system

Abstract

We provide the system. [Solution] A means of receiving the user's desired conditions, Means of collecting real estate-related data from multiple sources, A means of evaluating the characteristics of the surrounding area based on the collected data, A method for scoring and ranking real estate based on evaluation results, A means of providing users with scored and ranked real estate information, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, 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 chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In recent years, due to the diversification of personal lifestyles and values, it has become increasingly difficult to find a suitable property in real estate purchases. In particular, considering complex factors such as the surrounding environment, public security, and natural disaster risks, a vast amount of information collection and analysis are required to select the optimal property that meets the user's wishes, which is the cause of requiring a lot of time and effort. As a result, there is a problem that the user cannot efficiently and safely select the ideal residence that they originally desire.

Means for Solving the Problems

[0005] This invention relates to a system that receives user preferences as input and automatically collects real estate and related information from the internet and other sources. Based on the collected information, this system uses AI technology to analyze the surrounding environment, safety, natural disaster risk, etc., of properties and scores the results. By comprehensively evaluating the scores and creating a ranking, the system provides users with the most suitable real estate candidates. As a result, users can confidently select from properties that best suit their conditions.

[0006] "Means of receiving user preferences" refers to a function that collects the user's desired conditions regarding real estate and processes them within the system.

[0007] "Means of collecting data from information sources" refers to the function of obtaining necessary real estate-related data from multiple information providers, such as the internet and databases.

[0008] "Means for evaluating the characteristics of the surrounding area" refers to a function that evaluates the environment and convenience surrounding a property based on acquired data.

[0009] "Methods for scoring and ranking" refers to a function that quantifies real estate based on its individual evaluation results and generates an overall ranking.

[0010] "Means of providing real estate information to users" refers to a function that presents ranked property information to users and assists them in making a selection.

[0011] "Means for evaluating public safety information" refers to a function that analyzes information on crime rates and safety in a region and evaluates the level of public safety in that region.

[0012] "Means for evaluating natural disaster risk information" refers to a function that evaluates the risk to a property based on data related to natural disasters such as earthquakes and floods. [Brief explanation of the drawing]

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

[0014] An example of an embodiment of the system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

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

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

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

[0019] 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).

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

[0021] [First Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0034] This invention relates to a system that uses AI to collect and analyze real estate information and propose the most suitable properties to users, enabling them to confidently choose their ideal home. The system consists of a terminal that receives user input and a server that analyzes the information and supports decision-making.

[0035] The user first enters their desired property criteria (e.g., "area," "budget," "floor plan," "distance from station," etc.) into the terminal. The terminal then sends the entered criteria to the server. Based on this information, the server gathers property data that matches the criteria from multiple reliable real estate sources.

[0036] Next, the server analyzes the data collected for each property. In the surrounding environment assessment, it determines whether the area is livable based on available facilities and transportation information. In the safety assessment, it numerically indicates safety based on local crime statistics. For asset value assessment, it estimates future asset value using past real estate transaction data. Furthermore, in the disaster risk assessment, it uses statistical information on earthquake and flood risks to evaluate how well the area can cope with risks.

[0037] Based on these analysis results, the server scores each property and evaluates them in a ranking format. The scores are adjusted according to the user's priority of desired conditions, and the most suitable property is selected overall.

[0038] For example, if a user enters conditions such as "within Tokyo's 23 wards," "budget of 60 million yen," "3LDK," and "within a 10-minute walk from the station," the server will analyze all properties that match these conditions. Taking into account factors such as the safety of the surrounding area and predictions of land price fluctuations, the server will feed back information on the top-ranked properties to the user's terminal. The user can then use this information to check the details and select their ideal property.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The user enters their desired criteria into the terminal. These criteria may include "region," "budget," "floor plan," and "distance from the station." The terminal then sends this input information to the server.

[0042] Step 2:

[0043] The server accesses multiple real estate information sources based on the user's requested criteria and collects data on relevant properties. This includes basic information such as price, location, floor plan, and amenities.

[0044] Step 3:

[0045] The server also acquires surrounding environment data related to the collected property information. This includes information about the convenience of facilities such as educational institutions, medical facilities, parks, and transportation infrastructure.

[0046] Step 4:

[0047] The server performs a safety assessment for each property by referring to local crime statistics. This is an analysis based on the area's past crime rate and the location of the nearest police station.

[0048] Step 5:

[0049] The server uses historical real estate transaction data for the target area to assess the future asset value of properties. This is intended to predict fluctuations in land prices and demand trends.

[0050] Step 6:

[0051] The server analyzes risk data such as earthquakes and floods to assess natural disaster risks. In particular, it uses information on ground vulnerability and flood hazard maps.

[0052] Step 7:

[0053] The server comprehensively evaluates each property based on its surrounding environment, safety, asset value, and disaster risk, and assigns a score to each property. The scoring is adjusted according to the user's criteria and priorities.

[0054] Step 8:

[0055] The server creates a ranking based on the score for each property. The ranking displays properties in order of how well they meet the criteria.

[0056] Step 9:

[0057] The server sends the ranking results to the user's device. The device then displays detailed information about the top-ranked properties to the user, assisting them in their selection.

[0058] Step 10:

[0059] Users can review the presented information and select properties that interest them. Based on this information, they can then proceed to the next steps, such as meeting with a real estate agent or visiting the property.

[0060] (Example 1)

[0061] 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."

[0062] When choosing a home, users need to select a suitable residence based on raw data collected from numerous sources. However, this data is vast and requires individual evaluation, placing a significant burden of time and effort on users. Furthermore, the criteria for making selections that consider important factors such as the safety and disaster risk of a residence are unclear, hindering optimal decision-making.

[0063] 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.

[0064] In this invention, the server includes means for receiving the user's living conditions, means for collecting housing information from multiple databases, and means for analyzing the surrounding environment based on the collected information. This automates the process of selecting the optimal residence from a vast amount of housing information based on the user's conditions, and enables a comprehensive evaluation that takes into account important factors such as public safety and disaster risk.

[0065] "Means of receiving user's housing requirements" refers to a function that allows users to input detailed preferences for their desired residence, such as area, price, floor plan, and access, via a terminal, and collects this information as data.

[0066] "Means of collecting housing information from multiple databases" refers to a function that allows you to collect necessary housing information from housing information providers using APIs or data feeds.

[0067] "Means for analyzing the surrounding environment based on collected information" refers to a function that uses collected residential information to analyze and evaluate environmental data (such as nearby facilities, transportation access, and public safety information) related to the area surrounding the property.

[0068] "Means for evaluating and listing residences based on analysis results" refers to a function that quantifies factors such as the surrounding environment, safety, and asset value, ranks residences based on these quantifications, and presents them as a list.

[0069] "Means of providing users with evaluated and listed housing information" refers to a function that displays ranked housing information on the user's device in an easy-to-understand format and presents it in a selectable format.

[0070] "Means of adjusting evaluation results based on user preferences" refers to a function that adjusts the overall score or ranking according to the conditions that users particularly value (such as price, location, and safety).

[0071] This invention helps users efficiently select their ideal residence. The system mainly consists of terminals and servers, and provides users with optimal residence information through the processes of data collection, analysis, and result provision.

[0072] First, the user enters their desired housing conditions through their device. The device receives this data and sends it to the server. The server collects property information from a reliable real estate database via API or data feed. The server then analyzes the collected property information and evaluates details of the surrounding environment, such as nearby facilities, transportation access, and safety information. Based on this, the server performs a multifaceted evaluation of each property, including its asset value and disaster risk, and uses this information to perform scoring and ranking.

[0073] The server sends the scoring results to the terminal, where the user visually confirms the information. Users can smoothly compare and select from the displayed property information. Specifically, if a user provides conditions such as "within Tokyo's 23 wards," "budget of 60 million yen," "3LDK," and "within a 10-minute walk from the station," the server analyzes the corresponding properties in real time, identifies the property that best fits the conditions, and feeds its evaluation back to the terminal.

[0074] By utilizing a generative AI model, scoring can be dynamically adjusted according to each user's specific conditions and priorities. For example, for users who place more importance on asset value, the scoring will take that factor into account. An example prompt might be: "I'm looking for a 3LDK property near a train station in Tokyo's 23 wards, with a budget of under 60 million yen. Please provide a ranking of properties with good safety and promising future asset value." This allows for customized property selection for each user.

[0075] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0076] Step 1:

[0077] The user enters their housing requirements into the terminal.

[0078] Specifically, users enter their desired conditions, such as location, budget, floor plan, and distance from the station, into the terminal's user interface and press the send button.

[0079] Based on the input, the terminal compiles the user's conditions as structured data and generates data packets.

[0080] As output, the prepared data packets are sent to the server.

[0081] Step 2:

[0082] The terminal sends user condition data to the server.

[0083] Specifically, a request is sent to the server using the HTTP protocol, and the server receives the information.

[0084] The system sends conditional data as input to the server and receives an acknowledgment of receipt from the server as output.

[0085] Step 3:

[0086] The server collects property information from multiple databases based on the user's requested conditions.

[0087] In terms of specific operations, the server calls relevant APIs to retrieve property data from reliable real estate sources.

[0088] The system filters the results using user criteria as input, retrieves relevant property information, and prepares the results in a list format.

[0089] The output is a list of property information that matches the criteria.

[0090] Step 4:

[0091] The server analyzes the acquired property information and performs a detailed analysis.

[0092] Specifically, indicators such as the surrounding environment, public safety, property value, and disaster risk are calculated and scored.

[0093] The system analyzes the property information used as input and calculates individual scores based on each indicator.

[0094] The output is a set of scores assigned to each property.

[0095] Step 5:

[0096] The server adjusts the property scores based on the analysis results and ranks them accordingly.

[0097] Specifically, the overall score is recalculated by applying weights based on the user's priority criteria.

[0098] The system uses individual scores and user priority as input to calculate and sort properties in order of priority.

[0099] The output is a ranked list of properties.

[0100] Step 6:

[0101] The server sends the ranking results to the terminal.

[0102] Specifically, the ranking list is converted to JSON format and sent to the terminal as an HTTP response.

[0103] The ranking results, used as input, are formatted and sent to the terminal as output.

[0104] Step 7:

[0105] Users view the ranking results sent via their devices and consider properties.

[0106] Specifically, you scroll through the ranking information displayed on your device and tap to view the details.

[0107] The system visualizes ranking data sent to the terminal as input and provides information necessary for user decision-making as output.

[0108] (Application Example 1)

[0109] 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."

[0110] In modern real estate searching, a challenge exists in that it is difficult for users to find their ideal property based on their desired conditions. Furthermore, there is a lack of means for users to access relevant property information in real time, even while on the go. As a result, users have to manually search for a large amount of information, which is time-consuming and laborious.

[0111] 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.

[0112] In this invention, the server includes means for receiving user preferences, means for collecting real estate-related data from multiple sources, means for evaluating the characteristics of the surrounding area based on the collected data, means for scoring and ranking real estate based on the evaluation results, means for providing the scored and ranked real estate information to the user, and video presentation means for presenting real estate information to the user while they are on the move. This allows the user to efficiently select their ideal real estate even while on the move, saving time and effort.

[0113] "A means of receiving user preferences" refers to an interface for users to input their desired real estate conditions.

[0114] "Means of collecting real estate-related data from multiple sources" refers to a system that automatically collects relevant data from reliable real estate sources.

[0115] "Means for evaluating the characteristics of the surrounding area based on collected data" refers to a function that analyzes collected information and evaluates the living environment, transportation access, and other aspects of that area.

[0116] "A method for scoring and ranking real estate based on evaluation results" refers to a system that assigns points to each property and ranks them based on the evaluated data.

[0117] "Means of providing users with scored and ranked real estate information" refers to methods of displaying scoring results in a format that users can view.

[0118] "A video presentation method for showing real estate information to users while they are on the move" refers to a device or technology that provides real estate information visually in real time while a user is traveling.

[0119] To realize this invention, the server will build a system that provides real estate information in real time based on the user's desired conditions. The user will input their desired conditions using a smartphone or smart glasses, and this information will be sent to the server. In this process, the interface of the smartphone or smart glasses will function as a means of receiving the user's desired conditions.

[0120] The server uses Python and Tensorflow® to collect data from multiple real estate information sources and perform property valuations. Specifically, it evaluates the characteristics of the surrounding environment, the state of public safety, and the risk of natural disasters, using historical statistical data and real-time environmental data. Furthermore, a Flask-based backend processes and ranks this data.

[0121] The evaluation results are ranked based on the user's priorities and displayed in real time on the smartphone or smart glasses screen. In particular, when the user is on the move, the smart glasses' visual display features provide recommended properties in their vicinity. This allows users to efficiently select the best property even while traveling.

[0122] For example, if a user is searching for a property within Tokyo's 23 wards, they can enter conditions such as "budget of 60 million yen," "3LDK," and "within a 10-minute walk from the station." The server evaluates properties that match these conditions and displays the ranked properties on the smart glasses' display. An example of a prompt message in this case would be, "Based on the conditions you specified (e.g., within Tokyo's 23 wards, budget of 60 million yen), we will score recommended property information and display it on your smartphone."

