired conditions, and an adjustment means using a generative AI model that evaluates the acquired asset information and adjusts the proposals. This makes it possible to provide customized property information that takes the user's emotions into consideration.

[0166] An "input means" is a means for a user to input their desired housing conditions into an information processing device and transmit this information.

[0167] "Processing means" refers to means for obtaining appropriate asset information from a recording device based on the user's desired conditions.

[0168] "Analysis means" refers to a method for aggregating environmental information, geographical risk information, and provider reliability information from acquired asset information and calculating evaluation indicators.

[0169] A "generative AI model" is a model that utilizes artificial intelligence technology to analyze the emotional state of users and generate property information that has been adjusted accordingly.

[0170] "Adjustment methods" refer to means for customizing and optimizing the order and content of property suggestions based on feedback from the generating AI model, according to the user's emotional state.

[0171] "Transmission means" refers to the means of ultimately transmitting the adjusted property information to an information processing device and presenting it to the user.

[0172] This invention is a system that allows users to input their desired housing conditions and provides property information that takes their emotions into consideration. The system mainly consists of a terminal, a server, and a generative AI model working together.

[0173] First, the user uses a device to input their desired housing conditions. This includes location, floor plan, budget, and commute time. The device then sends this information to the server as formatted data.

[0174] The server uses processing tools to retrieve relevant asset information from the recording device based on the received data. SQL database technology is utilized for this information collection. Various real estate-related data are structured as asset information and are efficiently retrieved from the appropriate database as needed.

[0175] The acquired asset information is aggregated through analytical methods, along with environmental information, geographical risk information, and provider reliability information. This enables a detailed evaluation of the user's desired conditions. Specific examples include natural disaster risk and security information for the area where the property is located.

[0176] Next, the server uses a generative AI model to analyze the user's input data and past behavioral history. The generative AI model uses natural language processing (NLP) techniques to identify the user's emotional state. This emotional data is used in adjustments to optimize the priority and content of property recommendations for the user.

[0177] Finally, the adjusted property information is transmitted to the terminal via a transmission method and displayed on the user's screen. At this point, the user can check the property details based on the displayed information and contact the real estate agent for any properties that interest them further.

[0178] An example of a prompt message is: "Collect information on 3LDK apartments in nature-rich areas within a 30-minute radius from the real estate database, and set the priority order while considering the user's sentiment." Instructions can be given to the generating AI model in this format.

[0179] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0180] Step 1:

[0181] The user uses a terminal to enter their desired housing conditions. Input fields include location, floor plan, budget, and commute time. This input data is converted to JSON format and sent to the server. The terminal validates the user's input, formats it for accuracy, and prepares it for the next processing step.

[0182] Step 2:

[0183] The server retrieves relevant asset information from the recording device based on the user's desired conditions received from the terminal. Specifically, the server executes a query on the SQL database and extracts real estate data that matches the conditions. The input is an SQL query with a conditional expression, and the output is asset information that matches the conditions.

[0184] Step 3:

[0185] The server aggregates the acquired asset information using analytical tools. It combines environmental information, geographical risk information, and "provider reliability information" to calculate a comprehensive evaluation score. The data processing performed here involves calculation and integration of data from various sources. The input is asset information, and the output is a composite evaluation score.

[0186] Step 4:

[0187] The server analyzes the user's emotional state using a generative AI model. It extracts emotional patterns from the user's past behavior history and current input data using natural language processing techniques. This identifies the user's current emotional state. Input consists of past behavior data and current input data, while output is the result of the emotional state analysis.

[0188] Step 5:

[0189] The server uses adjustment mechanisms to customize the priority and content of property suggestions based on the user's emotional state. It implements adaptive rankings, incorporating feedback from the generating AI model. Input is the analysis results and evaluation scores of the emotional state, and output is the adjusted property list.

[0190] Step 6:

[0191] The server transmits the adjusted property information to the terminal via a transmission method. The terminal displays an optimized property list to the user. Based on this information, the user can perform detailed checks and make further decisions. The input is the adjusted property list, and the output is the information displayed to the user.

[0192] (Application Example 2)

[0193] 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".

[0194] Traditional real estate information systems allowed users to search for properties based on their desired criteria, but they lacked the ability to suggest properties that took into account the user's emotional state. As a result, users sometimes experienced stress when choosing a property, or did not receive suggestions that adequately met their expectations, making it necessary to improve user satisfaction.

[0195] 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.

[0196] In this invention, the server includes means for using a data device to input the user's desired conditions and transmit the information to the server; means for using an information processing device to acquire property information from a recording medium based on the user's desired conditions; and means for using an information processing device to aggregate surrounding environment information, natural disaster risk information, and reliability information of the providing organization for the acquired property information and calculate an evaluation value. This makes it possible to propose properties that are more accurate and satisfying, taking into account the user's emotional state.

[0197] A "user" is the entity that searches for and selects property information.

[0198] "Desired conditions" refer to the specific criteria and requests that the user has regarding their ideal property.

[0199] A "data device" is a terminal or device used to process user input information and transmit it to a server.

[0200] An "information processing device" is a computer system that combines acquired property information with surrounding environment information and natural disaster risk information to calculate evaluation values.

[0201] "Recording medium" refers to a database or storage device where property information is stored.

[0202] "Surrounding environment information" refers to information including safety information, transportation information, and commercial location information regarding the area surrounding the property in question.

[0203] "Natural disaster risk information" refers to information about the risk of disasters related to a property, based on earthquake risk data and flood risk data.

[0204] A "provider" refers to an organization or group that provides services related to property information and its evaluation.

[0205] An "evaluation score" is a score calculated based on property information, and it is a numerical value that indicates the value and suitability of the property.

[0206] An "emotion recognition device" is a tool or system that analyzes a user's emotional state and incorporates that information into property recommendations.

[0207] This invention provides a real estate information system that allows users to efficiently find the optimal property that meets their desired conditions, while also providing a function that takes into account the user's emotions. This system consists of multiple information processing devices, each of which plays a specific role.

[0208] Users input their desired criteria using a data device. This data device functions as a terminal that collects information such as the region, floor plan, and budget specified by the user and transmits it to a server. Examples of such devices include smartphones and tablets.

[0209] The server uses an information processing device to retrieve appropriate property information from the storage medium based on the received desired conditions. This retrieval process can utilize a database running on the Google Cloud Platform.

[0210] Furthermore, the server aggregates information on the surrounding environment, safety, transportation, natural disaster risk (including earthquake and flood risk data), and the reliability of the providing organizations. Based on this information, it calculates evaluation scores and ranks properties. This allows the user to receive property recommendations that are most suitable for them.

[0211] Furthermore, the emotion recognition device has the capability to analyze the user's emotional state in real time using NVIDIA Jetson. Once the user's emotions are recognized, the property suggestions are adjusted based on that feedback. For example, if the user is excited about the property search, the suggestions are broadened and new options are offered, while if they are feeling stressed, the options are narrowed down.

[0212] For example, if a user uses this system in the evening when they are a little tired, the emotion recognition device will detect this state and prioritize presenting properties that promote relaxation. Another example of a prompt using the generative AI model is, "When the user is feeling stressed, reduce the number of property options and explain them in simpler terms."

[0213] By operating such a system, users can find properties that are more satisfying than ever before.

[0214] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0215] Step 1:

[0216] The user uses a data device to enter their desired conditions. The entered data includes information such as region, floor plan, and budget. Once this data is entered and the user presses the submit button, the data is sent to the server.

[0217] Step 2:

[0218] The server retrieves property information from the storage medium based on the user's requested criteria. This storage medium includes databases and external APIs. The server searches for property information that matches the criteria and generates a list of properties that satisfy the conditions. This output becomes the input for the next process.

[0219] Step 3:

[0220] The server aggregates property information, surrounding environment information, natural disaster risk information, and reliability information of the providing organization, and calculates an evaluation score by weighting each element. The data used here is obtained from existing open data and specific APIs. The calculation is performed based on an algorithm for calculating the evaluation score.

[0221] Step 4:

[0222] Based on the evaluation scores, the server ranks the properties and sends the information to the data device for suggestion to the user. The transmitted information is displayed as a list on the user's terminal, providing the user with a visually intuitive selection. This output serves as a foundation to support the user's decision-making.

[0223] Step 5:

[0224] The server uses an emotion recognition device to analyze the user's emotional state in real time at the time of input. Based on the change in emotion, it adjusts the suggested properties using a generative AI model. Specifically, if the user is stressed, it provides simpler suggestions; if they are excited, it presents more options. An example of a prompt message would be, "When the user is stressed, reduce the number of property options and explain them in simpler terms."

[0225] Step 6:

[0226] The terminal visually displays the adjusted property suggestions received from the server to the user. The user then reviews the details and selects the most suitable home. This final output is a crucial element in enabling the user to confidently make the best decision.

[0227] 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.

[0228] 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.

[0229] 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.

[0230] [Second Embodiment]

[0231] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0232] 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.

[0233] 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).

[0234] 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.

[0235] 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.

[0236] 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).

[0237] 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.

[0238] 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.

[0239] 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.

[0240] 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.

[0241] 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.

[0242] 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".

[0243] One embodiment of the present invention is to provide an information processing system for efficiently finding properties that meet the user's desired housing conditions. This system collects, analyzes, and evaluates appropriate real estate information based on the user's desired conditions and proposes the optimal option.

[0244] First, the user uses their device to enter their desired housing conditions. Specifically, these conditions include the desired location, floor plan, budget, and walking distance from the nearest station. This information is received by the device and sent to the server.

[0245] The server retrieves property data that matches the user's criteria from the real estate database. The database filters properties based on criteria such as location, price range, and floor plan.

[0246] Next, the server investigates the surrounding environment information for the acquired property information. It obtains and analyzes information including safety information, transportation access, and the presence of commercial facilities from external APIs and public databases.

[0247] Furthermore, the server evaluates data on natural disaster risks (earthquake risk, flood risk, etc.). Based on geographical information, it refers to past disaster data and risk maps for the relevant area to perform a risk assessment for each property.

[0248] At the same time, to evaluate the reliability of the construction and real estate companies providing the properties, the server calculates a reliability score based on third-party review sites and user reviews.

[0249] Based on the aggregated information described above, the server calculates an overall evaluation score for each property and determines its suitability to the user's desired conditions. The evaluation scores are quantified and ranked to allow for easy comparison of each property.

[0250] The server then ranks the properties based on their evaluation scores and creates a list of the best properties. This information is sent to the terminal and displayed to the user. The user can then compare evaluation comments and detailed information from the presented properties and select a suitable place to live.

[0251] For example, if a user is looking for a 3LDK apartment in Tokyo priced under 50 million yen, the system will search for suitable properties and recommend those with good surrounding environments and low risk of natural disasters as top-ranked options. This process allows users to find their ideal home in a short amount of time.

[0252] The following describes the processing flow.

[0253] Step 1:

[0254] Users enter their desired housing requirements through a dedicated application or web interface. These requirements include preferred area, floor plan, budget, walking distance from the nearest station, and educational environment for children. The information entered by the user is collected as data by the terminal and sent to the server.

[0255] Step 2:

[0256] The server queries the real estate information database based on the user's requested conditions. It uses queries such as SQL to retrieve property information that matches the specified conditions. At this stage, basic property information (e.g., location, price, area, year built, etc.) is collected.

[0257] Step 3:

[0258] The server accesses external APIs and public databases to aggregate surrounding environment information for each acquired property. This information includes local safety conditions, nearest transportation options, and the presence of nearby commercial facilities and medical institutions. The server analyzes this data to evaluate whether each property has an optimal living environment.

[0259] Step 4:

[0260] The server assesses the natural disaster risk of each property based on its geographical location. It retrieves risk data for earthquakes, floods, tsunamis, etc., from a geographic database and applies it to the location of each property to perform a risk assessment. This information serves as a crucial evaluation criterion when providing information to users.

[0261] Step 5:

[0262] The server collects data from third-party review sites and user reviews to assess the reliability of construction and real estate companies offering properties. This data is then used to calculate a reliability score, which is incorporated into the scoring system as a key factor in property selection.

