system

Abstract

We provide the system. [Solution] A means for receiving user input information and predicting user preferences based on that information, A means for extracting the most suitable property information from a property information database based on the aforementioned preferences, A means for presenting the extracted property information to the user and collecting feedback information from the user, A means for analyzing the aforementioned feedback information and updating the user preference model, A means of predicting market trends based on the updated preference model and providing information to real estate-related businesses, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In a conventional real estate search system, it is difficult for a user to find an ideal property, and appropriate proposals based on the user's purchasing intention are not sufficiently made. Also, since it is impossible to analyze the user's preferences and behavioral data in real time and predict market trends, it is difficult for real estate-related businesses to formulate prompt and appropriate strategies. Thus, there is a problem in that useful information provision for both the user and real estate-related businesses is not sufficiently carried out.

Means for Solving the Problems

[0005] This invention provides a system that receives user input information and predicts user preferences based on that information. Furthermore, it includes means for extracting optimal property information from a property information database based on those preferences, presenting it to the user, and collecting user feedback. By analyzing the collected feedback and updating the user preference model, the system can make future property recommendations more accurate. In addition, it is possible to predict market trends based on the updated preference model and provide real estate-related businesses with information in real time to support strategic planning. This makes it possible to provide information that is beneficial to both users and real estate-related businesses.

[0006] "User input information" refers to data related to search criteria and preferences that users provide to the system.

[0007] "Preference prediction" is the process of predicting user preferences and needs based on collected user input information and behavioral data.

[0008] A "property information database" is a digital database that stores detailed information about real estate.

[0009] "Extracting optimal property information" is the process of selecting property information from a database that is suitable for the user's preferences.

[0010] "User feedback information" refers to data such as evaluations, opinions, and comments made by users regarding the properties presented.

[0011] "Feedback information analysis" refers to a series of operations to analyze collected user feedback and update the user preference model.

[0012] A "preference model" is a data model that shows a user's preferences, created based on their past behavior and feedback.

[0013] "Market trend forecasting" is the act of predicting trends and tendencies in the real estate market by using accumulated user data and preference models.

[0014] A "real estate-related business" refers to a company or individual that is involved in the real estate market and engages in activities such as buying, selling, leasing, and managing properties. [Brief explanation of the drawing]

[0015] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of the data processing device and 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]It is a sequence diagram showing the processing flow of the data processing system in Example 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

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

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

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

[0019] 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.

[0020] 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 disk (e.g., hard disk), or magnetic tape, etc.

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

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0023] [First Embodiment]

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

[0025] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0026] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0028] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0032] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0035] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0036] This invention is a real estate information matching system designed to enable users to efficiently find their ideal property. This system utilizes user input information and behavioral data to suggest properties that match the user's preferences and provides feedback on the results to real estate-related businesses.

[0037] The main components of this system consist of three elements: a server, a terminal, and a user. The server plays a central role in data collection, analysis, and preference prediction. Based on user input, it creates a preference model and extracts the most suitable properties from the property information database. The terminal provides an interface with the user, allowing them to search and view properties according to their requests. Feedback information entered by the user is sent to the server via the terminal and used to update the preference model.

[0038] As a concrete example, when a user sets conditions such as "newly built," "3LDK," and "within a 10-minute walk from the station" on their device and starts a property search, the device sends this information to the server. The server, taking into account the user's past browsing history, extracts properties that match these conditions from its database and sends the most suitable property information to the device. The user then views the displayed property list and examines properties of interest in detail. During the detailed examination, it is also possible to experience the property using virtual reality (VR) or augmented reality (AR). This allows the user to obtain a more concrete image of the property.

[0039] When users leave comments or ratings about a property, that feedback information is sent back to the server. The server analyzes this information and updates the user preference model. By repeating this process, the entire system refines its property information delivery to better match users and enables more accurate predictions of market trends. Real estate businesses are notified of the trend prediction information generated by the server, allowing them to respond quickly to market changes.

[0040] Thus, the present invention realizes a system that provides users with efficient property search and a rich experience, and real estate-related businesses with market insights, thereby providing valuable information to both parties.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The user enters their desired property criteria using their device and begins the property search. The user then sets detailed conditions such as "newly built," "3LDK," and "within a 10-minute walk from the station."