[0123] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0124] Step 1:

[0125] The user enters their desired property criteria (e.g., area, budget, floor plan, distance from the station) through a smartphone or smart glasses interface. This input data is stored on the device as a means of receiving the user's preferences.

[0126] Step 2:

[0127] The terminal sends the user's entered preferences to the server. This transmitted data becomes the input, and the server prepares for data collection for the next step.

[0128] Step 3:

[0129] The server automatically collects data from multiple real estate sources. This data includes property prices, floor plans, location, and surrounding environment information. The collected data is stored in a database using Python and TensorFlow.

[0130] Step 4:

[0131] The server analyzes the collected data. Specifically, algorithms are executed to evaluate the characteristics of the surrounding area, safety, and natural disaster risk based on the collected real estate information. These evaluations are performed through data processing that combines historical statistical data and real-time data.

[0132] Step 5:

[0133] The server scores each property based on the evaluation results and creates a ranking. This process prioritizes the collected and analyzed data and assigns scores accordingly. The ranking is performed using a backend system built with Flask.

[0134] Step 6:

[0135] The server provides scored and ranked property information to the user's device. This information is displayed on the screen of a smartphone or smart glasses, and the user uses this information to select their ideal property.

[0136] Step 7:

[0137] When a user is on the move, smart glasses visually display recommended properties in their vicinity in real time. Using overlay technology, the information is presented in an intuitive and easy-to-understand manner for users on the go.

[0138] 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.

[0139] This invention relates to a system that assists users in choosing real estate by taking their emotions into consideration. In addition to its basic functions of collecting and evaluating real estate information based on the user's desired conditions and proposing the most suitable properties, this system is equipped with an emotion engine that recognizes the user's emotional state and reflects it in the proposal process.

[0140] First, the user enters their desired property criteria into the terminal. Based on these criteria, the terminal sends the information to the server. The server accesses online real estate information sources and local information databases to collect property information that matches the specified criteria. The collected information includes property price, location, and facilities, as well as surrounding environment, safety information, and natural disaster risk data.

[0141] The emotion engine evaluates user emotions based on direct user feedback, interface usage, voice analysis, and facial expression analysis. For example, if a user displays an unsatisfied facial expression or tone of voice, the server can detect this and modify the evaluation process and suggestions. This information allows the system to strive to provide information in a stress-free manner for the user.

[0142] Let's explain a specific example of its operation. When a user enters conditions such as "Nagano City," "budget of 50 million yen," "4LDK," and "good view," the server searches for properties that meet the conditions, while the emotion engine monitors the user's reaction. If the user shows positive emotions towards the suggested properties, the system analyzes this trend and suggests other similar properties. If the user shows anxiety, the system enhances the information provided, providing detailed explanations of safety assessments and disaster risks.

[0143] In this way, by providing feedback that reflects the user's emotions, this system not only provides information but also reduces the user's psychological burden while supporting them in making the optimal real estate selection decision.

[0144] The following describes the processing flow.

[0145] Step 1:

[0146] The user enters their desired property criteria into the terminal. This includes information such as "region," "budget," "floor plan," and "specific requirements." The terminal then sends the received information to the server.

[0147] Step 2:

[0148] Based on the submitted conditions, the server retrieves property information from multiple real estate sources and related databases. At this stage, the server collects not only basic property information but also detailed data such as the surrounding environment, safety, and disaster risk.

[0149] Step 3:

[0150] The server organizes the collected data based on the user's preferences and scores each property. This scoring reflects the user's priorities, allowing for a presentation optimized to their desired conditions.

[0151] Step 4:

[0152] The emotion engine activates and analyzes the user's emotional state. This is done using facial expression data obtained through interaction with the user and voice analysis. Based on these results, it determines how the user is receiving the property information.

[0153] Step 5:

[0154] The server adjusts its property recommendation strategy based on the user's emotional state, as determined by the emotion engine. For example, if a user shows a negative reaction, the server changes the scoring criteria and recommendation content, and updates the list of candidate properties.

[0155] Step 6:

[0156] The server sends the updated property information to the terminal and presents it to the user. The terminal displays the detailed information and collects the user's response again.

[0157] Step 7:

[0158] Users can review the details of the properties presented and then view further information or contact agents for properties that interest them.

[0159] Step 8:

[0160] If feedback is received from the user regarding their final property selection, the terminal stores that information, and the server uses it to improve future suggestions. This process makes it possible to provide suggestions that are better suited to the user's needs.

[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 as the "terminal".

[0163] Conventional real estate information systems typically propose properties based on the user's desired conditions. However, there is a growing need to support users in selecting properties that are more suitable for them by also considering their emotional state. Furthermore, conventional systems often fail to adequately consider safety information and disaster risk information, resulting in insufficient information provision that allows users to choose a place to live with peace of mind.

[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 means for receiving the user's desired conditions, means for collecting property-related information from multiple sources, means for evaluating the characteristics of the area based on the collected information, means for analyzing the user's emotional state and reflecting it in the suggestions, and means for scoring and ranking properties considering the evaluation results and the user's emotions. This makes it possible to suggest optimal properties that reflect the user's emotions, and furthermore, to provide reassuring information that takes into account safety information and disaster risk information.

[0166] "User preferences" refer to information such as the area, budget, floor plan, and other special requests the user has for a property.

[0167] "Information sources" refer to multiple resources that provide real estate and local information data on the internet.

[0168] "Property-related information" includes all details about the property, such as its price, location, facilities, surrounding environment, safety information, and natural disaster risk data.

[0169] "Evaluating the characteristics of a region" means analyzing information about the region, including the surrounding environment, public safety, and natural disaster risks, and conducting a comprehensive evaluation.

[0170] "Analyzing emotional states and reflecting them in proposals" means analyzing the user's emotions from their facial expressions and voice, and incorporating the results into the property proposal process.

[0171] "Scoring and ranking" is a process of numerically evaluating and ranking properties based on evaluation results and user sentiment.

[0172] This real estate selection support system consists of three basic components: the user, the terminal, and the server. The system starts with the user using the terminal to input their desired property criteria. This terminal is equipped with a camera and microphone, and has the function of collecting facial expressions and voice data and sending it to the server. The terminal formats the entered desired criteria and sends the necessary information to the server.

[0173] The server plays a central role in collecting, storing, and analyzing property-related information from multiple sources. These sources include online databases and local information systems. This analysis utilizes dedicated analytical algorithms and generative AI models to score and rank properties based on the collected information. Furthermore, an emotion analysis engine assesses the user's emotional state in real time and incorporates this into the optimal property recommendations.

[0174] For example, if a user enters criteria such as "city center," "under 30 million yen," "pets allowed," and "within a 10-minute walk from the station," the server will search for suitable properties based on these criteria. If the user expresses feelings of joy or satisfaction, it will suggest further properties with similar characteristics. Conversely, if dissatisfaction is detected, the server will provide additional options and detailed information to help the user find a suitable property.

[0175] An example of a prompt message could be: "Please explain how the system works: when a user enters their desired real estate conditions, it suggests the most suitable properties based on those conditions and adjusts the suggestions according to the user's emotional response."

[0176] This system is expected to enable users to make intuitive and rational decisions about choosing real estate that are in line with their emotions, thereby reducing psychological burden.

[0177] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0178] Step 1:

[0179] The user enters their desired property criteria into the terminal. Specifically, they enter information such as location, budget, floor plan, and any special requests. This entered information is formatted and prepared to be sent to the server. The entered criteria serve as the basis for subsequent information gathering and analysis.

[0180] Step 2:

[0181] The server receives the user's desired conditions transmitted from the terminal. Based on the received information, it accesses multiple information sources on the internet and local information databases to collect detailed information about relevant properties. This includes property price, location, amenities, and surrounding environment. The collected information is stored in a database and analyzed later.

[0182] Step 3:

[0183] The server uses an emotion analysis engine to evaluate the user's emotional state. It analyzes voice and facial expression data collected from the device's microphone and camera to quantify the user's emotions. This analysis result is used as data to determine how the user perceives the property information.

[0184] Step 4:

[0185] The server combines collected property information with the user's emotional state and uses a generative AI model to score and rank properties. Real estate information is evaluated based on factors that evoke positive emotions in the user. This results in the ranking of properties that are best suited to the user.

[0186] Step 5:

[0187] The server provides users with scored and ranked property information. The information displayed to users also includes feedback based on the user's emotional state. If the user is highly satisfied, similar properties are suggested; conversely, if there are concerns or dissatisfactions, additional information and improvement suggestions are provided. Based on this information, users can choose their next action.

[0188] (Application Example 2)

[0189] 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".

[0190] In modern real estate selection, users must make decisions based on a large amount of information, which can increase their psychological burden. In particular, a user's emotional state greatly influences their choice, but conventional systems often do not take this into account. This invention aims to reduce psychological burden and support the selection of the optimal real estate by analyzing the user's emotional state and providing real estate proposals that reflect it.

[0191] 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.

[0192] In this invention, the server includes means for receiving the user's desired conditions, means for collecting real estate-related data from multiple sources, means for evaluating the characteristics of the surrounding area based on the collected data, and means for analyzing the user's emotional state and reflecting the analysis results in the proposal process. This makes it possible to propose the best real estate that takes the user's emotional state into consideration.

[0193] A "user" refers to an individual or corporation that uses the system to select real estate.

[0194] "Desired conditions" refer to the elements and requirements that users want to consider when choosing a property.

[0195] "Information sources" refer to various public and private databases and websites that provide data related to real estate.

[0196] "Real estate" refers to properties that are subject to buying, selling, or renting, such as houses, land, and commercial facilities.

[0197] "Data" refers to information related to real estate, such as price, location, facilities, surrounding environment, safety information, and natural disaster risk.

[0198] "Characteristics of the surrounding area" refers to various factors of the area surrounding the property, and includes elements for evaluating soundness, convenience, and livability.

[0199] "Evaluation" refers to the act of quantitatively or qualitatively analyzing and judging the characteristics of real estate and the surrounding area based on collected data.

[0200] "Scoring" refers to the process of assigning numerical values ​​to each element of a property based on the evaluation results.

[0201] "Ranking" refers to the process of sorting properties based on their importance and suitability, according to the scoring results.

[0202] "Emotional state" refers to the psychological or emotional response a user shows to a real estate proposal.

[0203] "Analysis" refers to the process of collecting data on users' emotional states, analyzing it, and making judgments based on that data.

[0204] The "proposal process" refers to a series of steps that involve presenting real estate information to users and guiding them to take the next action based on their responses.

[0205] The system for realizing this invention consists of a server, a user terminal, and a consumer robot. The server runs a program that scrapes real estate-related data from publicly available databases on the internet and other information sources. The collected data is processed in Python to analyze the characteristics of the surrounding area and perform various property valuations.

[0206] The user terminal has an interface for entering desired conditions, which are then sent to the server. This interface can be operated by the user via a smartphone or PC. Based on the entered information, the server scores and ranks the most suitable properties.

[0207] On the other hand, consumer robots have the ability to analyze the user's emotional state in real time. They use Google® Speech-to-Text API for speech recognition and OpenCV for facial expression analysis. This emotional analysis information is then reflected in the suggestion process on the server side.

[0208] For example, if a user enters "a quiet place with lots of nature" as their desired condition, the server will list properties that meet that condition with high priority, and a consumer robot will analyze the user's facial expressions to confirm whether the suggestions are appropriate.

[0209] Examples of prompt statements for a generative AI model are as follows:

[0210] "I've been really busy and tired lately, so I want to move to a quiet place where I can relax. But it should ideally be somewhere with good access."

[0211] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0212] Step 1:

[0213] Users use their smartphones or computers to input their desired property criteria into the terminal. This input includes the property's location, price range, floor plan, and specific requests (e.g., quiet neighborhood, natural environment). The terminal then sends this data to the server.

[0214] Step 2:

[0215] The server uses the user's requested criteria as input to access multiple real estate information sources. It uses a Python script to scrape data, collecting detailed property information from the internet. The output is a list of properties that match the specified criteria.

[0216] Step 3:

[0217] The server uses collected data as input to evaluate the characteristics of the surrounding area. This evaluation includes factors such as local safety, transportation convenience, educational environment, and the availability of commercial facilities. The output is an evaluation score for each property, which is used to score and rank real estate.

[0218] Step 4:

[0219] The consumer robot interacts with the user and analyzes their emotional state from their voice and facial expressions. The voice is transcribed using the Google Speech-to-Text API, and facial expressions are analyzed using OpenCV. The input is real-time user emotion data, and the output is a specific emotional state expressed by the user (e.g., joy, anxiety).

[0220] Step 5:

[0221] The server uses the user's emotional state as input to adjust the property information it suggests. The property list is re-ranked based on the emotional state, and information that matches the user's emotions is enhanced and supplemented. The output is an optimized property suggestion list that reflects the emotional state.