[0263] Step 6:

[0264] The server calculates an overall evaluation score for each property based on the collected information. Specifically, it integrates basic property information, assessment of the surrounding environment, natural disaster risk assessment, and reliability assessment to perform the scoring. This score quantifies how well the property matches the user's desired conditions.

[0265] Step 7:

[0266] The server ranks properties based on the calculated evaluation score and lists them in descending order of suitability. This ranked property list is then sent to the terminal and prepared for display to the user.

[0267] Step 8:

[0268] The terminal displays the received ranking information to the user. Based on the displayed information, the user can compare each property and view detailed information. The user can select the property that best suits their preferences, taking into account evaluation scores and various other information.

[0269] (Example 1)

[0270] 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".

[0271] In modern society, when buying or renting real estate, users need to select the optimal property from a vast amount of information. However, conventional methods place a heavy burden on users to collect and analyze information themselves, making it difficult to quickly find a property that meets their desired conditions. Furthermore, there is a lack of means to comprehensively judge the surrounding environment, disaster risk, and reliability of service providers of selected properties, which often leaves users feeling anxious. This invention aims to solve the above problems by providing a system that efficiently proposes properties that meet the user's desired conditions.

[0272] 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.

[0273] In this invention, the server includes means for retrieving information on real estate from an information storage device based on the user's desired conditions; means for integrating information on the surrounding environment, risk information regarding natural disasters, and credit information of real estate agents with the retrieved real estate information to generate an evaluation-based index; and means for ranking real estate based on the evaluation-based index and transmitting information to an information terminal device to recommend to the user. As a result, the user can quickly find a property that matches their desired conditions and make a decision while considering the surrounding environment, disaster risks, and the reliability of real estate agents.

[0274] An "information terminal device" is a device that has the function of allowing a user to input their desired conditions and sending that information to a server as communication data.

[0275] A "processing device" is a device that searches for information about real estate based on the user's desired conditions and performs calculations to integrate and evaluate the acquired information.

[0276] An "information storage device" is a storage device used to store information about the property being searched, as well as information about the surrounding environment and risk information.

[0277] The "evaluation-based index" is an index that integrates peripheral environmental information, natural disaster risks, and the credit information of businesses, and shows the results evaluated in a quantified form.

[0278] The "information on the surrounding environment" refers to information indicating the public security, safety, access to public transportation, presence of commercial facilities, etc. around the real estate.

[0279] The "risk information related to natural disasters" refers to information indicating the risks when natural disasters such as earthquakes and floods occur, and is calculated based on past disaster data and risk maps.

[0280] The "credit information of businesses" refers to information indicating the reliability of construction companies and real estate companies that provide properties, and is a score obtained based on third-party evaluation sites and user reviews.

[0281] The "recommended information" refers to information necessary for users to select properties, such as a list of properties ranked based on the evaluation-based index.

[0282] This invention is an information processing system for efficiently proposing properties that meet the conditions of the housing desired by the user. The system mainly includes an information terminal device, a processing device, and an information storage device.

[0283] The user uses the information terminal device to input the conditions of the desired housing. The information terminal device formats this information as digital data and transmits it to the server via the Internet. The transmitted data includes details such as the desired area, floor plan, budget, and distance from the nearest transportation facility.

[0284] The server searches for information on real estate stored in the information storage device and identifies properties that match the conditions input by the user. The processing device uses this data to first collect information on the surrounding environment. This is done by using external public databases or APIs to obtain information on public security, safety, access to public transportation, and commercial facilities.

[0285] After that, the server evaluates the risk information regarding natural disasters. This risk assessment is performed by analyzing the risks of earthquakes and floods by referring to geographical data. It is information based on past disaster data and risk maps.

[0286] Furthermore, the server evaluates the reliability of the property providing company. Information is collected from third - party evaluation sites and user reviews, and a reliability score is calculated using an AI model. This enables the evaluation of the risks and reliability associated with each property.

[0287] All the aggregated information is calculated within the server as an index based on the evaluation, and the properties are ranked based on the scores. Finally, the server transmits the ranked list of properties to the information terminal device and presents it to the user. By referring to this list, it becomes easier for the user to select a dwelling that meets their wishes.

[0288] As a specific example, it is possible for the user to input a prompt such as "Please propose the optimal property when looking for a safe condominium within 3LDK and within 50 million yen in Tokyo" into the generative AI model. Through this process, the user can find an ideal property without spending time.

[0289] The flow of the specific process in Example 1 will be described using Figure 11.

[0290] Step 1:

[0291] The user inputs the conditions of the desired dwelling using the information terminal device. The data to be input includes the desired area, floor plan, budget, distance from the nearest station, etc. The terminal receives this information, formats it as digital data, and transmits it to the server. The input is the raw desired conditions, and the output is data in a digital format that the server can receive.

[0292] Step 2:

[0293] The server searches for real estate information stored in the information storage device. The input is a request to the server based on the user's desired conditions, generating a search query for the database. The database is queried, and data for properties matching the conditions is output. At this stage, a database management system is used to efficiently extract the information.

[0294] Step 3:

[0295] The server collects information about the surrounding environment based on the acquired property data. The input is property data, and requests are sent to external APIs and public databases. The acquired security information, transportation access information, and commercial facility information are integrated to generate surrounding environment information as output. Specifically, the server accesses RESTful APIs to obtain the necessary data and integrates it on the server.

[0296] Step 4:

[0297] The server assesses the natural disaster risk for each property. The input is the property's geographical location. The server uses this information to refer to a natural disaster database and evaluates the risk of earthquakes and floods as output. By performing data analysis using historical disaster data and risk maps, it calculates a risk score.

[0298] Step 5:

[0299] The server collects credit information on businesses and calculates a reliability score. The input is information on businesses providing properties, collected by scraping third-party review sites and user reviews. The output is a reliability score calculated for each business. A generative AI model is used to statistically assess the reliability of the collected information.

[0300] Step 6:

[0301] The server uses the surrounding environment information, natural disaster risk assessment, and reliability score for each property to generate evaluation-based metrics. The input is each piece of information obtained in the previous step, and it is calculated by weighting multiple evaluation criteria. The output is the scored evaluation metrics for each property, and a score for ranking is generated.

[0302] Step 7:

[0303] The server ranks the properties based on the evaluation scores of each property. The input is all the evaluation scores, and the system rearranges the properties based on this. The output is a ranked list with the evaluations digitized. An algorithm is used to determine the optimal ranking of the properties.

[0304] Step 8:

[0305] The server sends the list of ranked properties to the information terminal device. The output is a list presented in a format viewable by the user, providing information for the user to make an optimal choice. The user can select the most suitable property based on this information.

[0306] (Application Example 1)

[0307] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0308] With the development of the city, in order for residents to find the optimal residence, it is important not only to provide property information but also to utilize real-time regional infrastructure information and environmental data. However, currently, a system for efficiently providing such comprehensive information is not yet established. There is a need for a mechanism that can quickly collect and analyze various information in housing selection by leveraging the framework of the smart city.

[0309] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following respective means.

[0310] In this invention, the server includes terminal means for inputting the user's desired conditions and transmitting the information to a central processing unit; means for acquiring regional infrastructure information from an external API in real time based on the conditions entered by the user and providing information to support the selection of the optimal property; and means for aggregating surrounding environment information, natural disaster risk information, and reliability information of service providers from the acquired asset information and calculating an evaluation index. As a result, users will be able to choose a more comfortable and safe home that takes into account the surrounding environment and disaster risks.

[0311] A "user" is someone who wishes to use an information processing system to find a place to live.

[0312] "Desired conditions" refer to the specific requests that users make when selecting a place to live, and include things like location, budget, and floor plan.

[0313] A "central processing unit" is a computer server that acquires, aggregates, and analyzes real estate information and other related information, and provides information that matches the user's desired conditions.

[0314] "Terminal device" refers to an electronic device used by a user to input desired conditions and transmit that information to a central processing unit, and includes smartphones, tablets, personal computers, etc.

[0315] "Asset information" refers to specific real estate property information, including location, price, floor plan, and detailed specifications.

[0316] "Information acquired in real time" refers to the latest regional infrastructure information and environmental data that are collected without time delay and reflect the current state.

[0317] "Surrounding environment information" refers to data on external factors that users should consider when choosing a residence, and includes information on public safety, transportation options, and commercial facilities.

[0318] "Natural disaster risk information" refers to information that assesses geological activity risks and flood risks based on past data and predictions.

[0319] "Service provider reliability information" refers to reliability evaluation data calculated based on indicators such as the creditworthiness of the organization providing the property and user reviews.

[0320] An "evaluation index" is a quantified evaluation result calculated based on acquired information, and serves as a standard to facilitate property selection.

[0321] In implementing this invention, desired conditions are entered from a terminal used by the user, and the information is transmitted to a central processing unit. This terminal is an electronic device such as a smartphone or tablet, and the desired conditions can be entered through a user interface.

[0322] The central processing unit retrieves asset information from the database based on the input information and, if necessary, collects real-time regional infrastructure information using external APIs. This process utilizes a cloud computing environment. Specifically, data aggregation and analysis are performed using Amazon Web Services and Google Cloud Platform.

[0323] The acquired asset information is further aggregated with information on the surrounding environment, natural disaster risks, and the reliability of service providers. Based on this aggregated information, evaluation indicators are calculated, and properties best suited to the user's needs are proposed.

[0324] The terminal displays evaluation results transmitted from the central processing unit, allowing users to view detailed information and compare options. This enables users to choose a home that takes the surrounding environment and safety into consideration.

[0325] For example, if a user enters the conditions "2LDK in Osaka City, within a 10-minute walk from the station, pet-friendly," the central processing unit searches for properties that match these conditions, analyzes transportation access information and local safety information, and suggests the most suitable property.

[0326] Examples of prompt statements for a generative AI model are as follows:

[0327] Based on the user's input criteria, search for properties in Osaka City and suggest the most suitable accommodations. Pay particular attention to transportation access information, surrounding facilities, and neighborhood safety. Pet-friendly properties will be given priority.

[0328] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0329] Step 1:

[0330] The user enters their desired conditions using a terminal. The entered conditions are specific requirements for selecting a residence (e.g., location, budget, floor plan, etc.), and this serves as the initial input for the system. This data is obtained from the terminal's user interface.

[0331] Step 2:

[0332] The terminal transmits the user's entered preferences to the central processing unit. Upon this transmission, the system begins preparing to search for property information based on those preferences. The data is transmitted to the server in a secure manner.

[0333] Step 3:

[0334] The server retrieves property information from the database based on the received request criteria. The retrieved information includes real estate details such as location, price, and floor plan. A database query is then generated to extract properties that match the criteria.

[0335] Step 4:

[0336] The server obtains real-time local infrastructure information via external APIs. This includes traffic conditions, surrounding facilities, and safety information, and the latest data is collected through integration with external systems. In this step, the information is kept up-to-date by obtaining data in real time.

[0337] Step 5:

[0338] The server aggregates the acquired asset information with surrounding environment information (security, transportation access, commercial facilities), natural disaster risk information (geological activity risk, flood risk), and reliability information of service providers, and calculates evaluation indicators. Here, the data is integrated and a comprehensive score is performed through an evaluation algorithm.

[0339] Step 6:

[0340] The server ranks properties based on evaluation metrics and sends the information, organized for easy viewing, to the user's terminal. The ranking process weights particularly important criteria to generate appropriate recommendations.

[0341] Step 7:

[0342] The evaluation results sent from the server are displayed on the user's device. The display is designed to make it easy for the user to compare property details, allowing them to choose the most suitable home. A visual user interface is used in this step.

[0343] 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.

[0344] One embodiment of the present invention is an information processing system for efficiently finding properties that meet the user's desired housing conditions, incorporating an emotion engine that recognizes the user's emotions. This system can collect, analyze, and evaluate optimal property information based on the user's desired conditions, and further make suggestions that take into account the user's emotional state.

[0345] First, the user uses a terminal to input their ideal housing requirements. These requirements include specific elements such as desired area, floor plan, budget, commute time, and school district. The terminal then sends the user's input information to the server.