[0044] Step 2:

[0045] The terminal sends the search criteria received from the user to the server and makes a request.

[0046] Step 3:

[0047] The server receives the request and searches its property database for properties that match the criteria. The server also analyzes the user's past behavior history and takes into account a preference model to narrow down the properties.

[0048] Step 4:

[0049] The server generates an optimized property list and sends this list to the terminal. The properties selected are prioritized based on the user's needs.

[0050] Step 5:

[0051] The device displays the received property list to the user. The user can view detailed property information and open the details screen for properties that interest them.

[0052] Step 6:

[0053] If a user selects a specific property and desires a visual experience through virtual reality (VR) or augmented reality (AR), the device will perform this action and provide the user with a three-dimensional image of the property.

[0054] Step 7:

[0055] Users enter feedback and ratings about properties on their devices, and this information is sent to the server.

[0056] Step 8:

[0057] The server analyzes user feedback and updates the user preference model. This data is used to improve the accuracy of future property recommendations.

[0058] Step 9:

[0059] The server collects and analyzes data from all users to predict market trends. This information is regularly provided as feedback to real estate businesses, serving as reference material for their business strategies.

[0060] The above describes the processing flow of the program in this system.

[0061] (Example 1)

[0062] 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."

[0063] Conventional real estate information search systems lack the functionality to suggest the most suitable properties based on user preferences and behavior. Furthermore, the means by which users can concretely experience detailed property information are limited, resulting in users having to expend considerable effort in selecting a property. In addition, the provision of information necessary to predict market trends in real time and enable real estate businesses to respond quickly is insufficient.

[0064] 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.

[0065] In this invention, the server includes a device that receives user input data and infers preferences based on said data, a device that extracts optimal data from an information database based on said preferences, and a device that displays the extracted information to the user and collects feedback from the user. As a result, users can efficiently obtain property information that best suits their preferences, and real estate businesses can quickly formulate business plans based on market trend predictions.

[0066] "User input data" refers to information provided by the user via their device to identify their property search criteria and preferences.

[0067] A "preference prediction device" is a system component that has the function of predicting a user's preferences and tendencies based on the user's input data and past behavioral history.

[0068] An "information database" is a collection of information about properties, and serves as a foundation for providing necessary information based on search criteria.

[0069] A "feedback collection device" is a function that receives comments and ratings provided by users, and the system analyzes them to update the user model.

[0070] "Market trend forecasting" is the process of predicting future market demand and trends by utilizing accumulated data and user preference models.

[0071] A "device for supplying information to related businesses" refers to a system component that provides predicted market trend information to real estate-related business operators and supports their business activities.

[0072] This real estate information matching system consists of three elements: server, terminal, and user. The specific roles and processing functions of each are described below.

[0073] First, the server plays a central role in this system and uses a cloud computing platform to process large amounts of data. Specific examples include Amazon Web Services and Microsoft Azure. This server utilizes the Python programming language and machine learning libraries such as TENSORFLOW and Scikit-learn to analyze user input data and infer preferences. It also executes SQL queries against the property information database to search for properties that match the specified criteria. The results of this analysis are used to update the user preference model and predict market trends.

[0074] Next, the terminal provides an interface for direct interaction with the user. On the terminal, the user can enter property search criteria, which are then sent to the server. In addition, it has the functionality to display detailed property information and search results to the user, and to provide a property experience through virtual reality (VR) and augmented reality (AR). User feedback is transferred from the terminal to the server as important data, further contributing to the improvement of the system's accuracy.

[0075] Users can use the system to efficiently search for their ideal property. Users input specific criteria such as "newly built," "3LDK," and "within a 10-minute walk from the station" via their terminal, and then search for properties based on those criteria. The feedback data accumulated during the search process evolves the user preference model, improving the accuracy of subsequent searches.

[0076] As a concrete example, let's assume the user enters the following prompt:

[0077] "Users are searching for properties that meet the following criteria: 'newly built,' '3LDK,' and 'within a 10-minute walk from the station.' We extract properties that match these criteria from our real estate database and provide detailed information. Past user behavior data is also taken into consideration."

[0078] This system allows users to receive property information that best matches their criteria, and enables real estate businesses to develop strategies to respond quickly to market fluctuations.