[0222] Step 6:

[0223] The user receives optimized real estate suggestions through a consumer robot. The robot verbally explains the details of the suggested properties (e.g., safety rating, natural environment, etc.) and monitors the user's additional emotional changes. The input is a list of suggestions from the server, and the output is the user's final feedback.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] [Second Embodiment]

[0228] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0229] 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.

[0230] 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).

[0231] 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.

[0232] 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.

[0233] 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).

[0234] 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.

[0235] 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.

[0236] 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.

[0237] 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.

[0238] 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.

[0239] 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".

[0240] This invention relates to a system that uses AI to collect and analyze real estate information and propose the most suitable properties to users, enabling them to confidently choose their ideal home. The system consists of a terminal that receives user input and a server that analyzes the information and supports decision-making.

[0241] The user first enters their desired property criteria (e.g., "area," "budget," "floor plan," "distance from station," etc.) into the terminal. The terminal then sends the entered criteria to the server. Based on this information, the server gathers property data that matches the criteria from multiple reliable real estate sources.

[0242] Next, the server analyzes the data collected for each property. In the surrounding environment assessment, it determines whether the area is livable based on available facilities and transportation information. In the safety assessment, it numerically indicates safety based on local crime statistics. For asset value assessment, it estimates future asset value using past real estate transaction data. Furthermore, in the disaster risk assessment, it uses statistical information on earthquake and flood risks to evaluate how well the area can cope with risks.

[0243] Based on these analysis results, the server scores each property and evaluates them in a ranking format. The scores are adjusted according to the user's priority of desired conditions, and the most suitable property is selected overall.

[0244] For example, if a user enters conditions such as "within Tokyo's 23 wards," "budget of 60 million yen," "3LDK," and "within a 10-minute walk from the station," the server will analyze all properties that match these conditions. Taking into account factors such as the safety of the surrounding area and predictions of land price fluctuations, the server will feed back information on the top-ranked properties to the user's terminal. The user can then use this information to check the details and select their ideal property.

[0245] The following describes the processing flow.

[0246] Step 1:

[0247] The user enters their desired criteria into the terminal. These criteria may include "region," "budget," "floor plan," and "distance from the station." The terminal then sends this input information to the server.

[0248] Step 2:

[0249] The server accesses multiple real estate information sources based on the user's requested criteria and collects data on relevant properties. This includes basic information such as price, location, floor plan, and amenities.

[0250] Step 3:

[0251] The server also acquires surrounding environment data related to the collected property information. This includes information about the convenience of facilities such as educational institutions, medical facilities, parks, and transportation infrastructure.

[0252] Step 4:

[0253] The server performs a safety assessment for each property by referring to local crime statistics. This is an analysis based on the area's past crime rate and the location of the nearest police station.

[0254] Step 5:

[0255] The server uses historical real estate transaction data for the target area to assess the future asset value of properties. This is intended to predict fluctuations in land prices and demand trends.

[0256] Step 6:

[0257] The server analyzes risk data such as earthquakes and floods to assess natural disaster risks. In particular, it uses information on ground vulnerability and flood hazard maps.

[0258] Step 7:

[0259] The server comprehensively evaluates each property based on its surrounding environment, safety, asset value, and disaster risk, and assigns a score to each property. The scoring is adjusted according to the user's criteria and priorities.

[0260] Step 8:

[0261] The server creates a ranking based on the score for each property. The ranking displays properties in order of how well they meet the criteria.

[0262] Step 9:

[0263] The server sends the ranking results to the user's device. The device then displays detailed information about the top-ranked properties to the user, assisting them in their selection.

[0264] Step 10:

[0265] Users can review the presented information and select properties that interest them. Based on this information, they can then proceed to the next steps, such as meeting with a real estate agent or visiting the property.

[0266] (Example 1)

[0267] 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."

[0268] When choosing a home, users need to select a suitable residence based on raw data collected from numerous sources. However, this data is vast and requires individual evaluation, placing a significant burden of time and effort on users. Furthermore, the criteria for making selections that consider important factors such as the safety and disaster risk of a residence are unclear, hindering optimal decision-making.

[0269] 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.

[0270] In this invention, the server includes means for receiving the user's living conditions, means for collecting housing information from multiple databases, and means for analyzing the surrounding environment based on the collected information. This automates the process of selecting the optimal residence from a vast amount of housing information based on the user's conditions, and enables a comprehensive evaluation that takes into account important factors such as public safety and disaster risk.

[0271] "Means of receiving user's housing requirements" refers to a function that allows users to input detailed preferences for their desired residence, such as area, price, floor plan, and access, via a terminal, and collects this information as data.

[0272] "Means of collecting housing information from multiple databases" refers to a function that allows you to collect necessary housing information from housing information providers using APIs or data feeds.

[0273] "Means for analyzing the surrounding environment based on collected information" refers to a function that uses collected residential information to analyze and evaluate environmental data (such as nearby facilities, transportation access, and public safety information) related to the area surrounding the property.

[0274] "Means for evaluating and listing residences based on analysis results" refers to a function that quantifies factors such as the surrounding environment, safety, and asset value, ranks residences based on these quantifications, and presents them as a list.

[0275] "Means of providing users with evaluated and listed housing information" refers to a function that displays ranked housing information on the user's device in an easy-to-understand format and presents it in a selectable format.

[0276] "Means of adjusting evaluation results based on user preferences" refers to a function that adjusts the overall score or ranking according to the conditions that users particularly value (such as price, location, and safety).

[0277] This invention helps users efficiently select their ideal residence. The system mainly consists of terminals and servers, and provides users with optimal residence information through the processes of data collection, analysis, and result provision.

[0278] First, the user enters their desired housing conditions through their device. The device receives this data and sends it to the server. The server collects property information from a reliable real estate database via API or data feed. The server then analyzes the collected property information and evaluates details of the surrounding environment, such as nearby facilities, transportation access, and safety information. Based on this, the server performs a multifaceted evaluation of each property, including its asset value and disaster risk, and uses this information to perform scoring and ranking.

[0279] The server sends the scoring results to the terminal, and the user visually checks the information on the terminal. The user can smoothly compare and select the displayed property information. Specifically, when the user presents conditions such as "within the 23 wards of Tokyo", "budget of 60 million yen", "3LDK", and "within a 10-minute walk from the station", the server analyzes the corresponding properties in real time, identifies the property most suitable for the conditions, and feedbacks its evaluation to the terminal.

[0280] By utilizing the generated AI model, the scoring can be dynamically adjusted according to the specific conditions and priorities of each user. For example, for users who place more importance on asset value, scoring is performed taking that factor into account. Also, as an example of the prompt text, the input is made in the form of "Looking for a 3LDK property within a budget of 60 million yen near a station in the 23 wards of Tokyo. Please rank places with good security and potential for future asset value." This enables property selection customized for each user.

[0281] The flow of the specific process in Example 1 will be described using FIG. 11.

[0282] Step 1:

[0283] The user inputs conditions related to the residence into the terminal.

[0284] Specifically, enter desired conditions such as region, budget, floor plan, distance from the station, etc. into the user interface of the terminal and press the send button.

[0285] Based on the input, the terminal summarizes the user's conditions as structured data and generates a data packet.

[0286] As output, the prepared data packet is sent to the server.

[0287] Step 2:

[0288] The terminal sends user condition data to the server.

[0289] Specifically, a request is sent to the server using the HTTP protocol, and the server receives the information.

[0290] The system sends conditional data as input to the server and receives an acknowledgment of receipt from the server as output.

[0291] Step 3:

[0292] The server collects property information from multiple databases based on the user's requested conditions.

[0293] In terms of specific operations, the server calls relevant APIs to retrieve property data from reliable real estate sources.

[0294] The system filters the results using user criteria as input, retrieves relevant property information, and prepares the results in a list format.

[0295] The output is a list of property information that matches the criteria.

[0296] Step 4:

[0297] The server analyzes the acquired property information and performs a detailed analysis.

[0298] Specifically, indicators such as the surrounding environment, public safety, property value, and disaster risk are calculated and scored.

[0299] The system analyzes the property information used as input and calculates individual scores based on each indicator.

[0300] The output is a set of scores assigned to each property.

[0301] Step 5:

[0302] The server adjusts the property scores based on the analysis results and ranks them accordingly.

[0303] Specifically, weighting is performed based on the user's priority conditions, and the comprehensive score is recalculated.

[0304] Using the individual scores and the user's priorities as input, calculations are performed and the properties are arranged in order of priority.

[0305] The output is a ranked list of properties.

[0306] Step 6:

[0307] The server sends the ranking result to the terminal.

[0308] As a specific operation, the ranking list is converted into JSON format and sent to the terminal as an HTTP response.

[0309] Format the ranking result as input and send it to the terminal as output.

[0310] Step 7:

[0311] The user views the ranking result sent by the terminal and considers the properties.

[0312] Specifically, scroll the ranking information displayed on the terminal and tap to view the detailed information.

[0313] Visualize the ranking data sent to the terminal as input and provide the information necessary for the user's decision-making as output.

[0314] (Application Example 1)

[0315] 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".

[0316] In modern real estate searching, a challenge exists in that it is difficult for users to find their ideal property based on their desired conditions. Furthermore, there is a lack of means for users to access relevant property information in real time, even while on the go. As a result, users have to manually search for a large amount of information, which is time-consuming and laborious.

[0317] 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.

[0318] In this invention, the server includes means for receiving user preferences, means for collecting real estate-related data from multiple sources, means for evaluating the characteristics of the surrounding area based on the collected data, means for scoring and ranking real estate based on the evaluation results, means for providing the scored and ranked real estate information to the user, and video presentation means for presenting real estate information to the user while they are on the move. This allows the user to efficiently select their ideal real estate even while on the move, saving time and effort.

[0319] "A means of receiving user preferences" refers to an interface for users to input their desired real estate conditions.

[0320] "Means of collecting real estate-related data from multiple sources" refers to a system that automatically collects relevant data from reliable real estate sources.

[0321] "Means for evaluating the characteristics of the surrounding area based on collected data" refers to a function that analyzes collected information and evaluates the living environment, transportation access, and other aspects of that area.

[0322] "A method for scoring and ranking real estate based on evaluation results" refers to a system that assigns points to each property and ranks them based on the evaluated data.

[0323] "Means of providing users with scored and ranked real estate information" refers to methods of displaying scoring results in a format that users can view.

[0324] "A video presentation method for showing real estate information to users while they are on the move" refers to a device or technology that provides real estate information visually in real time while a user is traveling.

[0325] To realize this invention, the server will build a system that provides real estate information in real time based on the user's desired conditions. The user will input their desired conditions using a smartphone or smart glasses, and this information will be sent to the server. In this process, the interface of the smartphone or smart glasses will function as a means of receiving the user's desired conditions.

[0326] The server uses Python and TensorFlow to collect data from multiple real estate sources and perform property evaluations. Specifically, it evaluates the characteristics of the surrounding environment, the state of safety and security, and the risk of natural disasters, using historical statistical data and real-time environmental data. Furthermore, a Flask-based backend processes and ranks this data.

[0327] The evaluation results are ranked based on the user's priorities and displayed in real time on the smartphone or smart glasses screen. In particular, when the user is on the move, the smart glasses' visual display features provide recommended properties in their vicinity. This allows users to efficiently select the best property even while traveling.

[0328] For example, if a user is searching for a property within Tokyo's 23 wards, they can enter conditions such as "budget of 60 million yen," "3LDK," and "within a 10-minute walk from the station." The server evaluates properties that match these conditions and displays the ranked properties on the smart glasses' display. An example of a prompt message in this case would be, "Based on the conditions you specified (e.g., within Tokyo's 23 wards, budget of 60 million yen), we will score recommended property information and display it on your smartphone."

[0329] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0330] Step 1:

[0331] The user enters their desired property criteria (e.g., area, budget, floor plan, distance from the station) through a smartphone or smart glasses interface. This input data is stored on the device as a means of receiving the user's preferences.

[0332] Step 2:

[0333] The terminal sends the user's entered preferences to the server. This transmitted data becomes the input, and the server prepares for data collection for the next step.

[0334] Step 3:

[0335] The server automatically collects data from multiple real estate sources. This data includes property prices, floor plans, location, and surrounding environment information. The collected data is stored in a database using Python and TensorFlow.

[0336] Step 4:

[0337] The server analyzes the collected data. Specifically, algorithms are executed to evaluate the characteristics of the surrounding area, safety, and natural disaster risk based on the collected real estate information. These evaluations are performed through data processing that combines historical statistical data and real-time data.

[0338] Step 5:

[0339] The server scores each property based on the evaluation results and creates a ranking. This process prioritizes the collected and analyzed data and assigns scores accordingly. The ranking is performed using a backend system built with Flask.

[0340] Step 6:

[0341] The server provides scored and ranked property information to the user's device. This information is displayed on the screen of a smartphone or smart glasses, and the user uses this information to select their ideal property.

[0342] Step 7:

[0343] When a user is on the move, smart glasses visually display recommended properties in their vicinity in real time. Using overlay technology, the information is presented in an intuitive and easy-to-understand manner for users on the go.

[0344] 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.