[0346] The server efficiently retrieves properties that match the user's desired criteria from the real estate database. The retrieved property information includes not only basic property data but also information on the surrounding environment and natural disaster risk.

[0347] Next, the server uses an emotion engine to analyze the user's emotional state from their input data and past selection history. The emotion engine recognizes the user's emotional state at the time of input (e.g., excitement, confusion, satisfaction, etc.) and adjusts the property recommendations to meet the user's needs and expectations.

[0348] For example, if a user is experiencing stress while choosing a property, the emotion engine will recognize this emotion and adjust the amount and complexity of information presented to make the properties more intuitive and easier to understand. Similarly, if a user has expectations or a positive feeling towards a particular area, prioritizing the presentation of property information in that area can increase user satisfaction.

[0349] In this way, the server adjusts the order and content of property suggestions based on feedback from the emotion engine, ultimately generating an optimal property list. This list is sent to the terminal and displayed to the user. Based on the displayed information, the user can review the details and select the most suitable home.

[0350] This system allows users to not only receive property information but also personalized information tailored to their individual emotional state, which is expected to significantly improve the traditional property selection process.

[0351] The following describes the processing flow.

[0352] Step 1:

[0353] The user uses a terminal to input their desired housing conditions. These conditions include specific areas, floor plans, budget, walking distance from the station, and proximity to educational facilities. This sets the specific criteria the user is looking for. The terminal processes this information and sends it to the server.

[0354] Step 2:

[0355] The server creates a search query for the real estate database based on the user's requested criteria. This query is used to retrieve property information filtered by location, price, floor plan, etc. The server then uses the results to identify properties that match the user's needs.

[0356] Step 3:

[0357] The server then collects additional information on the surrounding environment and natural disaster risk for the acquired property. This information includes local safety, transportation access, and the presence of commercial facilities, while the natural disaster risk information includes data on earthquakes and floods. This information is obtained via public databases and external APIs.

[0358] Step 4:

[0359] The server takes in additional data entered from the terminal to understand the user's emotional state and analyzes it using an emotion engine. Based on the input information and past user selection history, the emotion engine determines the user's current emotion (e.g., excitement, tension, anxiety, etc.).

[0360] Step 5:

[0361] The server adjusts the property recommendations based on the analysis results of the emotion engine. If the user is feeling stressed, the recommendations are simplified and information is presented more intuitively; if the user is satisfied, detailed and comprehensive information is prioritized.

[0362] Step 6:

[0363] The server optimizes the ranking of properties based on the user's emotional state, performs scoring, and generates a ranking that aligns with the user's desired criteria. The generated ranking, reflecting the overall evaluation score of the properties, is then sent to the terminal.

[0364] Step 7:

[0365] The terminal displays a ranked list of properties to the user, showing each property's rating, detailed information, and images. Based on this information, the user can compare properties and choose the most suitable home.

[0366] Step 8:

[0367] Users review the presented information and select properties that interest them to further explore the details. Furthermore, they can complete their selection by being satisfied with the optimized suggestions provided by the emotion engine.

[0368] (Example 2)

[0369] 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".

[0370] Traditional real estate information systems had the problem of placing an excessive burden on users and lowering their satisfaction by mechanically presenting property information without considering the emotional stress or personal expectations of the users. Furthermore, the effort required to provide property information tailored to each user's preferences and desires created a need for improved user experience.

[0371] 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.

[0372] In this invention, the server includes an input means for inputting the user's desired conditions and transmitting the information, a processing means for acquiring asset information based on the user's desired conditions, and an adjustment means using a generative AI model that evaluates the acquired asset information and adjusts the proposals. This makes it possible to provide customized property information that takes the user's emotions into consideration.

[0373] An "input means" is a means for a user to input their desired housing conditions into an information processing device and transmit this information.

[0374] "Processing means" refers to means for obtaining appropriate asset information from a recording device based on the user's desired conditions.

[0375] "Analysis means" refers to a method for aggregating environmental information, geographical risk information, and provider reliability information from acquired asset information and calculating evaluation indicators.

[0376] A "generative AI model" is a model that utilizes artificial intelligence technology to analyze the emotional state of users and generate property information that has been adjusted accordingly.

[0377] "Adjustment methods" refer to means for customizing and optimizing the order and content of property suggestions based on feedback from the generating AI model, according to the user's emotional state.

[0378] "Transmission means" refers to the means of ultimately transmitting the adjusted property information to an information processing device and presenting it to the user.

[0379] This invention is a system that allows users to input their desired housing conditions and provides property information that takes their emotions into consideration. The system mainly consists of a terminal, a server, and a generative AI model working together.

[0380] First, the user uses a device to input their desired housing conditions. This includes location, floor plan, budget, and commute time. The device then sends this information to the server as formatted data.

[0381] The server uses processing tools to retrieve relevant asset information from the recording device based on the received data. SQL database technology is utilized for this information collection. Various real estate-related data are structured as asset information and are efficiently retrieved from the appropriate database as needed.

[0382] The acquired asset information is aggregated through analytical methods, along with environmental information, geographical risk information, and provider reliability information. This enables a detailed evaluation of the user's desired conditions. Specific examples include natural disaster risk and security information for the area where the property is located.

[0383] Next, the server uses a generative AI model to analyze the user's input data and past behavioral history. The generative AI model uses natural language processing (NLP) techniques to identify the user's emotional state. This emotional data is used in adjustments to optimize the priority and content of property recommendations for the user.

[0384] Finally, the adjusted property information is transmitted to the terminal via a transmission method and displayed on the user's screen. At this point, the user can check the property details based on the displayed information and contact the real estate agent for any properties that interest them further.

[0385] An example of a prompt message is: "Collect information on 3LDK apartments in nature-rich areas within a 30-minute radius from the real estate database, and set the priority order while considering the user's sentiment." Instructions can be given to the generating AI model in this format.

[0386] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0387] Step 1:

[0388] The user uses a terminal to enter their desired housing conditions. Input fields include location, floor plan, budget, and commute time. This input data is converted to JSON format and sent to the server. The terminal validates the user's input, formats it for accuracy, and prepares it for the next processing step.

[0389] Step 2:

[0390] The server retrieves relevant asset information from the recording device based on the user's desired conditions received from the terminal. Specifically, the server executes a query on the SQL database and extracts real estate data that matches the conditions. The input is an SQL query with a conditional expression, and the output is asset information that matches the conditions.

[0391] Step 3:

[0392] The server aggregates the acquired asset information using analytical tools. It combines environmental information, geographical risk information, and "provider reliability information" to calculate a comprehensive evaluation score. The data processing performed here involves calculation and integration of data from various sources. The input is asset information, and the output is a composite evaluation score.

[0393] Step 4:

[0394] The server analyzes the user's emotional state using a generative AI model. It extracts emotional patterns from the user's past behavior history and current input data using natural language processing techniques. This identifies the user's current emotional state. Input consists of past behavior data and current input data, while output is the result of the emotional state analysis.

[0395] Step 5:

[0396] The server uses adjustment mechanisms to customize the priority and content of property suggestions based on the user's emotional state. It implements adaptive rankings, incorporating feedback from the generating AI model. Input is the analysis results and evaluation scores of the emotional state, and output is the adjusted property list.

[0397] Step 6:

[0398] The server transmits the adjusted property information to the terminal via a transmission method. The terminal displays an optimized property list to the user. Based on this information, the user can perform detailed checks and make further decisions. The input is the adjusted property list, and the output is the information displayed to the user.

[0399] (Application Example 2)

[0400] 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 as the "terminal".

[0401] Traditional real estate information systems allowed users to search for properties based on their desired criteria, but they lacked the ability to suggest properties that took into account the user's emotional state. As a result, users sometimes experienced stress when choosing a property, or did not receive suggestions that adequately met their expectations, making it necessary to improve user satisfaction.

[0402] 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.

[0403] In this invention, the server includes means for using a data device to input the user's desired conditions and transmit the information to the server; means for using an information processing device to acquire property information from a recording medium based on the user's desired conditions; and means for using an information processing device to aggregate surrounding environment information, natural disaster risk information, and reliability information of the providing organization for the acquired property information and calculate an evaluation value. This makes it possible to propose properties that are more accurate and satisfying, taking into account the user's emotional state.

[0404] A "user" is the entity that searches for and selects property information.

[0405] "Desired conditions" refer to the specific criteria and requests that the user has regarding their ideal property.

[0406] A "data device" is a terminal or device used to process user input information and transmit it to a server.

[0407] An "information processing device" is a computer system that combines acquired property information with surrounding environment information and natural disaster risk information to calculate evaluation values.

[0408] "Recording medium" refers to a database or storage device where property information is stored.

[0409] "Surrounding environment information" refers to information including safety information, transportation information, and commercial location information regarding the area surrounding the property in question.

[0410] "Natural disaster risk information" refers to information about the risk of disasters related to a property, based on earthquake risk data and flood risk data.

[0411] A "provider" refers to an organization or group that provides services related to property information and its evaluation.

[0412] An "evaluation score" is a score calculated based on property information, and it is a numerical value that indicates the value and suitability of the property.

[0413] An "emotion recognition device" is a tool or system that analyzes a user's emotional state and incorporates that information into property recommendations.

[0414] This invention provides a real estate information system that allows users to efficiently find the optimal property that meets their desired conditions, while also providing a function that takes into account the user's emotions. This system consists of multiple information processing devices, each of which plays a specific role.

[0415] Users input their desired criteria using a data device. This data device functions as a terminal that collects information such as the region, floor plan, and budget specified by the user and transmits it to a server. Examples of such devices include smartphones and tablets.

[0416] The server uses an information processing device to retrieve appropriate property information from the storage medium based on the received desired conditions. This retrieval process can utilize a database running on the Google Cloud Platform.

[0417] Furthermore, the server aggregates information on the surrounding environment, safety, transportation, natural disaster risk (including earthquake and flood risk data), and the reliability of the providing organizations. Based on this information, it calculates evaluation scores and ranks properties. This allows the user to receive property recommendations that are most suitable for them.

[0418] Furthermore, the emotion recognition device has the capability to analyze the user's emotional state in real time using NVIDIA Jetson. Once the user's emotions are recognized, the property suggestions are adjusted based on that feedback. For example, if the user is excited about the property search, the suggestions are broadened and new options are offered, while if they are feeling stressed, the options are narrowed down.

[0419] For example, if a user uses this system in the evening when they are a little tired, the emotion recognition device will detect this state and prioritize presenting properties that promote relaxation. Another example of a prompt using the generative AI model is, "When the user is feeling stressed, reduce the number of property options and explain them in simpler terms."

[0420] By operating such a system, users can find properties that are more satisfying than ever before.

[0421] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0422] Step 1:

[0423] The user uses a data device to enter their desired conditions. The entered data includes information such as region, floor plan, and budget. Once this data is entered and the user presses the submit button, the data is sent to the server.

[0424] Step 2:

[0425] The server retrieves property information from the storage medium based on the user's requested criteria. This storage medium includes databases and external APIs. The server searches for property information that matches the criteria and generates a list of properties that satisfy the conditions. This output becomes the input for the next process.

[0426] Step 3:

[0427] The server aggregates property information, surrounding environment information, natural disaster risk information, and reliability information of the providing organization, and calculates an evaluation score by weighting each element. The data used here is obtained from existing open data and specific APIs. The calculation is performed based on an algorithm for calculating the evaluation score.

[0428] Step 4:

[0429] Based on the evaluation scores, the server ranks the properties and sends the information to the data device for suggestion to the user. The transmitted information is displayed as a list on the user's terminal, providing the user with a visually intuitive selection. This output serves as a foundation to support the user's decision-making.

[0430] Step 5:

[0431] The server uses an emotion recognition device to analyze the user's emotional state in real time at the time of input. Based on the change in emotion, it adjusts the suggested properties using a generative AI model. Specifically, if the user is stressed, it provides simpler suggestions; if they are excited, it presents more options. An example of a prompt message would be, "When the user is stressed, reduce the number of property options and explain them in simpler terms."

[0432] Step 6:

[0433] The terminal visually displays the adjusted property suggestions received from the server to the user. The user then reviews the details and selects the most suitable home. This final output is a crucial element in enabling the user to confidently make the best decision.