[0079] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0080] Step 1:

[0081] Users enter property search criteria using a terminal. Specifically, they specify conditions such as "newly built," "3LDK," and "within a 10-minute walk from the station." The entered data is structured and prepared to be sent to the server via the terminal's user interface.

[0082] Step 2:

[0083] The terminal sends the conditions received from the user to the server. HTTP is generally used as the communication protocol, and the data is encoded in JSON format and securely sent to the server. The transmission is encrypted using SSL / TLS.

[0084] Step 3:

[0085] The server receives conditional data sent from the terminal and begins analysis. Using these conditions as input, the server performs data analysis through a generative AI model utilizing Python and TensorFlow. A preference model that takes into account the user's past behavior data is developed, and a list of suitable property information is generated.

[0086] Step 4:

[0087] The server executes SQL queries against the information database and extracts optimal property information based on the generated preference model. This selects the property information that best matches the user's conditions and preferences, and outputs it in a structured format.

[0088] Step 5:

[0089] The server returns the selected property information to the terminal. The output information includes detailed property information and links that allow the user to experience the property through virtual reality (VR) or augmented reality (AR).

[0090] Step 6:

[0091] The terminal displays property information received from the server to the user. Through the user interface, the user can browse the property list and examine properties of interest in detail. Using VR and AR, the user can virtually experience a more concrete image of the property.

[0092] Step 7:

[0093] Users enter feedback about a property and send it to the server via their device. This feedback includes their impression of the property, areas for improvement, and any additional requests.

[0094] Step 8:

[0095] The server analyzes the received feedback and updates the user preference model. Machine learning algorithms are used for data processing, and the model is trained as needed to improve its accuracy.

[0096] Step 9:

[0097] The server predicts market trends based on updated preference models and notifies real estate businesses. The predicted information is used to adjust business strategies and discover new market opportunities.

[0098] (Application Example 1)

[0099] 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."

[0100] Traditional real estate information systems make it difficult for users to efficiently find their ideal property. In particular, they lack the means to present optimal properties that take into account user preferences and conditions, as well as to make the property experience more realistic. Furthermore, there is a need for effective information delivery methods that can quickly respond to changes in market trends.

[0101] 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.

[0102] In this invention, the server includes means for receiving user input information and predicting the user's preferences based on that information, means for extracting optimal property information from a property information database, and means for visually presenting real estate in a virtual environment. This makes it possible for the user to intuitively operate and experience real estate properties that meet their criteria via a smart device.

[0103] "Means for predicting user preferences" refers to a system that analyzes information entered or provided by the user to predict the user's preferences and tendencies regarding properties.

[0104] A "property information database" is a dataset containing various information about real estate properties, including property details, location, price, and other related information.

[0105] "Means of collecting feedback information" refers to methods of recording evaluations and opinions that users give regarding the property information presented, and using this information for future data analysis and service improvement.

[0106] A "means for updating preference models" refers to a mechanism that utilizes feedback information obtained from users to re-evaluate user preference patterns and improve the accuracy of preference predictions in response to new data.

[0107] "Methods for predicting market trends" refer to algorithms that analyze real estate market trends based on collected data and predict future market changes and demand.

[0108] "Means of visually presenting real estate in a virtual environment" refers to methods that use VR and AR technologies to visually present properties to users in a computer-generated 3D space, providing a realistic experience.

[0109] "A means of intuitively operating and experiencing real estate properties via smart devices" refers to a method in which users can easily search for properties and intuitively manipulate property information in a virtual space using digital devices such as smartphones and smart glasses.

[0110] This invention is a real estate information matching system for users to efficiently find their ideal property, and consists of a server, a terminal, and a user. The server implements an algorithm that processes user input information and predicts preferences using a program written in Python or JavaScript (registered trademark). This algorithm analyzes past user behavior data and feedback information to predict user preferences and extracts the most suitable property information from the property information database.

[0111] The device functions as the user interface, enabling intuitive operation using smartphones or smart glasses. By utilizing software such as Unity and Unreal Engine, an environment is created that visually presents properties through virtual reality or augmented reality technology. This allows users to experience properties in a way that closely resembles the real world.

[0112] For example, when a user enters criteria such as "newly built," "3LDK," and "within a 10-minute walk from the station" via a device and starts a search, the server extracts matching properties from its database, taking into account the user's past browsing history, and sends the information to the device. The user can then virtually visit the displayed properties through smart glasses and experience the atmosphere of the actual property.