[0345] This invention relates to a system that assists users in choosing real estate by taking their emotions into consideration. In addition to its basic functions of collecting and evaluating real estate information based on the user's desired conditions and proposing the most suitable properties, this system is equipped with an emotion engine that recognizes the user's emotional state and reflects it in the proposal process.

[0346] First, the user enters their desired property criteria into the terminal. Based on these criteria, the terminal sends the information to the server. The server accesses online real estate information sources and local information databases to collect property information that matches the specified criteria. The collected information includes property price, location, and facilities, as well as surrounding environment, safety information, and natural disaster risk data.

[0347] The emotion engine evaluates user emotions based on direct user feedback, interface usage, voice analysis, and facial expression analysis. For example, if a user displays an unsatisfied facial expression or tone of voice, the server can detect this and modify the evaluation process and suggestions. This information allows the system to strive to provide information in a stress-free manner for the user.

[0348] Let's explain a specific example of its operation. When a user enters conditions such as "Nagano City," "budget of 50 million yen," "4LDK," and "good view," the server searches for properties that meet the conditions, while the emotion engine monitors the user's reaction. If the user shows positive emotions towards the suggested properties, the system analyzes this trend and suggests other similar properties. If the user shows anxiety, the system enhances the information provided, providing detailed explanations of safety assessments and disaster risks.

[0349] In this way, by providing feedback that reflects the user's emotions, this system not only provides information but also reduces the user's psychological burden while supporting them in making the optimal real estate selection decision.

[0350] The following describes the processing flow.

[0351] Step 1:

[0352] The user enters their desired property criteria into the terminal. This includes information such as "region," "budget," "floor plan," and "specific requirements." The terminal then sends the received information to the server.

[0353] Step 2:

[0354] Based on the submitted conditions, the server retrieves property information from multiple real estate sources and related databases. At this stage, the server collects not only basic property information but also detailed data such as the surrounding environment, safety, and disaster risk.

[0355] Step 3:

[0356] The server organizes the collected data based on the user's preferences and scores each property. This scoring reflects the user's priorities, allowing for a presentation optimized to their desired conditions.

[0357] Step 4:

[0358] The emotion engine activates and analyzes the user's emotional state. This is done using facial expression data obtained through interaction with the user and voice analysis. Based on these results, it determines how the user is receiving the property information.

[0359] Step 5:

[0360] The server adjusts its property recommendation strategy based on the user's emotional state, as determined by the emotion engine. For example, if a user shows a negative reaction, the server changes the scoring criteria and recommendation content, and updates the list of candidate properties.

[0361] Step 6:

[0362] The server sends the updated property information to the terminal and presents it to the user. The terminal displays the detailed information and collects the user's response again.

[0363] Step 7:

[0364] Users can review the details of the properties presented and then view further information or contact agents for properties that interest them.

[0365] Step 8:

[0366] If feedback is received from the user regarding their final property selection, the terminal stores that information, and the server uses it to improve future suggestions. This process makes it possible to provide suggestions that are better suited to the user's needs.

[0367] (Example 2)

[0368] 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".

[0369] Conventional real estate information systems typically propose properties based on the user's desired conditions. However, there is a growing need to support users in selecting properties that are more suitable for them by also considering their emotional state. Furthermore, conventional systems often fail to adequately consider safety information and disaster risk information, resulting in insufficient information provision that allows users to choose a place to live with peace of mind.

[0370] 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.

[0371] In this invention, the server includes means for receiving the user's desired conditions, means for collecting property-related information from multiple sources, means for evaluating the characteristics of the area based on the collected information, means for analyzing the user's emotional state and reflecting it in the suggestions, and means for scoring and ranking properties considering the evaluation results and the user's emotions. This makes it possible to suggest optimal properties that reflect the user's emotions, and furthermore, to provide reassuring information that takes into account safety information and disaster risk information.

[0372] "User preferences" refer to information such as the area, budget, floor plan, and other special requests the user has for a property.

[0373] "Information sources" refer to multiple resources that provide real estate and local information data on the internet.

[0374] "Property-related information" includes all details about the property, such as its price, location, facilities, surrounding environment, safety information, and natural disaster risk data.

[0375] "Evaluating the characteristics of a region" means analyzing information about the region, including the surrounding environment, public safety, and natural disaster risks, and conducting a comprehensive evaluation.

[0376] "Analyzing emotional states and reflecting them in proposals" means analyzing the user's emotions from their facial expressions and voice, and incorporating the results into the property proposal process.

[0377] "Scoring and ranking" is a process of numerically evaluating and ranking properties based on evaluation results and user sentiment.

[0378] This real estate selection support system consists of three basic components: the user, the terminal, and the server. The system starts with the user using the terminal to input their desired property criteria. This terminal is equipped with a camera and microphone, and has the function of collecting facial expressions and voice data and sending it to the server. The terminal formats the entered desired criteria and sends the necessary information to the server.

[0379] The server plays a central role in collecting, storing, and analyzing property-related information from multiple sources. These sources include online databases and local information systems. This analysis utilizes dedicated analytical algorithms and generative AI models to score and rank properties based on the collected information. Furthermore, an emotion analysis engine assesses the user's emotional state in real time and incorporates this into the optimal property recommendations.

[0380] For example, if a user enters criteria such as "city center," "under 30 million yen," "pets allowed," and "within a 10-minute walk from the station," the server will search for suitable properties based on these criteria. If the user expresses feelings of joy or satisfaction, it will suggest further properties with similar characteristics. Conversely, if dissatisfaction is detected, the server will provide additional options and detailed information to help the user find a suitable property.

[0381] An example of a prompt message could be: "Please explain how the system works: when a user enters their desired real estate conditions, it suggests the most suitable properties based on those conditions and adjusts the suggestions according to the user's emotional response."

[0382] This system is expected to enable users to make intuitive and rational decisions about choosing real estate that are in line with their emotions, thereby reducing psychological burden.

[0383] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0384] Step 1:

[0385] The user enters their desired property criteria into the terminal. Specifically, they enter information such as location, budget, floor plan, and any special requests. This entered information is formatted and prepared to be sent to the server. The entered criteria serve as the basis for subsequent information gathering and analysis.

[0386] Step 2:

[0387] The server receives the user's desired conditions transmitted from the terminal. Based on the received information, it accesses multiple information sources on the internet and local information databases to collect detailed information about relevant properties. This includes property price, location, amenities, and surrounding environment. The collected information is stored in a database and analyzed later.

[0388] Step 3:

[0389] The server uses an emotion analysis engine to evaluate the user's emotional state. It analyzes voice and facial expression data collected from the device's microphone and camera to quantify the user's emotions. This analysis result is used as data to determine how the user perceives the property information.

[0390] Step 4:

[0391] The server combines collected property information with the user's emotional state and uses a generative AI model to score and rank properties. Real estate information is evaluated based on factors that evoke positive emotions in the user. This results in the ranking of properties that are best suited to the user.

[0392] Step 5:

[0393] The server provides users with scored and ranked property information. The information displayed to users also includes feedback based on the user's emotional state. If the user is highly satisfied, similar properties are suggested; conversely, if there are concerns or dissatisfactions, additional information and improvement suggestions are provided. Based on this information, users can choose their next action.

[0394] (Application Example 2)

[0395] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0396] In modern real estate selection, users must make decisions based on a large amount of information, which can increase their psychological burden. In particular, a user's emotional state greatly influences their choice, but conventional systems often do not take this into account. This invention aims to reduce psychological burden and support the selection of the optimal real estate by analyzing the user's emotional state and providing real estate proposals that reflect it.

[0397] 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.

[0398] In this invention, the server includes means for receiving the user's desired conditions, means for collecting real estate-related data from multiple sources, means for evaluating the characteristics of the surrounding area based on the collected data, and means for analyzing the user's emotional state and reflecting the analysis results in the proposal process. This makes it possible to propose the best real estate that takes the user's emotional state into consideration.

[0399] A "user" refers to an individual or corporation that uses the system to select real estate.

[0400] "Desired conditions" refer to the elements and requirements that users want to consider when choosing a property.

[0401] "Information sources" refer to various public and private databases and websites that provide data related to real estate.

[0402] "Real estate" refers to properties that are subject to buying, selling, or renting, such as houses, land, and commercial facilities.

[0403] "Data" refers to information related to real estate, such as price, location, facilities, surrounding environment, safety information, and natural disaster risk.

[0404] "Characteristics of the surrounding area" refers to various factors of the area surrounding the property, and includes elements for evaluating soundness, convenience, and livability.

[0405] "Evaluation" refers to the act of quantitatively or qualitatively analyzing and judging the characteristics of real estate and the surrounding area based on collected data.

[0406] "Scoring" refers to the process of assigning numerical values ​​to each element of a property based on the evaluation results.

[0407] "Ranking" refers to the process of sorting properties based on their importance and suitability, according to the scoring results.

[0408] "Emotional state" refers to the psychological or emotional response a user shows to a real estate proposal.

[0409] "Analysis" refers to the process of collecting data on users' emotional states, analyzing it, and making judgments based on that data.

[0410] The "proposal process" refers to a series of steps that involve presenting real estate information to users and guiding them to take the next action based on their responses.

[0411] The system for realizing this invention consists of a server, a user terminal, and a consumer robot. The server runs a program that scrapes real estate-related data from publicly available databases on the internet and other information sources. The collected data is processed in Python to analyze the characteristics of the surrounding area and perform various property valuations.

[0412] The user terminal has an interface for entering desired conditions, which are then sent to the server. This interface can be operated by the user via a smartphone or PC. Based on the entered information, the server scores and ranks the most suitable properties.

[0413] On the other hand, consumer robots have the ability to analyze the user's emotional state in real time. They use the Google Speech-to-Text API for speech recognition and OpenCV for facial expression analysis. This emotional analysis information is then reflected in the suggestion process on the server side.

[0414] For example, if a user enters "a quiet place with lots of nature" as their desired condition, the server will list properties that meet that condition with high priority, and a consumer robot will analyze the user's facial expressions to confirm whether the suggestions are appropriate.

[0415] Examples of prompt statements for a generative AI model are as follows:

[0416] "I've been really busy and tired lately, so I want to move to a quiet place where I can relax. But it should ideally be somewhere with good access."

[0417] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0418] Step 1:

[0419] Users use their smartphones or computers to input their desired property criteria into the terminal. This input includes the property's location, price range, floor plan, and specific requests (e.g., quiet neighborhood, natural environment). The terminal then sends this data to the server.

[0420] Step 2:

[0421] The server uses the user's requested criteria as input to access multiple real estate information sources. It uses a Python script to scrape data, collecting detailed property information from the internet. The output is a list of properties that match the specified criteria.

[0422] Step 3:

[0423] The server uses collected data as input to evaluate the characteristics of the surrounding area. This evaluation includes factors such as local safety, transportation convenience, educational environment, and the availability of commercial facilities. The output is an evaluation score for each property, which is used to score and rank real estate.

[0424] Step 4:

[0425] The consumer robot interacts with the user and analyzes their emotional state from their voice and facial expressions. The voice is transcribed using the Google Speech-to-Text API, and facial expressions are analyzed using OpenCV. The input is real-time user emotion data, and the output is a specific emotional state expressed by the user (e.g., joy, anxiety).

[0426] Step 5:

[0427] The server uses the user's emotional state as input to adjust the property information it suggests. The property list is re-ranked based on the emotional state, and information that matches the user's emotions is enhanced and supplemented. The output is an optimized property suggestion list that reflects the emotional state.

[0428] Step 6:

[0429] The user receives optimized real estate suggestions through a consumer robot. The robot verbally explains the details of the suggested properties (e.g., safety rating, natural environment, etc.) and monitors the user's additional emotional changes. The input is a list of suggestions from the server, and the output is the user's final feedback.

[0430] 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.

[0431] 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.

[0432] 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.

[0433] [Third Embodiment]

[0434] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0435] 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.

[0436] 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).

[0437] 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.

[0438] 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.

[0439] 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).

[0440] 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.

[0441] 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.

[0442] 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.

[0443] 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.

[0444] 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.

[0445] 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".

[0446] This invention relates to a system that uses AI to collect and analyze real estate information and propose the most suitable properties to users, enabling them to confidently choose their ideal home. The system consists of a terminal that receives user input and a server that analyzes the information and supports decision-making.

[0447] The user first enters their desired property criteria (e.g., "area," "budget," "floor plan," "distance from station," etc.) into the terminal. The terminal then sends the entered criteria to the server. Based on this information, the server gathers property data that matches the criteria from multiple reliable real estate sources.

[0448] Next, the server analyzes the data collected for each property. In the surrounding environment assessment, it determines whether the area is livable based on available facilities and transportation information. In the safety assessment, it numerically indicates safety based on local crime statistics. For asset value assessment, it estimates future asset value using past real estate transaction data. Furthermore, in the disaster risk assessment, it uses statistical information on earthquake and flood risks to evaluate how well the area can cope with risks.

[0449] Based on these analysis results, the server scores each property and evaluates them in a ranking format. The scores are adjusted according to the user's priority of desired conditions, and the most suitable property is selected overall.