[0434] 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.

[0435] 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.

[0436] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0437] [Third Embodiment]

[0438] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0439] 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.

[0440] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0441] The 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.

[0442] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0443] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0444] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0445] Figure 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.

[0446] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0447] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0448] In the 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.

[0449] 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".

[0450] One embodiment of the present invention is to provide an information processing system for efficiently finding properties that meet the user's desired housing conditions. This system collects, analyzes, and evaluates appropriate real estate information based on the user's desired conditions and proposes the optimal option.

[0451] First, the user uses their device to enter their desired housing conditions. Specifically, these conditions include the desired location, floor plan, budget, and walking distance from the nearest station. This information is received by the device and sent to the server.

[0452] The server retrieves property data that matches the user's criteria from the real estate database. The database filters properties based on criteria such as location, price range, and floor plan.

[0453] Next, the server investigates the surrounding environment information for the acquired property information. It obtains and analyzes information including safety information, transportation access, and the presence of commercial facilities from external APIs and public databases.

[0454] Furthermore, the server evaluates data on natural disaster risks (earthquake risk, flood risk, etc.). Based on geographical information, it refers to past disaster data and risk maps for the relevant area to perform a risk assessment for each property.

[0455] At the same time, to evaluate the reliability of the construction and real estate companies providing the properties, the server calculates a reliability score based on third-party review sites and user reviews.

[0456] Based on the aggregated information described above, the server calculates an overall evaluation score for each property and determines its suitability to the user's desired conditions. The evaluation scores are quantified and ranked to allow for easy comparison of each property.

[0457] The server then ranks the properties based on their evaluation scores and creates a list of the best properties. This information is sent to the terminal and displayed to the user. The user can then compare evaluation comments and detailed information from the presented properties and select a suitable place to live.

[0458] For example, if a user is looking for a 3LDK apartment in Tokyo priced under 50 million yen, the system will search for suitable properties and recommend those with good surrounding environments and low risk of natural disasters as top-ranked options. This process allows users to find their ideal home in a short amount of time.

[0459] The following describes the processing flow.

[0460] Step 1:

[0461] Users enter their desired housing requirements through a dedicated application or web interface. These requirements include preferred area, floor plan, budget, walking distance from the nearest station, and educational environment for children. The information entered by the user is collected as data by the terminal and sent to the server.

[0462] Step 2:

[0463] The server queries the real estate information database based on the user's requested conditions. It uses queries such as SQL to retrieve property information that matches the specified conditions. At this stage, basic property information (e.g., location, price, area, year built, etc.) is collected.

[0464] Step 3:

[0465] The server accesses external APIs and public databases to aggregate surrounding environment information for each acquired property. This information includes local safety conditions, nearest transportation options, and the presence of nearby commercial facilities and medical institutions. The server analyzes this data to evaluate whether each property has an optimal living environment.

[0466] Step 4:

[0467] The server assesses the natural disaster risk of each property based on its geographical location. It retrieves risk data for earthquakes, floods, tsunamis, etc., from a geographic database and applies it to the location of each property to perform a risk assessment. This information serves as a crucial evaluation criterion when providing information to users.

[0468] Step 5:

[0469] The server collects data from third-party review sites and user reviews to assess the reliability of construction and real estate companies offering properties. This data is then used to calculate a reliability score, which is incorporated into the scoring system as a key factor in property selection.

[0470] Step 6:

[0471] The server calculates an overall evaluation score for each property based on the collected information. Specifically, it integrates basic property information, assessment of the surrounding environment, natural disaster risk assessment, and reliability assessment to perform the scoring. This score quantifies how well the property matches the user's desired conditions.

[0472] Step 7:

[0473] The server ranks properties based on the calculated evaluation score and lists them in descending order of suitability. This ranked property list is then sent to the terminal and prepared for display to the user.

[0474] Step 8:

[0475] The terminal displays the received ranking information to the user. Based on the displayed information, the user can compare each property and view detailed information. The user can select the property that best suits their preferences, taking into account evaluation scores and various other information.

[0476] (Example 1)

[0477] 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."

[0478] In modern society, when buying or renting real estate, users need to select the optimal property from a vast amount of information. However, conventional methods place a heavy burden on users to collect and analyze information themselves, making it difficult to quickly find a property that meets their desired conditions. Furthermore, there is a lack of means to comprehensively judge the surrounding environment, disaster risk, and reliability of service providers of selected properties, which often leaves users feeling anxious. This invention aims to solve the above problems by providing a system that efficiently proposes properties that meet the user's desired conditions.

[0479] 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.

[0480] In this invention, the server includes means for retrieving information on real estate from an information storage device based on the user's desired conditions; means for integrating information on the surrounding environment, risk information regarding natural disasters, and credit information of real estate agents with the retrieved real estate information to generate an evaluation-based index; and means for ranking real estate based on the evaluation-based index and transmitting information to an information terminal device to recommend to the user. As a result, the user can quickly find a property that matches their desired conditions and make a decision while considering the surrounding environment, disaster risks, and the reliability of real estate agents.

[0481] An "information terminal device" is a device that has the function of allowing a user to input their desired conditions and sending that information to a server as communication data.

[0482] A "processing device" is a device that searches for information about real estate based on the user's desired conditions and performs calculations to integrate and evaluate the acquired information.

[0483] An "information storage device" is a storage device used to store information about the property being searched, as well as information about the surrounding environment and risk information.

[0484] An "evaluation-based index" is an index that shows the results of an evaluation that integrates surrounding environmental information, natural disaster risk, and the creditworthiness of the business operator, and presents them in a numerical form.

[0485] "Information regarding the surrounding environment" refers to information such as the safety and security of the area around the property, access to public transportation, and the presence or absence of commercial facilities.

[0486] "Risk information related to natural disasters" refers to information that indicates the risk in the event of natural disasters such as earthquakes and floods, and is calculated based on past disaster data and risk maps.

[0487] "Contractor credit information" refers to information indicating the reliability of construction companies and real estate agents providing properties, and is a score obtained based on third-party evaluation sites and user reviews.

[0488] "Recommended information" refers to information necessary for users to select a property, such as a list of properties ranked based on evaluation-based indicators.

[0489] This invention is an information processing system for efficiently suggesting properties that meet the user's desired housing conditions. The system mainly includes an information terminal device, a processing device, and an information storage device.

[0490] Users input their desired housing requirements using an information terminal device. The information terminal device formats this information as digital data and transmits it to a server via the internet. This transmitted data includes details such as the desired area, floor plan, budget, and distance from the nearest public transportation.

[0491] The server searches for real estate information stored in the information storage device and identifies properties that match the criteria entered by the user. The processing unit then uses this data to first collect information about the surrounding environment. This is done by using external public databases and APIs to obtain information on public safety, security, public transportation access, and commercial facilities.

[0492] Subsequently, the server evaluates risk information related to natural disasters. This risk assessment is performed by analyzing the risk of earthquakes and floods by referring to geographical data. This information is based on past disaster data and risk maps.

[0493] Furthermore, the server evaluates the reliability of property providers. It collects information from third-party review sites and user reviews, and calculates a reliability score using an AI model. This makes it possible to evaluate the risks and reliability associated with each property.

[0494] All aggregated information is calculated on the server as evaluation-based metrics, and properties are ranked based on their scores. Finally, the server sends the ranked property list to the information terminal device and presents it to the user. By referring to this list, users can easily choose a home that meets their needs.

[0495] As a concrete example, a user can input a prompt into the AI ​​model such as, "Please suggest the best property for me to find a safe 3LDK apartment in Tokyo for under 50 million yen." This process allows the user to find their ideal property without spending a lot of time.

[0496] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0497] Step 1:

[0498] The user inputs their desired housing conditions using an information terminal device. The input data includes desired area, floor plan, budget, and distance from the nearest station. The terminal receives this information, formats it as digital data, and sends it to the server. The input is the raw desired conditions, and the output is data in a digital format that the server can receive.

[0499] Step 2:

[0500] The server searches for real estate information stored in the information storage device. The input is a request to the server based on the user's desired conditions, generating a search query for the database. The database is queried, and data for properties matching the conditions is output. At this stage, a database management system is used to efficiently extract the information.

[0501] Step 3:

[0502] The server collects information about the surrounding environment based on the acquired property data. The input is property data, and requests are sent to external APIs and public databases. The acquired security information, transportation access information, and commercial facility information are integrated to generate surrounding environment information as output. Specifically, the server accesses RESTful APIs to obtain the necessary data and integrates it on the server.

[0503] Step 4:

[0504] The server assesses the natural disaster risk for each property. The input is the property's geographical location. The server uses this information to refer to a natural disaster database and evaluates the risk of earthquakes and floods as output. By performing data analysis using historical disaster data and risk maps, it calculates a risk score.

[0505] Step 5:

[0506] The server collects credit information on businesses and calculates a reliability score. The input is information on businesses providing properties, collected by scraping third-party review sites and user reviews. The output is a reliability score calculated for each business. A generative AI model is used to statistically assess the reliability of the collected information.

[0507] Step 6:

[0508] The server generates evaluation-based indices using surrounding environment information, natural disaster risk assessments, and reliability scores for each property. The inputs are the information obtained in the previous step, and the calculations are performed by weighting multiple evaluation criteria. The output is the scored evaluation index for each property, and a ranking score is generated.

[0509] Step 7:

[0510] The server ranks properties based on their evaluation scores. The input is all evaluation scores, and the system sorts the properties based on these scores. The output is a ranking list with numerical evaluations. An algorithm is used to determine the optimal property ranking.

[0511] Step 8:

[0512] The server sends a list of ranked properties to the information terminal device. The output is a list displayed in a user-viewable format, providing information to help the user make the best choice. Based on this information, the user can select the most suitable property.

[0513] (Application Example 1)

[0514] 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."

[0515] As cities develop, it is crucial for residents to find the optimal housing not only by providing property information, but also by utilizing real-time local infrastructure and environmental data. However, currently, there is no system in place to efficiently provide such comprehensive information. A mechanism is needed that leverages the framework of smart cities to rapidly collect and analyze diverse information relevant to housing selection.

[0516] 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.

[0517] In this invention, the server includes terminal means for inputting the user's desired conditions and transmitting the information to a central processing unit; means for acquiring regional infrastructure information from an external API in real time based on the conditions entered by the user and providing information to support the selection of the optimal property; and means for aggregating surrounding environment information, natural disaster risk information, and reliability information of service providers from the acquired asset information and calculating an evaluation index. As a result, users will be able to choose a more comfortable and safe home that takes into account the surrounding environment and disaster risks.

[0518] A "user" is someone who wishes to use an information processing system to find a place to live.

[0519] "Desired conditions" refer to the specific requests that users make when selecting a place to live, and include things like location, budget, and floor plan.

[0520] A "central processing unit" is a computer server that acquires, aggregates, and analyzes real estate information and other related information, and provides information that matches the user's desired conditions.

[0521] "Terminal device" refers to an electronic device used by a user to input desired conditions and transmit that information to a central processing unit, and includes smartphones, tablets, personal computers, etc.

[0522] "Asset information" refers to specific real estate property information, including location, price, floor plan, and detailed specifications.

[0523] "Information acquired in real time" refers to the latest regional infrastructure information and environmental data that are collected without time delay and reflect the current state.

[0524] "Surrounding environment information" refers to data on external factors that users should consider when choosing a residence, and includes information on public safety, transportation options, and commercial facilities.

[0525] "Natural disaster risk information" refers to information that assesses geological activity risks and flood risks based on past data and predictions.

[0526] "Service provider reliability information" refers to reliability evaluation data calculated based on indicators such as the creditworthiness of the organization providing the property and user reviews.

[0527] An "evaluation index" is a quantified evaluation result calculated based on acquired information, and serves as a standard to facilitate property selection.

[0528] In implementing this invention, desired conditions are entered from a terminal used by the user, and the information is transmitted to a central processing unit. This terminal is an electronic device such as a smartphone or tablet, and the desired conditions can be entered through a user interface.