[0113] User feedback is automatically transferred to the server and used as data to generate new preference models. In this way, the system can continuously improve the user experience. It also predicts market trends and provides useful information to real estate businesses.

[0114] An example of a prompt message generated using an AI model might be: "The user is searching for a pet-friendly 2LDK property. Please retrieve the best matching property information and set it up so that the user can experience it in a virtual space." Based on this prompt message, the AI ​​can quickly suggest the most suitable property information.

[0115] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0116] Step 1:

[0117] The user uses a terminal to enter property search criteria. These criteria include location, floor plan, and building type. The terminal sends this input information to the server.

[0118] Step 2:

[0119] The server queries the property information database based on the received search criteria to extract candidate properties. The database uses SQL queries and other methods to quickly search for properties that match the criteria, organizes the information, and outputs it. The obtained property information is then processed in conjunction with user preference prediction parameters.

[0120] Step 3:

[0121] The server uses a generative AI model to select the property that best suits the user's preferences from extracted property information, taking into account the user's past behavior data and feedback. In this process, a data analysis algorithm combines preference parameters and property information to output individually optimized results.

[0122] Step 4:

[0123] The server sends the selected property information to the terminal. The terminal visually presents the received property information to the user. When using VR or AR technology, a virtual space is constructed using an engine such as Unity, allowing the user to realistically experience the property.

[0124] Step 5:

[0125] Users view the presented properties and virtually visit them through their smart devices. They then input feedback about this virtual visit into their devices. This feedback includes the user's level of interest and specific comments.

[0126] Step 6:

[0127] The device collects user feedback and sends it to the server. The server analyzes this feedback information and initiates a process to update the user preference model. Machine learning algorithms are used to optimize the model based on user preferences.

[0128] Step 7:

[0129] The server predicts market trends based on updated preference models and provides the latest trend information to real estate businesses. This information is provided in real time via API, allowing businesses to adjust their market strategies accordingly.

[0130] 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.

[0131] This invention is a real estate information system that takes into account the user's preferences and emotions when choosing a property, and provides more personalized property information. This system incorporates an emotion engine that recognizes the user's emotional state, creating a profile that includes the user's emotions, and contributing to property matching.

[0132] The core of the system consists of the server, terminal, and user. First, the user enters property search criteria via the terminal, and the resulting operations and reactions to the search results are sent to the server in real time. The terminal uses data such as the user's facial expressions, voice tone, and input speed to perform sentiment analysis using an emotion engine. For example, if a user is looking at the details of a property and is smiling while intently viewing the screen, the emotion engine recognizes that emotion as "interest."

[0133] The server creates a preference model that reflects the user's tastes and emotions based on the collected data. Based on this model, it extracts the most suitable property information from the property database and sends it to the terminal. This information is displayed to the user, and the order of the suggested property list is dynamically adjusted according to their emotions. For example, properties for which the emotion engine detects a "positive emotion" are displayed higher in the list.

[0134] The device sends back information to the server, including feedback and ratings that users have given about properties. The server analyzes the feedback and sentiment information to update the preference model, making it more accurate. In addition, market trends are analyzed based on this model and sentiment information, and this information is provided to real estate businesses, which can then use it to develop strategies for providing personalized services to their customers.

[0135] This system facilitates the streamlining and personalization of property selection for users, enabling smarter real estate choices. Furthermore, for real estate businesses, it allows for the provision of differentiated customer services by utilizing detailed customer profiles that include users' emotional tendencies. Thus, a property information provision system that integrates user preferences and emotions represents the primary form of this invention.

[0136] The following describes the processing flow.

[0137] Step 1:

[0138] The user enters property search criteria using their device and starts the search. The user sets conditions such as "newly built," "2LDK," and "with balcony."

[0139] Step 2:

[0140] The device sends the entered search criteria to the server via an emotion engine. It also simultaneously collects the user's facial expressions and voice for emotion recognition.

[0141] Step 3:

[0142] The emotion engine analyzes the user's facial expressions and voice to generate emotion labels such as "interested," "indifferent," and "dissatisfied."

[0143] Step 4:

[0144] The server receives emotion labels and search criteria, and searches the property database, taking into account the user's preference model. Relevant properties are prioritized based on the user's positive emotions.