[0450] For example, if a user enters conditions such as "within Tokyo's 23 wards," "budget of 60 million yen," "3LDK," and "within a 10-minute walk from the station," the server will analyze all properties that match these conditions. Taking into account factors such as the safety of the surrounding area and predictions of land price fluctuations, the server will feed back information on the top-ranked properties to the user's terminal. The user can then use this information to check the details and select their ideal property.

[0451] The following describes the processing flow.

[0452] Step 1:

[0453] The user enters their desired criteria into the terminal. These criteria may include "region," "budget," "floor plan," and "distance from the station." The terminal then sends this input information to the server.

[0454] Step 2:

[0455] The server accesses multiple real estate information sources based on the user's requested criteria and collects data on relevant properties. This includes basic information such as price, location, floor plan, and amenities.

[0456] Step 3:

[0457] The server also acquires surrounding environment data related to the collected property information. This includes information about the convenience of facilities such as educational institutions, medical facilities, parks, and transportation infrastructure.

[0458] Step 4:

[0459] The server performs a safety assessment for each property by referring to local crime statistics. This is an analysis based on the area's past crime rate and the location of the nearest police station.

[0460] Step 5:

[0461] The server uses historical real estate transaction data for the target area to assess the future asset value of properties. This is intended to predict fluctuations in land prices and demand trends.

[0462] Step 6:

[0463] The server analyzes risk data such as earthquakes and floods to assess natural disaster risks. In particular, it uses information on ground vulnerability and flood hazard maps.

[0464] Step 7:

[0465] The server comprehensively evaluates each property based on its surrounding environment, safety, asset value, and disaster risk, and assigns a score to each property. The scoring is adjusted according to the user's criteria and priorities.

[0466] Step 8:

[0467] The server creates a ranking based on the score for each property. The ranking displays properties in order of how well they meet the criteria.

[0468] Step 9:

[0469] The server sends the ranking results to the user's device. The device then displays detailed information about the top-ranked properties to the user, assisting them in their selection.

[0470] Step 10:

[0471] Users can review the presented information and select properties that interest them. Based on this information, they can then proceed to the next steps, such as meeting with a real estate agent or visiting the property.

[0472] (Example 1)

[0473] 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."

[0474] When choosing a home, users need to select a suitable residence based on raw data collected from numerous sources. However, this data is vast and requires individual evaluation, placing a significant burden of time and effort on users. Furthermore, the criteria for making selections that consider important factors such as the safety and disaster risk of a residence are unclear, hindering optimal decision-making.

[0475] 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.

[0476] In this invention, the server includes means for receiving the user's living conditions, means for collecting housing information from multiple databases, and means for analyzing the surrounding environment based on the collected information. This automates the process of selecting the optimal residence from a vast amount of housing information based on the user's conditions, and enables a comprehensive evaluation that takes into account important factors such as public safety and disaster risk.

[0477] "Means of receiving user's housing requirements" refers to a function that allows users to input detailed preferences for their desired residence, such as area, price, floor plan, and access, via a terminal, and collects this information as data.

[0478] "Means of collecting housing information from multiple databases" refers to a function that allows you to collect necessary housing information from housing information providers using APIs or data feeds.

[0479] "Means for analyzing the surrounding environment based on collected information" refers to a function that uses collected residential information to analyze and evaluate environmental data (such as nearby facilities, transportation access, and public safety information) related to the area surrounding the property.

[0480] "Means for evaluating and listing residences based on analysis results" refers to a function that quantifies factors such as the surrounding environment, safety, and asset value, ranks residences based on these quantifications, and presents them as a list.

[0481] "Means of providing users with evaluated and listed housing information" refers to a function that displays ranked housing information on the user's device in an easy-to-understand format and presents it in a selectable format.

[0482] "Means of adjusting evaluation results based on user preferences" refers to a function that adjusts the overall score or ranking according to the conditions that users particularly value (such as price, location, and safety).

[0483] This invention helps users efficiently select their ideal residence. The system mainly consists of terminals and servers, and provides users with optimal residence information through the processes of data collection, analysis, and result provision.

[0484] First, the user enters their desired housing conditions through their device. The device receives this data and sends it to the server. The server collects property information from a reliable real estate database via API or data feed. The server then analyzes the collected property information and evaluates details of the surrounding environment, such as nearby facilities, transportation access, and safety information. Based on this, the server performs a multifaceted evaluation of each property, including its asset value and disaster risk, and uses this information to perform scoring and ranking.

[0485] The server sends the scoring results to the terminal, where the user visually confirms the information. Users can smoothly compare and select from the displayed property information. Specifically, if a user provides conditions such as "within Tokyo's 23 wards," "budget of 60 million yen," "3LDK," and "within a 10-minute walk from the station," the server analyzes the corresponding properties in real time, identifies the property that best fits the conditions, and feeds its evaluation back to the terminal.

[0486] By utilizing a generative AI model, scoring can be dynamically adjusted according to each user's specific conditions and priorities. For example, for users who place more importance on asset value, the scoring will take that factor into account. An example prompt might be: "I'm looking for a 3LDK property near a train station in Tokyo's 23 wards, with a budget of under 60 million yen. Please provide a ranking of properties with good safety and promising future asset value." This allows for customized property selection for each user.

[0487] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0488] Step 1:

[0489] The user enters their housing requirements into the terminal.

[0490] Specifically, users enter their desired conditions, such as location, budget, floor plan, and distance from the station, into the terminal's user interface and press the send button.

[0491] Based on the input, the terminal compiles the user's conditions as structured data and generates data packets.

[0492] As output, the prepared data packets are sent to the server.

[0493] Step 2:

[0494] The terminal sends user condition data to the server.

[0495] Specifically, a request is sent to the server using the HTTP protocol, and the server receives the information.

[0496] The system sends conditional data as input to the server and receives an acknowledgment of receipt from the server as output.

[0497] Step 3:

[0498] The server collects property information from multiple databases based on the user's requested conditions.

[0499] In terms of specific operations, the server calls relevant APIs to retrieve property data from reliable real estate sources.

[0500] The system filters the results using user criteria as input, retrieves relevant property information, and prepares the results in a list format.

[0501] The output is a list of property information that matches the criteria.

[0502] Step 4:

[0503] The server analyzes the acquired property information and performs a detailed analysis.

[0504] Specifically, indicators such as the surrounding environment, public safety, property value, and disaster risk are calculated and scored.

[0505] The system analyzes the property information used as input and calculates individual scores based on each indicator.

[0506] The output is a set of scores assigned to each property.

[0507] Step 5:

[0508] The server adjusts the property scores based on the analysis results and ranks them accordingly.

[0509] Specifically, the overall score is recalculated by applying weights based on the user's priority criteria.

[0510] The system uses individual scores and user priority as input to calculate and sort properties in order of priority.

[0511] The output is a ranked list of properties.

[0512] Step 6:

[0513] The server sends the ranking results to the terminal.

[0514] Specifically, the ranking list is converted to JSON format and sent to the terminal as an HTTP response.

[0515] The ranking results, used as input, are formatted and sent to the terminal as output.

[0516] Step 7:

[0517] Users view the ranking results sent via their devices and consider properties.

[0518] Specifically, you scroll through the ranking information displayed on your device and tap to view the details.

[0519] The system visualizes ranking data sent to the terminal as input and provides information necessary for user decision-making as output.

[0520] (Application Example 1)

[0521] 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."

[0522] In modern real estate searching, a challenge exists in that it is difficult for users to find their ideal property based on their desired conditions. Furthermore, there is a lack of means for users to access relevant property information in real time, even while on the go. As a result, users have to manually search for a large amount of information, which is time-consuming and laborious.

[0523] 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.

[0524] In this invention, the server includes means for receiving user preferences, means for collecting real estate-related data from multiple sources, means for evaluating the characteristics of the surrounding area based on the collected data, means for scoring and ranking real estate based on the evaluation results, means for providing the scored and ranked real estate information to the user, and video presentation means for presenting real estate information to the user while they are on the move. This allows the user to efficiently select their ideal real estate even while on the move, saving time and effort.

[0525] "A means of receiving user preferences" refers to an interface for users to input their desired real estate conditions.

[0526] "Means of collecting real estate-related data from multiple sources" refers to a system that automatically collects relevant data from reliable real estate sources.

[0527] "Means for evaluating the characteristics of the surrounding area based on collected data" refers to a function that analyzes collected information and evaluates the living environment, transportation access, and other aspects of that area.

[0528] "A method for scoring and ranking real estate based on evaluation results" refers to a system that assigns points to each property and ranks them based on the evaluated data.

[0529] "Means of providing users with scored and ranked real estate information" refers to methods of displaying scoring results in a format that users can view.

[0530] "A video presentation method for showing real estate information to users while they are on the move" refers to a device or technology that provides real estate information visually in real time while a user is traveling.

[0531] To realize this invention, the server will build a system that provides real estate information in real time based on the user's desired conditions. The user will input their desired conditions using a smartphone or smart glasses, and this information will be sent to the server. In this process, the interface of the smartphone or smart glasses will function as a means of receiving the user's desired conditions.

[0532] The server uses Python and TensorFlow to collect data from multiple real estate sources and perform property evaluations. Specifically, it evaluates the characteristics of the surrounding environment, the state of safety and security, and the risk of natural disasters, using historical statistical data and real-time environmental data. Furthermore, a Flask-based backend processes and ranks this data.

[0533] The evaluation results are ranked based on the user's priorities and displayed in real time on the smartphone or smart glasses screen. In particular, when the user is on the move, the smart glasses' visual display features provide recommended properties in their vicinity. This allows users to efficiently select the best property even while traveling.

[0534] For example, if a user is searching for a property within Tokyo's 23 wards, they can enter conditions such as "budget of 60 million yen," "3LDK," and "within a 10-minute walk from the station." The server evaluates properties that match these conditions and displays the ranked properties on the smart glasses' display. An example of a prompt message in this case would be, "Based on the conditions you specified (e.g., within Tokyo's 23 wards, budget of 60 million yen), we will score recommended property information and display it on your smartphone."

[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 property criteria (e.g., area, budget, floor plan, distance from the station) through a smartphone or smart glasses interface. This input data is stored on the device as a means of receiving the user's preferences.

[0538] Step 2:

[0539] The terminal sends the user's entered preferences to the server. This transmitted data becomes the input, and the server prepares for data collection for the next step.

[0540] Step 3:

[0541] The server automatically collects data from multiple real estate sources. This data includes property prices, floor plans, location, and surrounding environment information. The collected data is stored in a database using Python and TensorFlow.

[0542] Step 4:

[0543] The server analyzes the collected data. Specifically, algorithms are executed to evaluate the characteristics of the surrounding area, safety, and natural disaster risk based on the collected real estate information. These evaluations are performed through data processing that combines historical statistical data and real-time data.

[0544] Step 5:

[0545] The server scores each property based on the evaluation results and creates a ranking. This process prioritizes the collected and analyzed data and assigns scores accordingly. The ranking is performed using a backend system built with Flask.

[0546] Step 6:

[0547] The server provides scored and ranked property information to the user's device. This information is displayed on the screen of a smartphone or smart glasses, and the user uses this information to select their ideal property.

[0548] Step 7:

[0549] When a user is on the move, smart glasses visually display recommended properties in their vicinity in real time. Using overlay technology, the information is presented in an intuitive and easy-to-understand manner for users on the go.

[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] This invention relates to a system that assists users in choosing real estate by taking their emotions into consideration. In addition to its basic functions of collecting and evaluating real estate information based on the user's desired conditions and proposing the most suitable properties, this system is equipped with an emotion engine that recognizes the user's emotional state and reflects it in the proposal process.

[0552] First, the user enters their desired property criteria into the terminal. Based on these criteria, the terminal sends the information to the server. The server accesses online real estate information sources and local information databases to collect property information that matches the specified criteria. The collected information includes property price, location, and facilities, as well as surrounding environment, safety information, and natural disaster risk data.

[0553] The emotion engine evaluates user emotions based on direct user feedback, interface usage, voice analysis, and facial expression analysis. For example, if a user displays an unsatisfied facial expression or tone of voice, the server can detect this and modify the evaluation process and suggestions. This information allows the system to strive to provide information in a stress-free manner for the user.

[0554] Let's explain a specific example of its operation. When a user enters conditions such as "Nagano City," "budget of 50 million yen," "4LDK," and "good view," the server searches for properties that meet the conditions, while the emotion engine monitors the user's reaction. If the user shows positive emotions towards the suggested properties, the system analyzes this trend and suggests other similar properties. If the user shows anxiety, the system enhances the information provided, providing detailed explanations of safety assessments and disaster risks.

[0555] In this way, by providing feedback that reflects the user's emotions, this system not only provides information but also reduces the user's psychological burden while supporting them in making the optimal real estate selection decision.

[0556] The following describes the processing flow.

[0557] Step 1:

[0558] The user enters their desired property criteria into the terminal. This includes information such as "region," "budget," "floor plan," and "specific requirements." The terminal then sends the received information to the server.