[0529] The central processing unit retrieves asset information from the database based on the input information and, if necessary, collects real-time regional infrastructure information using external APIs. This process utilizes a cloud computing environment. Specifically, data aggregation and analysis are performed using Amazon Web Services and Google Cloud Platform.

[0530] The acquired asset information is further aggregated with information on the surrounding environment, natural disaster risks, and the reliability of service providers. Based on this aggregated information, evaluation indicators are calculated, and properties best suited to the user's needs are proposed.

[0531] The terminal displays evaluation results transmitted from the central processing unit, allowing users to view detailed information and compare options. This enables users to choose a home that takes the surrounding environment and safety into consideration.

[0532] For example, if a user enters the conditions "2LDK in Osaka City, within a 10-minute walk from the station, pet-friendly," the central processing unit searches for properties that match these conditions, analyzes transportation access information and local safety information, and suggests the most suitable property.

[0533] Examples of prompt statements for a generative AI model are as follows:

[0534] Based on the user's input criteria, search for properties in Osaka City and suggest the most suitable accommodations. Pay particular attention to transportation access information, surrounding facilities, and neighborhood safety. Pet-friendly properties will be given priority.

[0535] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0536] Step 1:

[0537] The user enters their desired conditions using a terminal. The entered conditions are specific requirements for selecting a residence (e.g., location, budget, floor plan, etc.), and this serves as the initial input for the system. This data is obtained from the terminal's user interface.

[0538] Step 2:

[0539] The terminal transmits the user's entered preferences to the central processing unit. Upon this transmission, the system begins preparing to search for property information based on those preferences. The data is transmitted to the server in a secure manner.

[0540] Step 3:

[0541] The server retrieves property information from the database based on the received request criteria. The retrieved information includes real estate details such as location, price, and floor plan. A database query is then generated to extract properties that match the criteria.

[0542] Step 4:

[0543] The server obtains real-time local infrastructure information via external APIs. This includes traffic conditions, surrounding facilities, and safety information, and the latest data is collected through integration with external systems. In this step, the information is kept up-to-date by obtaining data in real time.

[0544] Step 5:

[0545] The server aggregates the acquired asset information with surrounding environment information (security, transportation access, commercial facilities), natural disaster risk information (geological activity risk, flood risk), and reliability information of service providers, and calculates evaluation indicators. Here, the data is integrated and a comprehensive score is performed through an evaluation algorithm.

[0546] Step 6:

[0547] The server ranks properties based on evaluation metrics and sends the information, organized for easy viewing, to the user's terminal. The ranking process weights particularly important criteria to generate appropriate recommendations.

[0548] Step 7:

[0549] The evaluation results sent from the server are displayed on the user's device. The display is designed to make it easy for the user to compare property details, allowing them to choose the most suitable home. A visual user interface is used in this step.

[0550] 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.

[0551] One embodiment of the present invention is an information processing system for efficiently finding properties that meet the user's desired housing conditions, incorporating an emotion engine that recognizes the user's emotions. This system can collect, analyze, and evaluate optimal property information based on the user's desired conditions, and further make suggestions that take into account the user's emotional state.

[0552] First, the user uses a terminal to input their ideal housing requirements. These requirements include specific elements such as desired area, floor plan, budget, commute time, and school district. The terminal then sends the user's input information to the server.

[0553] The server efficiently retrieves properties that match the user's desired criteria from the real estate database. The retrieved property information includes not only basic property data but also information on the surrounding environment and natural disaster risk.

[0554] Next, the server uses an emotion engine to analyze the user's emotional state from their input data and past selection history. The emotion engine recognizes the user's emotional state at the time of input (e.g., excitement, confusion, satisfaction, etc.) and adjusts the property recommendations to meet the user's needs and expectations.

[0555] For example, if a user is experiencing stress while choosing a property, the emotion engine will recognize this emotion and adjust the amount and complexity of information presented to make the properties more intuitive and easier to understand. Similarly, if a user has expectations or a positive feeling towards a particular area, prioritizing the presentation of property information in that area can increase user satisfaction.

[0556] In this way, the server adjusts the order and content of property suggestions based on feedback from the emotion engine, ultimately generating an optimal property list. This list is sent to the terminal and displayed to the user. Based on the displayed information, the user can review the details and select the most suitable home.

[0557] This system allows users to not only receive property information but also personalized information tailored to their individual emotional state, which is expected to significantly improve the traditional property selection process.

[0558] The following describes the processing flow.

[0559] Step 1:

[0560] The user uses a terminal to input their desired housing conditions. These conditions include specific areas, floor plans, budget, walking distance from the station, and proximity to educational facilities. This sets the specific criteria the user is looking for. The terminal processes this information and sends it to the server.

[0561] Step 2:

[0562] The server creates a search query for the real estate database based on the user's requested criteria. This query is used to retrieve property information filtered by location, price, floor plan, etc. The server then uses the results to identify properties that match the user's needs.

[0563] Step 3:

[0564] The server then collects additional information on the surrounding environment and natural disaster risk for the acquired property. This information includes local safety, transportation access, and the presence of commercial facilities, while the natural disaster risk information includes data on earthquakes and floods. This information is obtained via public databases and external APIs.

[0565] Step 4:

[0566] The server takes in additional data entered from the terminal to understand the user's emotional state and analyzes it using an emotion engine. Based on the input information and past user selection history, the emotion engine determines the user's current emotion (e.g., excitement, tension, anxiety, etc.).

[0567] Step 5:

[0568] The server adjusts the property recommendations based on the analysis results of the emotion engine. If the user is feeling stressed, the recommendations are simplified and information is presented more intuitively; if the user is satisfied, detailed and comprehensive information is prioritized.

[0569] Step 6:

[0570] The server optimizes the ranking of properties based on the user's emotional state, performs scoring, and generates a ranking that aligns with the user's desired criteria. The generated ranking, reflecting the overall evaluation score of the properties, is then sent to the terminal.

[0571] Step 7:

[0572] The terminal displays a ranked list of properties to the user, showing each property's rating, detailed information, and images. Based on this information, the user can compare properties and choose the most suitable home.

[0573] Step 8:

[0574] Users review the presented information and select properties that interest them to further explore the details. Furthermore, they can complete their selection by being satisfied with the optimized suggestions provided by the emotion engine.

[0575] (Example 2)

[0576] 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."

[0577] Traditional real estate information systems had the problem of placing an excessive burden on users and lowering their satisfaction by mechanically presenting property information without considering the emotional stress or personal expectations of the users. Furthermore, the effort required to provide property information tailored to each user's preferences and desires created a need for improved user experience.

[0578] 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.

[0579] In this invention, the server includes an input means for inputting the user's desired conditions and transmitting the information, a processing means for acquiring asset information based on the user's desired conditions, and an adjustment means using a generative AI model that evaluates the acquired asset information and adjusts the proposals. This makes it possible to provide customized property information that takes the user's emotions into consideration.

[0580] An "input means" is a means for a user to input their desired housing conditions into an information processing device and transmit this information.

[0581] "Processing means" refers to means for obtaining appropriate asset information from a recording device based on the user's desired conditions.

[0582] "Analysis means" refers to a method for aggregating environmental information, geographical risk information, and provider reliability information from acquired asset information and calculating evaluation indicators.

[0583] A "generative AI model" is a model that utilizes artificial intelligence technology to analyze the emotional state of users and generate property information that has been adjusted accordingly.

[0584] "Adjustment methods" refer to means for customizing and optimizing the order and content of property suggestions based on feedback from the generating AI model, according to the user's emotional state.

[0585] "Transmission means" refers to the means of ultimately transmitting the adjusted property information to an information processing device and presenting it to the user.

[0586] This invention is a system that allows users to input their desired housing conditions and provides property information that takes their emotions into consideration. The system mainly consists of a terminal, a server, and a generative AI model working together.

[0587] First, the user uses a device to input their desired housing conditions. This includes location, floor plan, budget, and commute time. The device then sends this information to the server as formatted data.

[0588] The server uses processing tools to retrieve relevant asset information from the recording device based on the received data. SQL database technology is utilized for this information collection. Various real estate-related data are structured as asset information and are efficiently retrieved from the appropriate database as needed.

[0589] The acquired asset information is aggregated through analytical methods, along with environmental information, geographical risk information, and provider reliability information. This enables a detailed evaluation of the user's desired conditions. Specific examples include natural disaster risk and security information for the area where the property is located.

[0590] Next, the server uses a generative AI model to analyze the user's input data and past behavioral history. The generative AI model uses natural language processing (NLP) techniques to identify the user's emotional state. This emotional data is used in adjustments to optimize the priority and content of property recommendations for the user.

[0591] Finally, the adjusted property information is transmitted to the terminal via a transmission method and displayed on the user's screen. At this point, the user can check the property details based on the displayed information and contact the real estate agent for any properties that interest them further.

[0592] An example of a prompt message is: "Collect information on 3LDK apartments in nature-rich areas within a 30-minute radius from the real estate database, and set the priority order while considering the user's sentiment." Instructions can be given to the generating AI model in this format.

[0593] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0594] Step 1:

[0595] The user uses a terminal to enter their desired housing conditions. Input fields include location, floor plan, budget, and commute time. This input data is converted to JSON format and sent to the server. The terminal validates the user's input, formats it for accuracy, and prepares it for the next processing step.

[0596] Step 2:

[0597] The server retrieves relevant asset information from the recording device based on the user's desired conditions received from the terminal. Specifically, the server executes a query on the SQL database and extracts real estate data that matches the conditions. The input is an SQL query with a conditional expression, and the output is asset information that matches the conditions.

[0598] Step 3:

[0599] The server aggregates the acquired asset information using analytical tools. It combines environmental information, geographical risk information, and "provider reliability information" to calculate a comprehensive evaluation score. The data processing performed here involves calculation and integration of data from various sources. The input is asset information, and the output is a composite evaluation score.

[0600] Step 4:

[0601] The server analyzes the user's emotional state using a generative AI model. It extracts emotional patterns from the user's past behavior history and current input data using natural language processing techniques. This identifies the user's current emotional state. Input consists of past behavior data and current input data, while output is the result of the emotional state analysis.

[0602] Step 5:

[0603] The server uses adjustment mechanisms to customize the priority and content of property suggestions based on the user's emotional state. It implements adaptive rankings, incorporating feedback from the generating AI model. Input is the analysis results and evaluation scores of the emotional state, and output is the adjusted property list.

[0604] Step 6:

[0605] The server transmits the adjusted property information to the terminal via a transmission method. The terminal displays an optimized property list to the user. Based on this information, the user can perform detailed checks and make further decisions. The input is the adjusted property list, and the output is the information displayed to the user.

[0606] (Application Example 2)

[0607] 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."

[0608] Traditional real estate information systems allowed users to search for properties based on their desired criteria, but they lacked the ability to suggest properties that took into account the user's emotional state. As a result, users sometimes experienced stress when choosing a property, or did not receive suggestions that adequately met their expectations, making it necessary to improve user satisfaction.

[0609] 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.

[0610] In this invention, the server includes means for using a data device to input the user's desired conditions and transmit the information to the server; means for using an information processing device to acquire property information from a recording medium based on the user's desired conditions; and means for using an information processing device to aggregate surrounding environment information, natural disaster risk information, and reliability information of the providing organization for the acquired property information and calculate an evaluation value. This makes it possible to propose properties that are more accurate and satisfying, taking into account the user's emotional state.

[0611] A "user" is the entity that searches for and selects property information.

[0612] "Desired conditions" refer to the specific criteria and requests that the user has regarding their ideal property.

[0613] A "data device" is a terminal or device used to process user input information and transmit it to a server.

[0614] An "information processing device" is a computer system that combines acquired property information with surrounding environment information and natural disaster risk information to calculate evaluation values.

[0615] "Recording medium" refers to a database or storage device where property information is stored.

[0616] "Surrounding environment information" refers to information including safety information, transportation information, and commercial location information regarding the area surrounding the property in question.

[0617] "Natural disaster risk information" refers to information about the risk of disasters related to a property, based on earthquake risk data and flood risk data.

[0618] A "provider" refers to an organization or group that provides services related to property information and its evaluation.