[0145] Step 5:

[0146] The server generates an optimized property list, adjusts the order of the list based on sentiment information, and then sends it to the terminal.

[0147] Step 6:

[0148] The device displays a list of properties to the user. When the user selects an item from the list, detailed information about that property is displayed.

[0149] Step 7:

[0150] Users input their feelings and evaluations about a property and provide feedback through their device. The emotion engine re-analyzes the emotional information as needed.

[0151] Step 8:

[0152] The feedback information and re-analyzed sentiment labels are sent to the server and used to further improve the preference model.

[0153] Step 9:

[0154] The server predicts market trends based on updated preference models and sentiment data, and provides this information to real estate businesses. These businesses then use this information to personalize customer service.

[0155] In this way, a property information provision system incorporating an emotion engine operates effectively by taking into account both the user's preferences and emotions.

[0156] (Example 2)

[0157] 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".

[0158] Traditional real estate information systems only suggest properties based on the user's search criteria, resulting in insufficient personalization that takes into account the user's emotional tendencies and detailed preferences. Furthermore, mechanisms for effectively collecting user feedback and using it to improve future recommendations were limited, making it difficult to provide an advanced customer experience.

[0159] 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.

[0160] In this invention, the server includes means for receiving user input information and sentiment data and predicting the user's preferences based on these, means for extracting optimal property information from a property information database, and means for prioritizing and presenting properties to the user and collecting feedback information from the user. This enables highly accurate property recommendations that take the user's emotions into consideration.

[0161] "User input information" refers to data indicating the conditions and preferences that users provide to the system when searching for properties.

[0162] "Emotional data" refers to data that indicates the user's emotional state, obtained from the user's facial expressions, voice tone, input speed, etc.

[0163] "Methods for predicting preferences" refer to methods that analyze user input information and emotional data to infer what characteristics of properties a user will prefer.

[0164] A "property information database" is a collection of data that systematically stores information related to real estate.

[0165] "Methods for extracting optimal property information" refers to methods of selecting suitable property information from a property information database based on user preference data.

[0166] "Prioritization" is the process of rearranging extracted property information according to the user's preferences.

[0167] "Feedback information" refers to the evaluations and opinions that users provide regarding property information.

[0168] A "preference model" is a profile generated based on data analysis, representing user preferences as a machine learning model.

[0169] "Methods for predicting market trends" refer to methods that use updated preference models and sentiment data to infer future changes in the real estate market.

[0170] "Related businesses" refers to companies and professionals in the real estate industry that utilize the information provided by this system.

[0171] This invention is a real estate information system that more accurately understands user preferences and provides personalized property information. The system consists of three elements: a server, a terminal, and a user. First, the user inputs property search criteria using the terminal. Specifically, they select conditions such as location, floor plan, and price range in the input fields.

[0172] The device utilizes its built-in camera and microphone to collect emotional data, such as facial expressions, voice tone, and input speed, in addition to the entered search criteria. This data is analyzed in real time by software called an emotion engine. The emotion engine can recognize the user's emotional state from their gaze, smile, etc., and classify it as "interested" or "anxious," etc.

[0173] The server plays a central role in collecting and analyzing diverse data. Based on user input and sentiment data, it creates a user preference model using a generative AI model. The generative AI model learns from past data using machine learning algorithms to predict what types of properties the user will be interested in.

[0174] The server extracts suitable properties from the property information database based on a preference model. This selection process utilizes machine learning algorithms, creating a property list with priorities that reflect the user's sentiment. The selected property information is then sent to the terminal and displayed to the user.

[0175] When a user provides feedback on a displayed property list, the device sends that information to the server. The server re-analyzes the feedback information and sentiment data to further refine the preference model. This improves the accuracy of property recommendations in the future.

[0176] A concrete example of a prompt message would be: "Please enter the property criteria the user is looking for, so that the device can analyze their emotions while they are viewing the property details. Also, please enter any other information about the user's emotions and preferences that should be considered."

[0177] This system allows users to select properties based on their essential needs, saving time and resulting in higher satisfaction. Furthermore, real estate businesses can leverage detailed user preference and emotional data to provide more effective services.