[0559] Step 2:

[0560] Based on the submitted conditions, the server retrieves property information from multiple real estate sources and related databases. At this stage, the server collects not only basic property information but also detailed data such as the surrounding environment, safety, and disaster risk.

[0561] Step 3:

[0562] The server organizes the collected data based on the user's preferences and scores each property. This scoring reflects the user's priorities, allowing for a presentation optimized to their desired conditions.

[0563] Step 4:

[0564] The emotion engine activates and analyzes the user's emotional state. This is done using facial expression data obtained through interaction with the user and voice analysis. Based on these results, it determines how the user is receiving the property information.

[0565] Step 5:

[0566] The server adjusts its property recommendation strategy based on the user's emotional state, as determined by the emotion engine. For example, if a user shows a negative reaction, the server changes the scoring criteria and recommendation content, and updates the list of candidate properties.

[0567] Step 6:

[0568] The server sends the updated property information to the terminal and presents it to the user. The terminal displays the detailed information and collects the user's response again.

[0569] Step 7:

[0570] Users can review the details of the properties presented and then view further information or contact agents for properties that interest them.

[0571] Step 8:

[0572] If feedback is received from the user regarding their final property selection, the terminal stores that information, and the server uses it to improve future suggestions. This process makes it possible to provide suggestions that are better suited to the user's needs.

[0573] (Example 2)

[0574] 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."

[0575] Conventional real estate information systems typically propose properties based on the user's desired conditions. However, there is a growing need to support users in selecting properties that are more suitable for them by also considering their emotional state. Furthermore, conventional systems often fail to adequately consider safety information and disaster risk information, resulting in insufficient information provision that allows users to choose a place to live with peace of mind.

[0576] 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.

[0577] In this invention, the server includes means for receiving the user's desired conditions, means for collecting property-related information from multiple sources, means for evaluating the characteristics of the area based on the collected information, means for analyzing the user's emotional state and reflecting it in the suggestions, and means for scoring and ranking properties considering the evaluation results and the user's emotions. This makes it possible to suggest optimal properties that reflect the user's emotions, and furthermore, to provide reassuring information that takes into account safety information and disaster risk information.

[0578] "User preferences" refer to information such as the area, budget, floor plan, and other special requests the user has for a property.

[0579] "Information sources" refer to multiple resources that provide real estate and local information data on the internet.

[0580] "Property-related information" includes all details about the property, such as its price, location, facilities, surrounding environment, safety information, and natural disaster risk data.

[0581] "Evaluating the characteristics of a region" means analyzing information about the region, including the surrounding environment, public safety, and natural disaster risks, and conducting a comprehensive evaluation.

[0582] "Analyzing emotional states and reflecting them in proposals" means analyzing the user's emotions from their facial expressions and voice, and incorporating the results into the property proposal process.

[0583] "Scoring and ranking" is a process of numerically evaluating and ranking properties based on evaluation results and user sentiment.

[0584] This real estate selection support system consists of three basic components: the user, the terminal, and the server. The system starts with the user using the terminal to input their desired property criteria. This terminal is equipped with a camera and microphone, and has the function of collecting facial expressions and voice data and sending it to the server. The terminal formats the entered desired criteria and sends the necessary information to the server.

[0585] The server plays a central role in collecting, storing, and analyzing property-related information from multiple sources. These sources include online databases and local information systems. This analysis utilizes dedicated analytical algorithms and generative AI models to score and rank properties based on the collected information. Furthermore, an emotion analysis engine assesses the user's emotional state in real time and incorporates this into the optimal property recommendations.

[0586] For example, if a user enters criteria such as "city center," "under 30 million yen," "pets allowed," and "within a 10-minute walk from the station," the server will search for suitable properties based on these criteria. If the user expresses feelings of joy or satisfaction, it will suggest further properties with similar characteristics. Conversely, if dissatisfaction is detected, the server will provide additional options and detailed information to help the user find a suitable property.

[0587] An example of a prompt message could be: "Please explain how the system works: when a user enters their desired real estate conditions, it suggests the most suitable properties based on those conditions and adjusts the suggestions according to the user's emotional response."

[0588] This system is expected to enable users to make intuitive and rational decisions about choosing real estate that are in line with their emotions, thereby reducing psychological burden.

[0589] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0590] Step 1:

[0591] The user enters their desired property criteria into the terminal. Specifically, they enter information such as location, budget, floor plan, and any special requests. This entered information is formatted and prepared to be sent to the server. The entered criteria serve as the basis for subsequent information gathering and analysis.

[0592] Step 2:

[0593] The server receives the user's desired conditions transmitted from the terminal. Based on the received information, it accesses multiple information sources on the internet and local information databases to collect detailed information about relevant properties. This includes property price, location, amenities, and surrounding environment. The collected information is stored in a database and analyzed later.

[0594] Step 3:

[0595] The server uses an emotion analysis engine to evaluate the user's emotional state. It analyzes voice and facial expression data collected from the device's microphone and camera to quantify the user's emotions. This analysis result is used as data to determine how the user perceives the property information.

[0596] Step 4:

[0597] The server combines collected property information with the user's emotional state and uses a generative AI model to score and rank properties. Real estate information is evaluated based on factors that evoke positive emotions in the user. This results in the ranking of properties that are best suited to the user.

[0598] Step 5:

[0599] The server provides users with scored and ranked property information. The information displayed to users also includes feedback based on the user's emotional state. If the user is highly satisfied, similar properties are suggested; conversely, if there are concerns or dissatisfactions, additional information and improvement suggestions are provided. Based on this information, users can choose their next action.

[0600] (Application Example 2)

[0601] 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."

[0602] In modern real estate selection, users must make decisions based on a large amount of information, which can increase their psychological burden. In particular, a user's emotional state greatly influences their choice, but conventional systems often do not take this into account. This invention aims to reduce psychological burden and support the selection of the optimal real estate by analyzing the user's emotional state and providing real estate proposals that reflect it.

[0603] 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.

[0604] In this invention, the server includes means for receiving the user's desired conditions, means for collecting real estate-related data from multiple sources, means for evaluating the characteristics of the surrounding area based on the collected data, and means for analyzing the user's emotional state and reflecting the analysis results in the proposal process. This makes it possible to propose the best real estate that takes the user's emotional state into consideration.

[0605] A "user" refers to an individual or corporation that uses the system to select real estate.

[0606] "Desired conditions" refer to the elements and requirements that users want to consider when choosing a property.

[0607] "Information sources" refer to various public and private databases and websites that provide data related to real estate.

[0608] "Real estate" refers to properties that are subject to buying, selling, or renting, such as houses, land, and commercial facilities.

[0609] "Data" refers to information related to real estate, such as price, location, facilities, surrounding environment, safety information, and natural disaster risk.

[0610] "Characteristics of the surrounding area" refers to various factors of the area surrounding the property, and includes elements for evaluating soundness, convenience, and livability.

[0611] "Evaluation" refers to the act of quantitatively or qualitatively analyzing and judging the characteristics of real estate and the surrounding area based on collected data.

[0612] "Scoring" refers to the process of assigning numerical values ​​to each element of a property based on the evaluation results.

[0613] "Ranking" refers to the process of sorting properties based on their importance and suitability, according to the scoring results.

[0614] "Emotional state" refers to the psychological or emotional response a user shows to a real estate proposal.

[0615] "Analysis" refers to the process of collecting data on users' emotional states, analyzing it, and making judgments based on that data.

[0616] The "proposal process" refers to a series of steps that involve presenting real estate information to users and guiding them to take the next action based on their responses.

[0617] The system for realizing this invention consists of a server, a user terminal, and a consumer robot. The server runs a program that scrapes real estate-related data from publicly available databases on the internet and other information sources. The collected data is processed in Python to analyze the characteristics of the surrounding area and perform various property valuations.

[0618] The user terminal has an interface for entering desired conditions, which are then sent to the server. This interface can be operated by the user via a smartphone or PC. Based on the entered information, the server scores and ranks the most suitable properties.

[0619] On the other hand, consumer robots have the ability to analyze the user's emotional state in real time. They use the Google Speech-to-Text API for speech recognition and OpenCV for facial expression analysis. This emotional analysis information is then reflected in the suggestion process on the server side.

[0620] For example, if a user enters "a quiet place with lots of nature" as their desired condition, the server will list properties that meet that condition with high priority, and a consumer robot will analyze the user's facial expressions to confirm whether the suggestions are appropriate.

[0621] Examples of prompt statements for a generative AI model are as follows:

[0622] "I've been really busy and tired lately, so I want to move to a quiet place where I can relax. But it should ideally be somewhere with good access."

[0623] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0624] Step 1:

[0625] Users use their smartphones or computers to input their desired property criteria into the terminal. This input includes the property's location, price range, floor plan, and specific requests (e.g., quiet neighborhood, natural environment). The terminal then sends this data to the server.

[0626] Step 2:

[0627] The server uses the user's requested criteria as input to access multiple real estate information sources. It uses a Python script to scrape data, collecting detailed property information from the internet. The output is a list of properties that match the specified criteria.

[0628] Step 3:

[0629] The server uses collected data as input to evaluate the characteristics of the surrounding area. This evaluation includes factors such as local safety, transportation convenience, educational environment, and the availability of commercial facilities. The output is an evaluation score for each property, which is used to score and rank real estate.

[0630] Step 4:

[0631] The consumer robot interacts with the user and analyzes their emotional state from their voice and facial expressions. The voice is transcribed using the Google Speech-to-Text API, and facial expressions are analyzed using OpenCV. The input is real-time user emotion data, and the output is a specific emotional state expressed by the user (e.g., joy, anxiety).

[0632] Step 5:

[0633] The server uses the user's emotional state as input to adjust the property information it suggests. The property list is re-ranked based on the emotional state, and information that matches the user's emotions is enhanced and supplemented. The output is an optimized property suggestion list that reflects the emotional state.

[0634] Step 6:

[0635] The user receives optimized real estate suggestions through a consumer robot. The robot verbally explains the details of the suggested properties (e.g., safety rating, natural environment, etc.) and monitors the user's additional emotional changes. The input is a list of suggestions from the server, and the output is the user's final feedback.

[0636] 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.

[0637] 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.

[0638] 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.

[0639] [Fourth Embodiment]

[0640] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0641] 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.

[0642] 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).

[0643] 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.

[0644] 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.

[0645] 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).

[0646] 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.

[0647] 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.

[0648] 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.

[0649] 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.

[0650] 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.

[0651] 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.

[0652] 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".

[0653] This invention relates to a system that uses AI to collect and analyze real estate information and propose the most suitable properties to users, enabling them to confidently choose their ideal home. The system consists of a terminal that receives user input and a server that analyzes the information and supports decision-making.

[0654] The user first enters their desired property criteria (e.g., "area," "budget," "floor plan," "distance from station," etc.) into the terminal. The terminal then sends the entered criteria to the server. Based on this information, the server gathers property data that matches the criteria from multiple reliable real estate sources.

[0655] Next, the server analyzes the data collected for each property. In the surrounding environment assessment, it determines whether the area is livable based on available facilities and transportation information. In the safety assessment, it numerically indicates safety based on local crime statistics. For asset value assessment, it estimates future asset value using past real estate transaction data. Furthermore, in the disaster risk assessment, it uses statistical information on earthquake and flood risks to evaluate how well the area can cope with risks.

[0656] Based on these analysis results, the server scores each property and evaluates them in a ranking format. The scores are adjusted according to the user's priority of desired conditions, and the most suitable property is selected overall.

[0657] For example, if a user enters conditions such as "within Tokyo's 23 wards," "budget of 60 million yen," "3LDK," and "within a 10-minute walk from the station," the server will analyze all properties that match these conditions. Taking into account factors such as the safety of the surrounding area and predictions of land price fluctuations, the server will feed back information on the top-ranked properties to the user's terminal. The user can then use this information to check the details and select their ideal property.

[0658] The following describes the processing flow.

[0659] Step 1:

[0660] The user enters their desired criteria into the terminal. These criteria may include "region," "budget," "floor plan," and "distance from the station." The terminal then sends this input information to the server.

[0661] Step 2:

[0662] The server accesses multiple real estate information sources based on the user's requested criteria and collects data on relevant properties. This includes basic information such as price, location, floor plan, and amenities.

[0663] Step 3:

[0664] The server also acquires surrounding environment data related to the collected property information. This includes information about the convenience of facilities such as educational institutions, medical facilities, parks, and transportation infrastructure.

[0665] Step 4:

[0666] The server performs a safety assessment for each property by referring to local crime statistics. This is an analysis based on the area's past crime rate and the location of the nearest police station.

[0667] Step 5:

[0668] The server uses historical real estate transaction data for the target area to assess the future asset value of properties. This is intended to predict fluctuations in land prices and demand trends.

[0669] Step 6:

[0670] The server analyzes risk data such as earthquakes and floods to assess natural disaster risks. In particular, it uses information on ground vulnerability and flood hazard maps.

[0671] Step 7:

[0672] The server comprehensively evaluates each property based on its surrounding environment, safety, asset value, and disaster risk, and assigns a score to each property. The scoring is adjusted according to the user's criteria and priorities.