[0619] An "evaluation score" is a score calculated based on property information, and it is a numerical value that indicates the value and suitability of the property.

[0620] An "emotion recognition device" is a tool or system that analyzes a user's emotional state and incorporates that information into property recommendations.

[0621] This invention provides a real estate information system that allows users to efficiently find the optimal property that meets their desired conditions, while also providing a function that takes into account the user's emotions. This system consists of multiple information processing devices, each of which plays a specific role.

[0622] Users input their desired criteria using a data device. This data device functions as a terminal that collects information such as the region, floor plan, and budget specified by the user and transmits it to a server. Examples of such devices include smartphones and tablets.

[0623] The server uses an information processing device to retrieve appropriate property information from the storage medium based on the received desired conditions. This retrieval process can utilize a database running on the Google Cloud Platform.

[0624] Furthermore, the server aggregates information on the surrounding environment, safety, transportation, natural disaster risk (including earthquake and flood risk data), and the reliability of the providing organizations. Based on this information, it calculates evaluation scores and ranks properties. This allows the user to receive property recommendations that are most suitable for them.

[0625] Furthermore, the emotion recognition device has the capability to analyze the user's emotional state in real time using NVIDIA Jetson. Once the user's emotions are recognized, the property suggestions are adjusted based on that feedback. For example, if the user is excited about the property search, the suggestions are broadened and new options are offered, while if they are feeling stressed, the options are narrowed down.

[0626] For example, if a user uses this system in the evening when they are a little tired, the emotion recognition device will detect this state and prioritize presenting properties that promote relaxation. Another example of a prompt using the generative AI model is, "When the user is feeling stressed, reduce the number of property options and explain them in simpler terms."

[0627] By operating such a system, users can find properties that are more satisfying than ever before.

[0628] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0629] Step 1:

[0630] The user uses a data device to enter their desired conditions. The entered data includes information such as region, floor plan, and budget. Once this data is entered and the user presses the submit button, the data is sent to the server.

[0631] Step 2:

[0632] The server retrieves property information from the storage medium based on the user's requested criteria. This storage medium includes databases and external APIs. The server searches for property information that matches the criteria and generates a list of properties that satisfy the conditions. This output becomes the input for the next process.

[0633] Step 3:

[0634] The server aggregates property information, surrounding environment information, natural disaster risk information, and reliability information of the providing organization, and calculates an evaluation score by weighting each element. The data used here is obtained from existing open data and specific APIs. The calculation is performed based on an algorithm for calculating the evaluation score.

[0635] Step 4:

[0636] Based on the evaluation scores, the server ranks the properties and sends the information to the data device for suggestion to the user. The transmitted information is displayed as a list on the user's terminal, providing the user with a visually intuitive selection. This output serves as a foundation to support the user's decision-making.

[0637] Step 5:

[0638] The server uses an emotion recognition device to analyze the user's emotional state in real time at the time of input. Based on the change in emotion, it adjusts the suggested properties using a generative AI model. Specifically, if the user is stressed, it provides simpler suggestions; if they are excited, it presents more options. An example of a prompt message would be, "When the user is stressed, reduce the number of property options and explain them in simpler terms."

[0639] Step 6:

[0640] The terminal visually displays the adjusted property suggestions received from the server to the user. The user then reviews the details and selects the most suitable home. This final output is a crucial element in enabling the user to confidently make the best decision.

[0641] 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.

[0642] 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.

[0643] 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.

[0644] [Fourth Embodiment]

[0645] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0646] 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.

[0647] 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).

[0648] 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.

[0649] 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.

[0650] 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).

[0651] 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.

[0652] 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.

[0653] 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.

[0654] 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.

[0655] 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.

[0656] 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.

[0657] 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".

[0658] One embodiment of the present invention is to provide an information processing system for efficiently finding properties that meet the user's desired housing conditions. This system collects, analyzes, and evaluates appropriate real estate information based on the user's desired conditions and proposes the optimal option.

[0659] First, the user uses their device to enter their desired housing conditions. Specifically, these conditions include the desired location, floor plan, budget, and walking distance from the nearest station. This information is received by the device and sent to the server.

[0660] The server retrieves property data that matches the user's criteria from the real estate database. The database filters properties based on criteria such as location, price range, and floor plan.

[0661] Next, the server investigates the surrounding environment information for the acquired property information. It obtains and analyzes information including safety information, transportation access, and the presence of commercial facilities from external APIs and public databases.

[0662] Furthermore, the server evaluates data on natural disaster risks (earthquake risk, flood risk, etc.). Based on geographical information, it refers to past disaster data and risk maps for the relevant area to perform a risk assessment for each property.

[0663] At the same time, to evaluate the reliability of the construction and real estate companies providing the properties, the server calculates a reliability score based on third-party review sites and user reviews.

[0664] Based on the aggregated information described above, the server calculates an overall evaluation score for each property and determines its suitability to the user's desired conditions. The evaluation scores are quantified and ranked to allow for easy comparison of each property.

[0665] The server then ranks the properties based on their evaluation scores and creates a list of the best properties. This information is sent to the terminal and displayed to the user. The user can then compare evaluation comments and detailed information from the presented properties and select a suitable place to live.

[0666] For example, if a user is looking for a 3LDK apartment in Tokyo priced under 50 million yen, the system will search for suitable properties and recommend those with good surrounding environments and low risk of natural disasters as top-ranked options. This process allows users to find their ideal home in a short amount of time.

[0667] The following describes the processing flow.

[0668] Step 1:

[0669] Users enter their desired housing requirements through a dedicated application or web interface. These requirements include preferred area, floor plan, budget, walking distance from the nearest station, and educational environment for children. The information entered by the user is collected as data by the terminal and sent to the server.

[0670] Step 2:

[0671] The server queries the real estate information database based on the user's requested conditions. It uses queries such as SQL to retrieve property information that matches the specified conditions. At this stage, basic property information (e.g., location, price, area, year built, etc.) is collected.

[0672] Step 3:

[0673] The server accesses external APIs and public databases to aggregate surrounding environment information for each acquired property. This information includes local safety conditions, nearest transportation options, and the presence of nearby commercial facilities and medical institutions. The server analyzes this data to evaluate whether each property has an optimal living environment.

[0674] Step 4:

[0675] The server assesses the natural disaster risk of each property based on its geographical location. It retrieves risk data for earthquakes, floods, tsunamis, etc., from a geographic database and applies it to the location of each property to perform a risk assessment. This information serves as a crucial evaluation criterion when providing information to users.

[0676] Step 5:

[0677] The server collects data from third-party review sites and user reviews to assess the reliability of construction and real estate companies offering properties. This data is then used to calculate a reliability score, which is incorporated into the scoring system as a key factor in property selection.

[0678] Step 6:

[0679] The server calculates an overall evaluation score for each property based on the collected information. Specifically, it integrates basic property information, assessment of the surrounding environment, natural disaster risk assessment, and reliability assessment to perform the scoring. This score quantifies how well the property matches the user's desired conditions.

[0680] Step 7:

[0681] The server ranks properties based on the calculated evaluation score and lists them in descending order of suitability. This ranked property list is then sent to the terminal and prepared for display to the user.

[0682] Step 8:

[0683] The terminal displays the received ranking information to the user. Based on the displayed information, the user can compare each property and view detailed information. The user can select the property that best suits their preferences, taking into account evaluation scores and various other information.

[0684] (Example 1)

[0685] 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".

[0686] In modern society, when buying or renting real estate, users need to select the optimal property from a vast amount of information. However, conventional methods place a heavy burden on users to collect and analyze information themselves, making it difficult to quickly find a property that meets their desired conditions. Furthermore, there is a lack of means to comprehensively judge the surrounding environment, disaster risk, and reliability of service providers of selected properties, which often leaves users feeling anxious. This invention aims to solve the above problems by providing a system that efficiently proposes properties that meet the user's desired conditions.

[0687] 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.

[0688] In this invention, the server includes means for retrieving information on real estate from an information storage device based on the user's desired conditions; means for integrating information on the surrounding environment, risk information regarding natural disasters, and credit information of real estate agents with the retrieved real estate information to generate an evaluation-based index; and means for ranking real estate based on the evaluation-based index and transmitting information to an information terminal device to recommend to the user. As a result, the user can quickly find a property that matches their desired conditions and make a decision while considering the surrounding environment, disaster risks, and the reliability of real estate agents.

[0689] An "information terminal device" is a device that has the function of allowing a user to input their desired conditions and sending that information to a server as communication data.

[0690] A "processing device" is a device that searches for information about real estate based on the user's desired conditions and performs calculations to integrate and evaluate the acquired information.

[0691] An "information storage device" is a storage device used to store information about the property being searched, as well as information about the surrounding environment and risk information.

[0692] An "evaluation-based index" is an index that shows the results of an evaluation that integrates surrounding environmental information, natural disaster risk, and the creditworthiness of the business operator, and presents them in a numerical form.

[0693] "Information regarding the surrounding environment" refers to information such as the safety and security of the area around the property, access to public transportation, and the presence or absence of commercial facilities.

[0694] "Risk information related to natural disasters" refers to information that indicates the risk in the event of natural disasters such as earthquakes and floods, and is calculated based on past disaster data and risk maps.

[0695] "Contractor credit information" refers to information indicating the reliability of construction companies and real estate agents providing properties, and is a score obtained based on third-party evaluation sites and user reviews.

[0696] "Recommended information" refers to information necessary for users to select a property, such as a list of properties ranked based on evaluation-based indicators.

[0697] This invention is an information processing system for efficiently suggesting properties that meet the user's desired housing conditions. The system mainly includes an information terminal device, a processing device, and an information storage device.

[0698] Users input their desired housing requirements using an information terminal device. The information terminal device formats this information as digital data and transmits it to a server via the internet. This transmitted data includes details such as the desired area, floor plan, budget, and distance from the nearest public transportation.

[0699] The server searches for real estate information stored in the information storage device and identifies properties that match the criteria entered by the user. The processing unit then uses this data to first collect information about the surrounding environment. This is done by using external public databases and APIs to obtain information on public safety, security, public transportation access, and commercial facilities.

[0700] Subsequently, the server evaluates risk information related to natural disasters. This risk assessment is performed by analyzing the risk of earthquakes and floods by referring to geographical data. This information is based on past disaster data and risk maps.

[0701] Furthermore, the server evaluates the reliability of property providers. It collects information from third-party review sites and user reviews, and calculates a reliability score using an AI model. This makes it possible to evaluate the risks and reliability associated with each property.

[0702] All aggregated information is calculated on the server as evaluation-based metrics, and properties are ranked based on their scores. Finally, the server sends the ranked property list to the information terminal device and presents it to the user. By referring to this list, users can easily choose a home that meets their needs.

[0703] As a concrete example, a user can input a prompt into the AI ​​model such as, "Please suggest the best property for me to find a safe 3LDK apartment in Tokyo for under 50 million yen." This process allows the user to find their ideal property without spending a lot of time.

[0704] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0705] Step 1:

[0706] The user inputs their desired housing conditions using an information terminal device. The input data includes desired area, floor plan, budget, and distance from the nearest station. The terminal receives this information, formats it as digital data, and sends it to the server. The input is the raw desired conditions, and the output is data in a digital format that the server can receive.

[0707] Step 2:

[0708] The server searches for real estate information stored in the information storage device. The input is a request to the server based on the user's desired conditions, generating a search query for the database. The database is queried, and data for properties matching the conditions is output. At this stage, a database management system is used to efficiently extract the information.

[0709] Step 3:

[0710] The server collects information about the surrounding environment based on the acquired property data. The input is property data, and requests are sent to external APIs and public databases. The acquired security information, transportation access information, and commercial facility information are integrated to generate surrounding environment information as output. Specifically, the server accesses RESTful APIs to obtain the necessary data and integrates it on the server.

[0711] Step 4:

[0712] The server assesses the natural disaster risk for each property. The input is the property's geographical location. The server uses this information to refer to a natural disaster database and evaluates the risk of earthquakes and floods as output. By performing data analysis using historical disaster data and risk maps, it calculates a risk score.