[0178] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0179] Step 1:

[0180] Users enter property search criteria using their own devices. This information includes, for example, "region," "price range," and "floor plan." This criteria information is converted into a data format on the device and stored on the client-side application.

[0181] Step 2:

[0182] In addition to input information, the device captures the user's facial expressions with its built-in camera and analyzes their voice tone with its microphone. It also records the user's input speed and mouse movements in real time. This data is processed by an emotion engine to estimate the user's emotional state. For example, a smiling expression or a gentle voice tone is recognized as a "positive emotion."

[0183] Step 3:

[0184] The terminal sends the user's search criteria and sentiment data to the server. The server receives this data and analyzes it using a generative AI model. In this process, a user preference model is generated based on the input data. The preference model learns the characteristics and sentimental tendencies of properties that the user has shown interest in in the past.

[0185] Step 4:

[0186] The server searches the property database using the generated preference model. The database contains detailed information on numerous properties. The server uses a machine learning algorithm to select the properties that best match the user's preferences and sets their priorities. This result is generated in list format and sent to the terminal.

[0187] Step 5:

[0188] The terminal displays a list of properties sent from the server to the user. The list is sorted based on the user's preferences, with higher-priority properties displayed at the top. The user then views this list of properties and provides further evaluations and feedback.

[0189] Step 6:

[0190] User feedback and ratings are resent from the device to the server. The server analyzes this feedback and updates the previously generated preference model. This update further improves the accuracy of property recommendations in the future.

[0191] Step 7:

[0192] The server predicts trends in the real estate market based on updated preference models and collected sentiment data. This analysis is provided to relevant businesses, enabling them to enhance their customer service and marketing strategies.

[0193] (Application Example 2)

[0194] 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".

[0195] Traditional real estate information systems have a low degree of personalization in property selection because they only consider user preferences and fail to adequately reflect emotions in their information provision. Furthermore, they do not dynamically present properties that take into account the user's emotional reactions, resulting in a lack of ability to capture user interest.

[0196] 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.

[0197] In this invention, the server includes means for receiving user input information and emotional information and predicting preferences, means for extracting property information based on preferences and emotional information, and means for recognizing the user's emotional state in real time. This makes it possible to provide personalized property information based on the user's emotions.

[0198] "User input information" refers to information such as requirements, conditions, and keywords that users provide when searching for properties.

[0199] "Emotional information" refers to data on a user's emotional state, analyzed through their facial expressions, voice tone, and other factors.

[0200] "Means of predicting preferences" refer to processes or functions that predict user preferences based on user input information and emotional information.

[0201] "Methods for extracting property information" refers to a mechanism that selects the most suitable property information from a database based on the user's preferences and emotional information.

[0202] "Means of recognizing a user's emotional state" refers to technologies and devices that read a user's facial expressions, voice, etc., in real time and determine their emotions.

[0203] "Recognizing in real time" means processing data instantly and generating results without delay.

[0204] "Personalized property information" refers to customized property information that reflects the individual user's preferences and feelings.

[0205] The system to realize this application example includes a program that analyzes the user's emotions and preferences in real time when selecting a real estate property and provides personalized property information.

[0206] The device uses smart glasses to capture the user's facial expressions and voice in real time. This utilizes hardware such as a facial recognition camera and microphone, and analyzes emotional states using software such as the "EmotionRecognitionAPI." The user also inputs their requests into the device and sends them to the server along with emotional information.

[0207] The server receives this data and uses a generative AI model to create a user preference model. This generative AI model extracts matching properties from the property information database based on real-time sentiment analysis and user profiles.

[0208] For example, if a user smiles when viewing a large living room during a virtual tour, this emotional information is analyzed as "interested," and the server receives feedback to prioritize displaying properties of similar size. By making choices based on this information, users can select properties that lead to a deeper level of satisfaction.

[0209] An example of a prompt for a generative AI model is: "When a user is impressed by a spacious living room, suggest other properties that might be suitable."

[0210] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0211] Step 1:

[0212] The device captures the user's facial expressions and voice in real time through smart glasses. Input data comes from the camera and microphone, which is converted into emotional information using the "EmotionRecognitionAPI". The output is the user's current emotional state. At this stage, the device prepares to send the emotional data to the server.