[0673] Step 8:

[0674] The server creates a ranking based on the score for each property. The ranking displays properties in order of how well they meet the criteria.

[0675] Step 9:

[0676] The server sends the ranking results to the user's device. The device then displays detailed information about the top-ranked properties to the user, assisting them in their selection.

[0677] Step 10:

[0678] Users can review the presented information and select properties that interest them. Based on this information, they can then proceed to the next steps, such as meeting with a real estate agent or visiting the property.

[0679] (Example 1)

[0680] 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".

[0681] When choosing a home, users need to select a suitable residence based on raw data collected from numerous sources. However, this data is vast and requires individual evaluation, placing a significant burden of time and effort on users. Furthermore, the criteria for making selections that consider important factors such as the safety and disaster risk of a residence are unclear, hindering optimal decision-making.

[0682] 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.

[0683] In this invention, the server includes means for receiving the user's living conditions, means for collecting housing information from multiple databases, and means for analyzing the surrounding environment based on the collected information. This automates the process of selecting the optimal residence from a vast amount of housing information based on the user's conditions, and enables a comprehensive evaluation that takes into account important factors such as public safety and disaster risk.

[0684] "Means of receiving user's housing requirements" refers to a function that allows users to input detailed preferences for their desired residence, such as area, price, floor plan, and access, via a terminal, and collects this information as data.

[0685] "Means of collecting housing information from multiple databases" refers to a function that allows you to collect necessary housing information from housing information providers using APIs or data feeds.

[0686] "Means for analyzing the surrounding environment based on collected information" refers to a function that uses collected residential information to analyze and evaluate environmental data (such as nearby facilities, transportation access, and public safety information) related to the area surrounding the property.

[0687] "Means for evaluating and listing residences based on analysis results" refers to a function that quantifies factors such as the surrounding environment, safety, and asset value, ranks residences based on these quantifications, and presents them as a list.

[0688] "Means of providing users with evaluated and listed housing information" refers to a function that displays ranked housing information on the user's device in an easy-to-understand format and presents it in a selectable format.

[0689] "Means of adjusting evaluation results based on user preferences" refers to a function that adjusts the overall score or ranking according to the conditions that users particularly value (such as price, location, and safety).

[0690] This invention helps users efficiently select their ideal residence. The system mainly consists of terminals and servers, and provides users with optimal residence information through the processes of data collection, analysis, and result provision.

[0691] First, the user enters their desired housing conditions through their device. The device receives this data and sends it to the server. The server collects property information from a reliable real estate database via API or data feed. The server then analyzes the collected property information and evaluates details of the surrounding environment, such as nearby facilities, transportation access, and safety information. Based on this, the server performs a multifaceted evaluation of each property, including its asset value and disaster risk, and uses this information to perform scoring and ranking.

[0692] The server sends the scoring results to the terminal, where the user visually confirms the information. Users can smoothly compare and select from the displayed property information. Specifically, if a user provides conditions such as "within Tokyo's 23 wards," "budget of 60 million yen," "3LDK," and "within a 10-minute walk from the station," the server analyzes the corresponding properties in real time, identifies the property that best fits the conditions, and feeds its evaluation back to the terminal.

[0693] By utilizing a generative AI model, scoring can be dynamically adjusted according to each user's specific conditions and priorities. For example, for users who place more importance on asset value, the scoring will take that factor into account. An example prompt might be: "I'm looking for a 3LDK property near a train station in Tokyo's 23 wards, with a budget of under 60 million yen. Please provide a ranking of properties with good safety and promising future asset value." This allows for customized property selection for each user.

[0694] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0695] Step 1:

[0696] The user enters their housing requirements into the terminal.

[0697] Specifically, users enter their desired conditions, such as location, budget, floor plan, and distance from the station, into the terminal's user interface and press the send button.

[0698] Based on the input, the terminal compiles the user's conditions as structured data and generates data packets.

[0699] As output, the prepared data packets are sent to the server.

[0700] Step 2:

[0701] The terminal sends user condition data to the server.

[0702] Specifically, a request is sent to the server using the HTTP protocol, and the server receives the information.

[0703] The system sends conditional data as input to the server and receives an acknowledgment of receipt from the server as output.

[0704] Step 3:

[0705] The server collects property information from multiple databases based on the user's requested conditions.

[0706] In terms of specific operations, the server calls relevant APIs to retrieve property data from reliable real estate sources.

[0707] The system filters the results using user criteria as input, retrieves relevant property information, and prepares the results in a list format.

[0708] The output is a list of property information that matches the criteria.

[0709] Step 4:

[0710] The server analyzes the acquired property information and performs a detailed analysis.

[0711] Specifically, indicators such as the surrounding environment, public safety, property value, and disaster risk are calculated and scored.

[0712] The system analyzes the property information used as input and calculates individual scores based on each indicator.

[0713] The output is a set of scores assigned to each property.

[0714] Step 5:

[0715] The server adjusts the property scores based on the analysis results and ranks them accordingly.

[0716] Specifically, the overall score is recalculated by applying weights based on the user's priority criteria.

[0717] The system uses individual scores and user priority as input to calculate and sort properties in order of priority.

[0718] The output is a ranked list of properties.

[0719] Step 6:

[0720] The server sends the ranking results to the terminal.

[0721] Specifically, the ranking list is converted to JSON format and sent to the terminal as an HTTP response.

[0722] The ranking results, used as input, are formatted and sent to the terminal as output.

[0723] Step 7:

[0724] Users view the ranking results sent via their devices and consider properties.

[0725] Specifically, you scroll through the ranking information displayed on your device and tap to view the details.

[0726] The system visualizes ranking data sent to the terminal as input and provides information necessary for user decision-making as output.

[0727] (Application Example 1)

[0728] 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".

[0729] In modern real estate searching, a challenge exists in that it is difficult for users to find their ideal property based on their desired conditions. Furthermore, there is a lack of means for users to access relevant property information in real time, even while on the go. As a result, users have to manually search for a large amount of information, which is time-consuming and laborious.

[0730] 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.

[0731] In this invention, the server includes means for receiving user preferences, means for collecting real estate-related data from multiple sources, means for evaluating the characteristics of the surrounding area based on the collected data, means for scoring and ranking real estate based on the evaluation results, means for providing the scored and ranked real estate information to the user, and video presentation means for presenting real estate information to the user while they are on the move. This allows the user to efficiently select their ideal real estate even while on the move, saving time and effort.

[0732] "A means of receiving user preferences" refers to an interface for users to input their desired real estate conditions.

[0733] "Means of collecting real estate-related data from multiple sources" refers to a system that automatically collects relevant data from reliable real estate sources.

[0734] "Means for evaluating the characteristics of the surrounding area based on collected data" refers to a function that analyzes collected information and evaluates the living environment, transportation access, and other aspects of that area.

[0735] "A method for scoring and ranking real estate based on evaluation results" refers to a system that assigns points to each property and ranks them based on the evaluated data.

[0736] "Means of providing users with scored and ranked real estate information" refers to methods of displaying scoring results in a format that users can view.

[0737] "A video presentation method for showing real estate information to users while they are on the move" refers to a device or technology that provides real estate information visually in real time while a user is traveling.

[0738] To realize this invention, the server will build a system that provides real estate information in real time based on the user's desired conditions. The user will input their desired conditions using a smartphone or smart glasses, and this information will be sent to the server. In this process, the interface of the smartphone or smart glasses will function as a means of receiving the user's desired conditions.

[0739] The server uses Python and TensorFlow to collect data from multiple real estate sources and perform property evaluations. Specifically, it evaluates the characteristics of the surrounding environment, the state of safety and security, and the risk of natural disasters, using historical statistical data and real-time environmental data. Furthermore, a Flask-based backend processes and ranks this data.

[0740] The evaluation results are ranked based on the user's priorities and displayed in real time on the smartphone or smart glasses screen. In particular, when the user is on the move, the smart glasses' visual display features provide recommended properties in their vicinity. This allows users to efficiently select the best property even while traveling.

[0741] For example, if a user is searching for a property within Tokyo's 23 wards, they can enter conditions such as "budget of 60 million yen," "3LDK," and "within a 10-minute walk from the station." The server evaluates properties that match these conditions and displays the ranked properties on the smart glasses' display. An example of a prompt message in this case would be, "Based on the conditions you specified (e.g., within Tokyo's 23 wards, budget of 60 million yen), we will score recommended property information and display it on your smartphone."

[0742] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0743] Step 1:

[0744] The user enters their desired property criteria (e.g., area, budget, floor plan, distance from the station) through a smartphone or smart glasses interface. This input data is stored on the device as a means of receiving the user's preferences.

[0745] Step 2:

[0746] The terminal sends the user's entered preferences to the server. This transmitted data becomes the input, and the server prepares for data collection for the next step.

[0747] Step 3:

[0748] The server automatically collects data from multiple real estate sources. This data includes property prices, floor plans, location, and surrounding environment information. The collected data is stored in a database using Python and TensorFlow.

[0749] Step 4:

[0750] The server analyzes the collected data. Specifically, algorithms are executed to evaluate the characteristics of the surrounding area, safety, and natural disaster risk based on the collected real estate information. These evaluations are performed through data processing that combines historical statistical data and real-time data.

[0751] Step 5:

[0752] The server scores each property based on the evaluation results and creates a ranking. This process prioritizes the collected and analyzed data and assigns scores accordingly. The ranking is performed using a backend system built with Flask.

[0753] Step 6:

[0754] The server provides scored and ranked property information to the user's device. This information is displayed on the screen of a smartphone or smart glasses, and the user uses this information to select their ideal property.

[0755] Step 7:

[0756] When a user is on the move, smart glasses visually display recommended properties in their vicinity in real time. Using overlay technology, the information is presented in an intuitive and easy-to-understand manner for users on the go.

[0757] 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.

[0758] This invention relates to a system that assists users in choosing real estate by taking their emotions into consideration. In addition to its basic functions of collecting and evaluating real estate information based on the user's desired conditions and proposing the most suitable properties, this system is equipped with an emotion engine that recognizes the user's emotional state and reflects it in the proposal process.

[0759] First, the user enters their desired property criteria into the terminal. Based on these criteria, the terminal sends the information to the server. The server accesses online real estate information sources and local information databases to collect property information that matches the specified criteria. The collected information includes property price, location, and facilities, as well as surrounding environment, safety information, and natural disaster risk data.

[0760] The emotion engine evaluates user emotions based on direct user feedback, interface usage, voice analysis, and facial expression analysis. For example, if a user displays an unsatisfied facial expression or tone of voice, the server can detect this and modify the evaluation process and suggestions. This information allows the system to strive to provide information in a stress-free manner for the user.

[0761] Let's explain a specific example of its operation. When a user enters conditions such as "Nagano City," "budget of 50 million yen," "4LDK," and "good view," the server searches for properties that meet the conditions, while the emotion engine monitors the user's reaction. If the user shows positive emotions towards the suggested properties, the system analyzes this trend and suggests other similar properties. If the user shows anxiety, the system enhances the information provided, providing detailed explanations of safety assessments and disaster risks.

[0762] In this way, by providing feedback that reflects the user's emotions, this system not only provides information but also reduces the user's psychological burden while supporting them in making the optimal real estate selection decision.

[0763] The following describes the processing flow.

[0764] Step 1:

[0765] The user enters their desired property criteria into the terminal. This includes information such as "region," "budget," "floor plan," and "specific requirements." The terminal then sends the received information to the server.

[0766] Step 2:

[0767] Based on the submitted conditions, the server retrieves property information from multiple real estate sources and related databases. At this stage, the server collects not only basic property information but also detailed data such as the surrounding environment, safety, and disaster risk.

[0768] Step 3:

[0769] The server organizes the collected data based on the user's preferences and scores each property. This scoring reflects the user's priorities, allowing for a presentation optimized to their desired conditions.

[0770] Step 4:

[0771] The emotion engine activates and analyzes the user's emotional state. This is done using facial expression data obtained through interaction with the user and voice analysis. Based on these results, it determines how the user is receiving the property information.

[0772] Step 5:

[0773] The server adjusts its property recommendation strategy based on the user's emotional state, as determined by the emotion engine. For example, if a user shows a negative reaction, the server changes the scoring criteria and recommendation content, and updates the list of candidate properties.

[0774] Step 6:

[0775] The server sends the updated property information to the terminal and presents it to the user. The terminal displays the detailed information and collects the user's response again.

[0776] Step 7:

[0777] Users can review the details of the properties presented and then view further information or contact agents for properties that interest them.

[0778] Step 8:

[0779] If feedback is received from the user regarding their final property selection, the terminal stores that information, and the server uses it to improve future suggestions. This process makes it possible to provide suggestions that are better suited to the user's needs.

[0780] (Example 2)

[0781] 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".

[0782] Conventional real estate information systems typically propose properties based on the user's desired conditions. However, there is a growing need to support users in selecting properties that are more suitable for them by also considering their emotional state. Furthermore, conventional systems often fail to adequately consider safety information and disaster risk information, resulting in insufficient information provision that allows users to choose a place to live with peace of mind.