[0713] Step 5:

[0714] The server collects credit information on businesses and calculates a reliability score. The input is information on businesses providing properties, collected by scraping third-party review sites and user reviews. The output is a reliability score calculated for each business. A generative AI model is used to statistically assess the reliability of the collected information.

[0715] Step 6:

[0716] The server generates evaluation-based indices using surrounding environment information, natural disaster risk assessments, and reliability scores for each property. The inputs are the information obtained in the previous step, and the calculations are performed by weighting multiple evaluation criteria. The output is the scored evaluation index for each property, and a ranking score is generated.

[0717] Step 7:

[0718] The server ranks properties based on their evaluation scores. The input is all evaluation scores, and the system sorts the properties based on these scores. The output is a ranking list with numerical evaluations. An algorithm is used to determine the optimal property ranking.

[0719] Step 8:

[0720] The server sends a list of ranked properties to the information terminal device. The output is a list displayed in a user-viewable format, providing information to help the user make the best choice. Based on this information, the user can select the most suitable property.

[0721] (Application Example 1)

[0722] 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".

[0723] As cities develop, it is crucial for residents to find the optimal housing not only by providing property information, but also by utilizing real-time local infrastructure and environmental data. However, currently, there is no system in place to efficiently provide such comprehensive information. A mechanism is needed that leverages the framework of smart cities to rapidly collect and analyze diverse information relevant to housing selection.

[0724] 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.

[0725] In this invention, the server includes terminal means for inputting the user's desired conditions and transmitting the information to a central processing unit; means for acquiring regional infrastructure information from an external API in real time based on the conditions entered by the user and providing information to support the selection of the optimal property; and means for aggregating surrounding environment information, natural disaster risk information, and reliability information of service providers from the acquired asset information and calculating an evaluation index. As a result, users will be able to choose a more comfortable and safe home that takes into account the surrounding environment and disaster risks.

[0726] A "user" is someone who wishes to use an information processing system to find a place to live.

[0727] "Desired conditions" refer to the specific requests that users make when selecting a place to live, and include things like location, budget, and floor plan.

[0728] A "central processing unit" is a computer server that acquires, aggregates, and analyzes real estate information and other related information, and provides information that matches the user's desired conditions.

[0729] "Terminal device" refers to an electronic device used by a user to input desired conditions and transmit that information to a central processing unit, and includes smartphones, tablets, personal computers, etc.

[0730] "Asset information" refers to specific real estate property information, including location, price, floor plan, and detailed specifications.

[0731] "Information acquired in real time" refers to the latest regional infrastructure information and environmental data that are collected without time delay and reflect the current state.

[0732] "Surrounding environment information" refers to data on external factors that users should consider when choosing a residence, and includes information on public safety, transportation options, and commercial facilities.

[0733] "Natural disaster risk information" refers to information that assesses geological activity risks and flood risks based on past data and predictions.

[0734] "Service provider reliability information" refers to reliability evaluation data calculated based on indicators such as the creditworthiness of the organization providing the property and user reviews.

[0735] An "evaluation index" is a quantified evaluation result calculated based on acquired information, and serves as a standard to facilitate property selection.

[0736] In implementing this invention, desired conditions are entered from a terminal used by the user, and the information is transmitted to a central processing unit. This terminal is an electronic device such as a smartphone or tablet, and the desired conditions can be entered through a user interface.

[0737] The central processing unit retrieves asset information from the database based on the input information and, if necessary, collects real-time regional infrastructure information using external APIs. This process utilizes a cloud computing environment. Specifically, data aggregation and analysis are performed using Amazon Web Services and Google Cloud Platform.

[0738] The acquired asset information is further aggregated with information on the surrounding environment, natural disaster risks, and the reliability of service providers. Based on this aggregated information, evaluation indicators are calculated, and properties best suited to the user's needs are proposed.

[0739] The terminal displays evaluation results transmitted from the central processing unit, allowing users to view detailed information and compare options. This enables users to choose a home that takes the surrounding environment and safety into consideration.

[0740] For example, if a user enters the conditions "2LDK in Osaka City, within a 10-minute walk from the station, pet-friendly," the central processing unit searches for properties that match these conditions, analyzes transportation access information and local safety information, and suggests the most suitable property.

[0741] Examples of prompt statements for a generative AI model are as follows:

[0742] Based on the user's input criteria, search for properties in Osaka City and suggest the most suitable accommodations. Pay particular attention to transportation access information, surrounding facilities, and neighborhood safety. Pet-friendly properties will be given priority.

[0743] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0744] Step 1:

[0745] The user enters their desired conditions using a terminal. The entered conditions are specific requirements for selecting a residence (e.g., location, budget, floor plan, etc.), and this serves as the initial input for the system. This data is obtained from the terminal's user interface.

[0746] Step 2:

[0747] The terminal transmits the user's entered preferences to the central processing unit. Upon this transmission, the system begins preparing to search for property information based on those preferences. The data is transmitted to the server in a secure manner.

[0748] Step 3:

[0749] The server retrieves property information from the database based on the received request criteria. The retrieved information includes real estate details such as location, price, and floor plan. A database query is then generated to extract properties that match the criteria.

[0750] Step 4:

[0751] The server obtains real-time local infrastructure information via external APIs. This includes traffic conditions, surrounding facilities, and safety information, and the latest data is collected through integration with external systems. In this step, the information is kept up-to-date by obtaining data in real time.

[0752] Step 5:

[0753] The server aggregates the acquired asset information with surrounding environment information (security, transportation access, commercial facilities), natural disaster risk information (geological activity risk, flood risk), and reliability information of service providers, and calculates evaluation indicators. Here, the data is integrated and a comprehensive score is performed through an evaluation algorithm.

[0754] Step 6:

[0755] The server ranks properties based on evaluation metrics and sends the information, organized for easy viewing, to the user's terminal. The ranking process weights particularly important criteria to generate appropriate recommendations.

[0756] Step 7:

[0757] The evaluation results sent from the server are displayed on the user's device. The display is designed to make it easy for the user to compare property details, allowing them to choose the most suitable home. A visual user interface is used in this step.

[0758] 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.

[0759] One embodiment of the present invention is an information processing system for efficiently finding properties that meet the user's desired housing conditions, incorporating an emotion engine that recognizes the user's emotions. This system can collect, analyze, and evaluate optimal property information based on the user's desired conditions, and further make suggestions that take into account the user's emotional state.

[0760] First, the user uses a terminal to input their ideal housing requirements. These requirements include specific elements such as desired area, floor plan, budget, commute time, and school district. The terminal then sends the user's input information to the server.

[0761] The server efficiently retrieves properties that match the user's desired criteria from the real estate database. The retrieved property information includes not only basic property data but also information on the surrounding environment and natural disaster risk.

[0762] Next, the server uses an emotion engine to analyze the user's emotional state from their input data and past selection history. The emotion engine recognizes the user's emotional state at the time of input (e.g., excitement, confusion, satisfaction, etc.) and adjusts the property recommendations to meet the user's needs and expectations.

[0763] For example, if a user is experiencing stress while choosing a property, the emotion engine will recognize this emotion and adjust the amount and complexity of information presented to make the properties more intuitive and easier to understand. Similarly, if a user has expectations or a positive feeling towards a particular area, prioritizing the presentation of property information in that area can increase user satisfaction.

[0764] In this way, the server adjusts the order and content of property suggestions based on feedback from the emotion engine, ultimately generating an optimal property list. This list is sent to the terminal and displayed to the user. Based on the displayed information, the user can review the details and select the most suitable home.

[0765] This system allows users to not only receive property information but also personalized information tailored to their individual emotional state, which is expected to significantly improve the traditional property selection process.

[0766] The following describes the processing flow.

[0767] Step 1:

[0768] The user uses a terminal to input their desired housing conditions. These conditions include specific areas, floor plans, budget, walking distance from the station, and proximity to educational facilities. This sets the specific criteria the user is looking for. The terminal processes this information and sends it to the server.

[0769] Step 2:

[0770] The server creates a search query for the real estate database based on the user's requested criteria. This query is used to retrieve property information filtered by location, price, floor plan, etc. The server then uses the results to identify properties that match the user's needs.

[0771] Step 3:

[0772] The server then collects additional information on the surrounding environment and natural disaster risk for the acquired property. This information includes local safety, transportation access, and the presence of commercial facilities, while the natural disaster risk information includes data on earthquakes and floods. This information is obtained via public databases and external APIs.

[0773] Step 4:

[0774] The server takes in additional data entered from the terminal to understand the user's emotional state and analyzes it using an emotion engine. Based on the input information and past user selection history, the emotion engine determines the user's current emotion (e.g., excitement, tension, anxiety, etc.).

[0775] Step 5:

[0776] The server adjusts the property recommendations based on the analysis results of the emotion engine. If the user is feeling stressed, the recommendations are simplified and information is presented more intuitively; if the user is satisfied, detailed and comprehensive information is prioritized.

[0777] Step 6:

[0778] The server optimizes the ranking of properties based on the user's emotional state, performs scoring, and generates a ranking that aligns with the user's desired criteria. The generated ranking, reflecting the overall evaluation score of the properties, is then sent to the terminal.

[0779] Step 7:

[0780] The terminal displays a ranked list of properties to the user, showing each property's rating, detailed information, and images. Based on this information, the user can compare properties and choose the most suitable home.

[0781] Step 8:

[0782] Users review the presented information and select properties that interest them to further explore the details. Furthermore, they can complete their selection by being satisfied with the optimized suggestions provided by the emotion engine.

[0783] (Example 2)

[0784] 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".

[0785] Traditional real estate information systems had the problem of placing an excessive burden on users and lowering their satisfaction by mechanically presenting property information without considering the emotional stress or personal expectations of the users. Furthermore, the effort required to provide property information tailored to each user's preferences and desires created a need for improved user experience.

[0786] 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.

[0787] In this invention, the server includes an input means for inputting the user's desired conditions and transmitting the information, a processing means for acquiring asset information based on the user's desired conditions, and an adjustment means using a generative AI model that evaluates the acquired asset information and adjusts the proposals. This makes it possible to provide customized property information that takes the user's emotions into consideration.

[0788] An "input means" is a means for a user to input their desired housing conditions into an information processing device and transmit this information.

[0789] "Processing means" refers to means for obtaining appropriate asset information from a recording device based on the user's desired conditions.

[0790] "Analysis means" refers to a method for aggregating environmental information, geographical risk information, and provider reliability information from acquired asset information and calculating evaluation indicators.

[0791] A "generative AI model" is a model that utilizes artificial intelligence technology to analyze the emotional state of users and generate property information that has been adjusted accordingly.

[0792] "Adjustment methods" refer to means for customizing and optimizing the order and content of property suggestions based on feedback from the generating AI model, according to the user's emotional state.

[0793] "Transmission means" refers to the means of ultimately transmitting the adjusted property information to an information processing device and presenting it to the user.

[0794] This invention is a system that allows users to input their desired housing conditions and provides property information that takes their emotions into consideration. The system mainly consists of a terminal, a server, and a generative AI model working together.

[0795] First, the user uses a device to input their desired housing conditions. This includes location, floor plan, budget, and commute time. The device then sends this information to the server as formatted data.

[0796] The server uses processing tools to retrieve relevant asset information from the recording device based on the received data. SQL database technology is utilized for this information collection. Various real estate-related data are structured as asset information and are efficiently retrieved from the appropriate database as needed.

[0797] The acquired asset information is aggregated through analytical methods, along with environmental information, geographical risk information, and provider reliability information. This enables a detailed evaluation of the user's desired conditions. Specific examples include natural disaster risk and security information for the area where the property is located.

[0798] Next, the server uses a generative AI model to analyze the user's input data and past behavioral history. The generative AI model uses natural language processing (NLP) techniques to identify the user's emotional state. This emotional data is used in adjustments to optimize the priority and content of property recommendations for the user.

[0799] Finally, the adjusted property information is transmitted to the terminal via a transmission method and displayed on the user's screen. At this point, the user can check the property details based on the displayed information and contact the real estate agent for any properties that interest them further.