[0783] 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.

[0784] In this invention, the server includes means for receiving the user's desired conditions, means for collecting property-related information from multiple sources, means for evaluating the characteristics of the area based on the collected information, means for analyzing the user's emotional state and reflecting it in the suggestions, and means for scoring and ranking properties considering the evaluation results and the user's emotions. This makes it possible to suggest optimal properties that reflect the user's emotions, and furthermore, to provide reassuring information that takes into account safety information and disaster risk information.

[0785] "User preferences" refer to information such as the area, budget, floor plan, and other special requests the user has for a property.

[0786] "Information sources" refer to multiple resources that provide real estate and local information data on the internet.

[0787] "Property-related information" includes all details about the property, such as its price, location, facilities, surrounding environment, safety information, and natural disaster risk data.

[0788] "Evaluating the characteristics of a region" means analyzing information about the region, including the surrounding environment, public safety, and natural disaster risks, and conducting a comprehensive evaluation.

[0789] "Analyzing emotional states and reflecting them in proposals" means analyzing the user's emotions from their facial expressions and voice, and incorporating the results into the property proposal process.

[0790] "Scoring and ranking" is a process of numerically evaluating and ranking properties based on evaluation results and user sentiment.

[0791] This real estate selection support system consists of three basic components: the user, the terminal, and the server. The system starts with the user using the terminal to input their desired property criteria. This terminal is equipped with a camera and microphone, and has the function of collecting facial expressions and voice data and sending it to the server. The terminal formats the entered desired criteria and sends the necessary information to the server.

[0792] The server plays a central role in collecting, storing, and analyzing property-related information from multiple sources. These sources include online databases and local information systems. This analysis utilizes dedicated analytical algorithms and generative AI models to score and rank properties based on the collected information. Furthermore, an emotion analysis engine assesses the user's emotional state in real time and incorporates this into the optimal property recommendations.

[0793] For example, if a user enters criteria such as "city center," "under 30 million yen," "pets allowed," and "within a 10-minute walk from the station," the server will search for suitable properties based on these criteria. If the user expresses feelings of joy or satisfaction, it will suggest further properties with similar characteristics. Conversely, if dissatisfaction is detected, the server will provide additional options and detailed information to help the user find a suitable property.

[0794] An example of a prompt message could be: "Please explain how the system works: when a user enters their desired real estate conditions, it suggests the most suitable properties based on those conditions and adjusts the suggestions according to the user's emotional response."

[0795] This system is expected to enable users to make intuitive and rational decisions about choosing real estate that are in line with their emotions, thereby reducing psychological burden.

[0796] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0797] Step 1:

[0798] The user enters their desired property criteria into the terminal. Specifically, they enter information such as location, budget, floor plan, and any special requests. This entered information is formatted and prepared to be sent to the server. The entered criteria serve as the basis for subsequent information gathering and analysis.

[0799] Step 2:

[0800] The server receives the user's desired conditions transmitted from the terminal. Based on the received information, it accesses multiple information sources on the internet and local information databases to collect detailed information about relevant properties. This includes property price, location, amenities, and surrounding environment. The collected information is stored in a database and analyzed later.

[0801] Step 3:

[0802] The server uses an emotion analysis engine to evaluate the user's emotional state. It analyzes voice and facial expression data collected from the device's microphone and camera to quantify the user's emotions. This analysis result is used as data to determine how the user perceives the property information.

[0803] Step 4:

[0804] The server combines collected property information with the user's emotional state and uses a generative AI model to score and rank properties. Real estate information is evaluated based on factors that evoke positive emotions in the user. This results in the ranking of properties that are best suited to the user.

[0805] Step 5:

[0806] The server provides users with scored and ranked property information. The information displayed to users also includes feedback based on the user's emotional state. If the user is highly satisfied, similar properties are suggested; conversely, if there are concerns or dissatisfactions, additional information and improvement suggestions are provided. Based on this information, users can choose their next action.

[0807] (Application Example 2)

[0808] 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".

[0809] In modern real estate selection, users must make decisions based on a large amount of information, which can increase their psychological burden. In particular, a user's emotional state greatly influences their choice, but conventional systems often do not take this into account. This invention aims to reduce psychological burden and support the selection of the optimal real estate by analyzing the user's emotional state and providing real estate proposals that reflect it.

[0810] 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.

[0811] In this invention, the server includes means for receiving the user's desired conditions, means for collecting real estate-related data from multiple sources, means for evaluating the characteristics of the surrounding area based on the collected data, and means for analyzing the user's emotional state and reflecting the analysis results in the proposal process. This makes it possible to propose the best real estate that takes the user's emotional state into consideration.

[0812] A "user" refers to an individual or corporation that uses the system to select real estate.

[0813] "Desired conditions" refer to the elements and requirements that users want to consider when choosing a property.

[0814] "Information sources" refer to various public and private databases and websites that provide data related to real estate.

[0815] "Real estate" refers to properties that are subject to buying, selling, or renting, such as houses, land, and commercial facilities.

[0816] "Data" refers to information related to real estate, such as price, location, facilities, surrounding environment, safety information, and natural disaster risk.

[0817] "Characteristics of the surrounding area" refers to various factors of the area surrounding the property, and includes elements for evaluating soundness, convenience, and livability.

[0818] "Evaluation" refers to the act of quantitatively or qualitatively analyzing and judging the characteristics of real estate and the surrounding area based on collected data.

[0819] "Scoring" refers to the process of assigning numerical values ​​to each element of a property based on the evaluation results.

[0820] "Ranking" refers to the process of sorting properties based on their importance and suitability, according to the scoring results.

[0821] "Emotional state" refers to the psychological or emotional response a user shows to a real estate proposal.

[0822] "Analysis" refers to the process of collecting data on users' emotional states, analyzing it, and making judgments based on that data.

[0823] The "proposal process" refers to a series of steps that involve presenting real estate information to users and guiding them to take the next action based on their responses.

[0824] The system for realizing this invention consists of a server, a user terminal, and a consumer robot. The server runs a program that scrapes real estate-related data from publicly available databases on the internet and other information sources. The collected data is processed in Python to analyze the characteristics of the surrounding area and perform various property valuations.

[0825] The user terminal has an interface for entering desired conditions, which are then sent to the server. This interface can be operated by the user via a smartphone or PC. Based on the entered information, the server scores and ranks the most suitable properties.

[0826] On the other hand, consumer robots have the ability to analyze the user's emotional state in real time. They use the Google Speech-to-Text API for speech recognition and OpenCV for facial expression analysis. This emotional analysis information is then reflected in the suggestion process on the server side.

[0827] For example, if a user enters "a quiet place with lots of nature" as their desired condition, the server will list properties that meet that condition with high priority, and a consumer robot will analyze the user's facial expressions to confirm whether the suggestions are appropriate.

[0828] Examples of prompt statements for a generative AI model are as follows:

[0829] "I've been really busy and tired lately, so I want to move to a quiet place where I can relax. But it should ideally be somewhere with good access."

[0830] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0831] Step 1:

[0832] Users use their smartphones or computers to input their desired property criteria into the terminal. This input includes the property's location, price range, floor plan, and specific requests (e.g., quiet neighborhood, natural environment). The terminal then sends this data to the server.

[0833] Step 2:

[0834] The server uses the user's requested criteria as input to access multiple real estate information sources. It uses a Python script to scrape data, collecting detailed property information from the internet. The output is a list of properties that match the specified criteria.

[0835] Step 3:

[0836] The server uses collected data as input to evaluate the characteristics of the surrounding area. This evaluation includes factors such as local safety, transportation convenience, educational environment, and the availability of commercial facilities. The output is an evaluation score for each property, which is used to score and rank real estate.

[0837] Step 4:

[0838] The consumer robot interacts with the user and analyzes their emotional state from their voice and facial expressions. The voice is transcribed using the Google Speech-to-Text API, and facial expressions are analyzed using OpenCV. The input is real-time user emotion data, and the output is a specific emotional state expressed by the user (e.g., joy, anxiety).

[0839] Step 5:

[0840] The server uses the user's emotional state as input to adjust the property information it suggests. The property list is re-ranked based on the emotional state, and information that matches the user's emotions is enhanced and supplemented. The output is an optimized property suggestion list that reflects the emotional state.

[0841] Step 6:

[0842] The user receives optimized real estate suggestions through a consumer robot. The robot verbally explains the details of the suggested properties (e.g., safety rating, natural environment, etc.) and monitors the user's additional emotional changes. The input is a list of suggestions from the server, and the output is the user's final feedback.

[0843] 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.

[0844] 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.

[0845] 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.

[0846] 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.

[0847] 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.

[0848] 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.

[0849] 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.

[0850] 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.

[0851] 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."

[0852] 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.

[0853] 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.

[0854] 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.

[0855] 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.

[0856] 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.

[0857] 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.

[0858] 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.

[0859] 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.

[0860] 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.

[0861] 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.

[0862] 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.

[0863] 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.

[0864] The following is further disclosed regarding the embodiments described above.

[0865] (Claim 1)

[0866] A means of receiving the user's desired conditions,

[0867] Means of collecting real estate-related data from multiple sources,

[0868] A means of evaluating the characteristics of the surrounding area based on the collected data,

[0869] A method for scoring and ranking real estate based on evaluation results,

[0870] A means of providing users with scored and ranked real estate information,

[0871] A system that includes this.

[0872] (Claim 2)

[0873] The system according to claim 1, further comprising means for evaluating security information relating to real estate based on collected data.

[0874] (Claim 3)

[0875] The system according to claim 1, further comprising means for evaluating natural disaster risk information relating to real estate based on collected data.

[0876] "Example 1"

[0877] (Claim 1)

[0878] A means of receiving the user's residency conditions,

[0879] A means of collecting residential information from multiple databases,

[0880] A means of analyzing the surrounding environment based on the collected information,

[0881] A means of evaluating and listing dwellings based on the analysis results,

[0882] Means for providing users with evaluated and enumerated housing information,

[0883] A means of adjusting evaluation results based on user preference criteria,

[0884] A system that includes this.

[0885] (Claim 2)

[0886] The system according to claim 1, further comprising means for evaluating safety information related to a residence based on collected information.

[0887] (Claim 3)

[0888] The system according to claim 1, further comprising means for evaluating disaster risk information related to a residence based on collected information.

[0889] "Application Example 1"

[0890] (Claim 1)

[0891] A means of receiving the user's desired conditions,

[0892] Means of collecting real estate-related data from multiple sources,

[0893] A means of evaluating the characteristics of the surrounding area based on the collected data,

[0894] A method for scoring and ranking real estate based on evaluation results,

[0895] A means of providing users with scored and ranked real estate information,

[0896] A video presentation method that shows real estate information to users while they are on the move,

[0897] A system that includes this.

[0898] (Claim 2)

[0899] The system according to claim 1, further comprising means for evaluating security information relating to real estate based on collected data.

[0900] (Claim 3)

[0901] The system according to claim 1, further comprising means for evaluating natural disaster risk information relating to real estate based on collected data.

[0902] "Example 2 of combining an emotion engine"

[0903] (Claim 1)

[0904] A means of receiving the user's desired conditions,

[0905] Means of collecting property-related information from multiple sources,

[0906] A means of evaluating the characteristics of a region based on the collected information,

[0907] A means of analyzing the user's emotional state and reflecting it in the suggestions,

[0908] A method for scoring and ranking properties, taking into account evaluation results and user sentiment,

[0909] A means of providing users with scored and ranked property information,

[0910] A system that includes this.

[0911] (Claim 2)

[0912] The system according to claim 1, further comprising means for evaluating security information relating to a property based on collected information.

[0913] (Claim 3)

[0914] The system according to claim 1, further comprising means for evaluating disaster risk information relating to a property based on collected information.

[0915] "Application example 2 when combining with an emotional engine"

[0916] (Claim 1)

[0917] A means of receiving the user's desired conditions,

[0918] Means of collecting real estate-related data from multiple sources,

[0919] A means of evaluating the characteristics of the surrounding area based on the collected data,

[0920] A method for scoring and ranking real estate based on evaluation results,

[0921] A means of providing users with scored and ranked real estate information,

[0922] A means of analyzing the user's emotional state and reflecting the analysis results in the proposal process,

[0923] A system that includes this.

[0924] (Claim 2)

[0925] The system according to claim 1, further comprising means for evaluating security information relating to real estate based on collected data.

[0926] (Claim 3)

[0927] The system according to claim 1, further comprising means for evaluating natural disaster risk information relating to real estate based on collected 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 means of receiving the user's desired conditions, Means of collecting real estate-related data from multiple sources, A means of evaluating the characteristics of the surrounding area based on the collected data, A method for scoring and ranking real estate based on evaluation results, A means of providing users with scored and ranked real estate information, A system that includes this.

2. The system according to claim 1, further comprising means for evaluating security information relating to real estate based on collected data.

3. The system according to claim 1, further comprising means for evaluating natural disaster risk information relating to real estate based on collected data.

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