[0800] An example of a prompt message is: "Collect information on 3LDK apartments in nature-rich areas within a 30-minute radius from the real estate database, and set the priority order while considering the user's sentiment." Instructions can be given to the generating AI model in this format.

[0801] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0802] Step 1:

[0803] The user uses a terminal to enter their desired housing conditions. Input fields include location, floor plan, budget, and commute time. This input data is converted to JSON format and sent to the server. The terminal validates the user's input, formats it for accuracy, and prepares it for the next processing step.

[0804] Step 2:

[0805] The server retrieves relevant asset information from the recording device based on the user's desired conditions received from the terminal. Specifically, the server executes a query on the SQL database and extracts real estate data that matches the conditions. The input is an SQL query with a conditional expression, and the output is asset information that matches the conditions.

[0806] Step 3:

[0807] The server aggregates the acquired asset information using analytical tools. It combines environmental information, geographical risk information, and "provider reliability information" to calculate a comprehensive evaluation score. The data processing performed here involves calculation and integration of data from various sources. The input is asset information, and the output is a composite evaluation score.

[0808] Step 4:

[0809] The server analyzes the user's emotional state using a generative AI model. It extracts emotional patterns from the user's past behavior history and current input data using natural language processing techniques. This identifies the user's current emotional state. Input consists of past behavior data and current input data, while output is the result of the emotional state analysis.

[0810] Step 5:

[0811] The server uses adjustment mechanisms to customize the priority and content of property suggestions based on the user's emotional state. It implements adaptive rankings, incorporating feedback from the generating AI model. Input is the analysis results and evaluation scores of the emotional state, and output is the adjusted property list.

[0812] Step 6:

[0813] The server transmits the adjusted property information to the terminal via a transmission method. The terminal displays an optimized property list to the user. Based on this information, the user can perform detailed checks and make further decisions. The input is the adjusted property list, and the output is the information displayed to the user.

[0814] (Application Example 2)

[0815] 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".

[0816] Traditional real estate information systems allowed users to search for properties based on their desired criteria, but they lacked the ability to suggest properties that took into account the user's emotional state. As a result, users sometimes experienced stress when choosing a property, or did not receive suggestions that adequately met their expectations, making it necessary to improve user satisfaction.

[0817] 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.

[0818] In this invention, the server includes means for using a data device to input the user's desired conditions and transmit the information to the server; means for using an information processing device to acquire property information from a recording medium based on the user's desired conditions; and means for using an information processing device to aggregate surrounding environment information, natural disaster risk information, and reliability information of the providing organization for the acquired property information and calculate an evaluation value. This makes it possible to propose properties that are more accurate and satisfying, taking into account the user's emotional state.

[0819] A "user" is the entity that searches for and selects property information.

[0820] "Desired conditions" refer to the specific criteria and requests that the user has regarding their ideal property.

[0821] A "data device" is a terminal or device used to process user input information and transmit it to a server.

[0822] An "information processing device" is a computer system that combines acquired property information with surrounding environment information and natural disaster risk information to calculate evaluation values.

[0823] "Recording medium" refers to a database or storage device where property information is stored.

[0824] "Surrounding environment information" refers to information including safety information, transportation information, and commercial location information regarding the area surrounding the property in question.

[0825] "Natural disaster risk information" refers to information about the risk of disasters related to a property, based on earthquake risk data and flood risk data.

[0826] A "provider" refers to an organization or group that provides services related to property information and its evaluation.

[0827] An "evaluation score" is a score calculated based on property information, and it is a numerical value that indicates the value and suitability of the property.

[0828] An "emotion recognition device" is a tool or system that analyzes a user's emotional state and incorporates that information into property recommendations.

[0829] This invention provides a real estate information system that allows users to efficiently find the optimal property that meets their desired conditions, while also providing a function that takes into account the user's emotions. This system consists of multiple information processing devices, each of which plays a specific role.

[0830] Users input their desired criteria using a data device. This data device functions as a terminal that collects information such as the region, floor plan, and budget specified by the user and transmits it to a server. Examples of such devices include smartphones and tablets.

[0831] The server uses an information processing device to retrieve appropriate property information from the storage medium based on the received desired conditions. This retrieval process can utilize a database running on the Google Cloud Platform.

[0832] Furthermore, the server aggregates information on the surrounding environment, safety, transportation, natural disaster risk (including earthquake and flood risk data), and the reliability of the providing organizations. Based on this information, it calculates evaluation scores and ranks properties. This allows the user to receive property recommendations that are most suitable for them.

[0833] Furthermore, the emotion recognition device has the capability to analyze the user's emotional state in real time using NVIDIA Jetson. Once the user's emotions are recognized, the property suggestions are adjusted based on that feedback. For example, if the user is excited about the property search, the suggestions are broadened and new options are offered, while if they are feeling stressed, the options are narrowed down.

[0834] For example, if a user uses this system in the evening when they are a little tired, the emotion recognition device will detect this state and prioritize presenting properties that promote relaxation. Another example of a prompt using the generative AI model is, "When the user is feeling stressed, reduce the number of property options and explain them in simpler terms."

[0835] By operating such a system, users can find properties that are more satisfying than ever before.

[0836] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0837] Step 1:

[0838] The user uses a data device to enter their desired conditions. The entered data includes information such as region, floor plan, and budget. Once this data is entered and the user presses the submit button, the data is sent to the server.

[0839] Step 2:

[0840] The server retrieves property information from the storage medium based on the user's requested criteria. This storage medium includes databases and external APIs. The server searches for property information that matches the criteria and generates a list of properties that satisfy the conditions. This output becomes the input for the next process.

[0841] Step 3:

[0842] The server aggregates property information, surrounding environment information, natural disaster risk information, and reliability information of the providing organization, and calculates an evaluation score by weighting each element. The data used here is obtained from existing open data and specific APIs. The calculation is performed based on an algorithm for calculating the evaluation score.

[0843] Step 4:

[0844] Based on the evaluation scores, the server ranks the properties and sends the information to the data device for suggestion to the user. The transmitted information is displayed as a list on the user's terminal, providing the user with a visually intuitive selection. This output serves as a foundation to support the user's decision-making.

[0845] Step 5:

[0846] The server uses an emotion recognition device to analyze the user's emotional state in real time at the time of input. Based on the change in emotion, it adjusts the suggested properties using a generative AI model. Specifically, if the user is stressed, it provides simpler suggestions; if they are excited, it presents more options. An example of a prompt message would be, "When the user is stressed, reduce the number of property options and explain them in simpler terms."

[0847] Step 6:

[0848] The terminal visually displays the adjusted property suggestions received from the server to the user. The user then reviews the details and selects the most suitable home. This final output is a crucial element in enabling the user to confidently make the best decision.

[0849] 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.

[0850] 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.

[0851] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0852] 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.

[0853] 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.

[0854] 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.

[0855] 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.

[0856] 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.

[0857] 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."

[0858] 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.

[0859] 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.

[0860] 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.

[0861] 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.

[0862] 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.

[0863] 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.

[0864] 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.

[0865] 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.

[0866] 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.

[0867] 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.

[0868] 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.

[0869] 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.

[0870] The following is further disclosed regarding the embodiments described above.

[0871] (Claim 1)

[0872] A terminal that inputs the user's desired conditions and sends the information to the server,

[0873] A server that retrieves real estate information from a database based on the user's desired conditions,

[0874] A server that aggregates surrounding environment information, natural disaster risk information, and reliability information of service providers for acquired real estate information and calculates an evaluation score,

[0875] A server that ranks properties based on evaluation scores and sends information to the user's terminal to suggest them,

[0876] A system that includes this.

[0877] (Claim 2)

[0878] The system according to claim 1, wherein the surrounding environment information includes public safety information, transportation access information, and commercial facility information.

[0879] (Claim 3)

[0880] The system according to claim 1, wherein natural disaster risk information is calculated by referring to earthquake risk data and flood risk data.

[0881] "Example 1"

[0882] (Claim 1)

[0883] An information terminal device that receives user requests, converts them into communication data, and transmits them to a processing device,

[0884] A processing device that retrieves information about real estate from an information storage device based on the user's desired conditions,

[0885] A processing device that integrates information on acquired real estate, information on the surrounding environment, risk information regarding natural disasters, and credit information of the developer, and generates an evaluation-based index.

[0886] A processing device that ranks real estate based on evaluation-based indicators and transmits information to an information terminal device to recommend it to the user,

[0887] A system that includes this.

[0888] (Claim 2)

[0889] The system according to claim 1, wherein the information regarding the surrounding environment includes safety information, information regarding public transportation, and information regarding commercial facilities.

[0890] (Claim 3)

[0891] The system according to claim 1, wherein risk information regarding natural disasters is calculated by referring to earthquake risk data and flood risk data.

[0892] "Application Example 1"

[0893] (Claim 1)

[0894] A terminal means for inputting user preferences and transmitting the information to a central processing unit,

[0895] A means of obtaining asset information from a database based on the user's desired conditions,

[0896] A means for aggregating surrounding environment information, natural disaster risk information, and reliability information of service providers from acquired asset information and calculating evaluation indicators,

[0897] A means of ranking properties based on evaluation indicators and sending information to the user's terminal for suggestion,

[0898] A means of providing information to support the selection of the most suitable property by obtaining local infrastructure information in real time from an external API based on conditions entered by the user,

[0899] A system that includes this.

[0900] (Claim 2)

[0901] The system according to claim 1, wherein the surrounding environment information includes public safety information, transportation activity information, and commercial facility information.

[0902] (Claim 3)

[0903] The system according to claim 1, wherein natural disaster risk information is calculated by referring to geological activity risk data and flood risk data.

[0904] "Example 2 of combining an emotion engine"

[0905] (Claim 1)

[0906] An input means for inputting the user's desired conditions and transmitting the information to an information processing device,

[0907] A processing means for acquiring asset information from a recording device based on the user's desired conditions,

[0908] An analytical means for aggregating environmental information, geographical risk information, and provider reliability information from acquired asset information and calculating evaluation indicators,

[0909] Based on feedback from the analysis tools, a generative AI model is used to analyze the user's emotional state and customize the order and content of property suggestions to present the user with adjusted information.

[0910] A transmission means for transmitting information to an information processing device for proposing adjusted property information to the user,

[0911] A system that includes this.

[0912] (Claim 2)

[0913] The system according to claim 1, wherein the surrounding environment information includes security information, transportation access information, and sales facility information.

[0914] (Claim 3)

[0915] The system according to claim 1, wherein natural disaster risk information is calculated by referring to earthquake risk data and flood risk data.

[0916] "Application example 2 when combining with an emotional engine"

[0917] (Claim 1)

[0918] A data device that takes user preferences as input and sends the information to a server,

[0919] An information processing device that retrieves property information from a recording medium based on the user's desired conditions,

[0920] An information processing device that aggregates surrounding environment information, natural disaster risk information, and reliability information of the providing organization for acquired property information and calculates an evaluation value,

[0921] An information processing device that ranks properties based on evaluation scores and transmits information to a data device for proposing them to users,

[0922] An information processing device that adjusts the content of a proposed property using an emotion recognition device that recognizes the emotional state of the user,

[0923] A system that includes this.

[0924] (Claim 2)

[0925] The system according to claim 1, wherein the surrounding environment information includes safety information, transportation information, and commercial location information.

[0926] (Claim 3)

[0927] The system according to claim 1, wherein natural disaster risk information is calculated by referring to earthquake risk data and flood risk data. [Explanation of Symbols]

[0928] 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 terminal means for inputting user preferences and transmitting the information to a central processing unit, A means of obtaining asset information from a database based on the user's desired conditions, A means for aggregating surrounding environment information, natural disaster risk information, and reliability information of service providers from acquired asset information and calculating evaluation indicators, A means of ranking properties based on evaluation indicators and sending information to the user's terminal for suggestion, A means of providing information to support the selection of the most suitable property by obtaining local infrastructure information in real time from an external API based on conditions entered by the user, A system that includes this.

2. The system according to claim 1, wherein the surrounding environment information includes public safety information, transportation activity information, and commercial facility information.

3. The system according to claim 1, wherein natural disaster risk information is calculated by referring to geological activity risk data and flood risk data.

Images