[0213] Step 2:

[0214] Users enter property search criteria via a terminal. This input includes keywords, conditions, and selections from lists. Based on this, the terminal formats the user's request and sends it to the server. This process organizes search data based on the user's preferences.

[0215] Step 3:

[0216] The server integrates the emotional information and search criteria received from the terminal and begins processing. The input consists of emotional data and search data, and a generative AI model is used to create a user preference model based on these. Through data calculations, the most suitable property information for the user is extracted.

[0217] Step 4:

[0218] The server narrows down the property database based on the user's preferences and sends the extracted property list to the terminal. The terminal then presents the user with customized property information. The output at this stage is the display of the property list to the user.

[0219] Step 5:

[0220] Users provide feedback on the presented properties. This feedback is provided as input, based on their selections. This information is then sent back to the server via the terminal. The user's feedback verifies the consistency between their emotions and actual preferences.

[0221] Step 6:

[0222] The server updates its preference model using collected feedback information. Feedback data serves as input, and data processing refines the preference model. As a result, new market trend information is generated and used to inform future property recommendations.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] [Second Embodiment]

[0227] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0228] 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.

[0229] 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).

[0230] 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.

[0231] 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.

[0232] 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).

[0233] 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.

[0234] 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.

[0235] 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.

[0236] 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.

[0237] 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.

[0238] 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".

[0239] This invention is a real estate information matching system designed to enable users to efficiently find their ideal property. This system utilizes user input information and behavioral data to suggest properties that match the user's preferences and provides feedback on the results to real estate-related businesses.

[0240] The main components of this system consist of three elements: a server, a terminal, and a user. The server plays a central role in data collection, analysis, and preference prediction. Based on user input, it creates a preference model and extracts the most suitable properties from the property information database. The terminal provides an interface with the user, allowing them to search and view properties as requested. Feedback information entered by the user is sent to the server via the terminal and used to update the preference model.

[0241] As a concrete example, when a user sets conditions such as "newly built," "3LDK," and "within a 10-minute walk from the station" on their device and starts a property search, the device sends this information to the server. The server, taking into account the user's past browsing history, extracts properties that match these conditions from its database and sends the most suitable property information to the device. The user then views the displayed property list and examines properties of interest in detail. During the detailed examination, it is also possible to experience the property using virtual reality (VR) or augmented reality (AR). This allows the user to obtain a more concrete image of the property.

[0242] When users leave comments or ratings about a property, that feedback information is sent back to the server. The server analyzes this information and updates the user preference model. By repeating this process, the entire system refines its property information delivery to better match users and enables more accurate predictions of market trends. Real estate businesses are notified of the trend prediction information generated by the server, allowing them to respond quickly to market changes.

[0243] Thus, the present invention realizes a system that provides users with efficient property search and a rich experience, and real estate-related businesses with market insights, thereby providing valuable information to both parties.

[0244] The following describes the processing flow.

[0245] Step 1:

[0246] The user enters their desired property criteria using their device and begins the property search. The user then sets detailed conditions such as "newly built," "3LDK," and "within a 10-minute walk from the station."

[0247] Step 2:

[0248] The terminal sends the search criteria received from the user to the server and makes a request.

[0249] Step 3:

[0250] The server receives the request and searches its property database for properties that match the criteria. The server also analyzes the user's past behavior history and takes into account a preference model to narrow down the properties.

[0251] Step 4:

[0252] The server generates an optimized property list and sends this list to the terminal. The properties selected are prioritized based on the user's needs.

[0253] Step 5:

[0254] The device displays the received property list to the user. The user can view detailed property information and open the details screen for properties that interest them.

[0255] Step 6:

[0256] If a user selects a specific property and desires a visual experience through virtual reality (VR) or augmented reality (AR), the device will perform this action and provide the user with a three-dimensional image of the property.

[0257] Step 7:

[0258] Users enter feedback and ratings about properties on their devices, and this information is sent to the server.

[0259] Step 8:

[0260] The server analyzes user feedback and updates the user preference model. This data is used to improve the accuracy of future property recommendations.

[0261] Step 9:

[0262] The server collects and analyzes data from all users to predict market trends. This information is regularly provided as feedback to real estate businesses, serving as reference material for their business strategies.

[0263] The above describes the processing flow of the program in this system.

[0264] (Example 1)

[0265] 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."

[0266] Conventional real estate information search systems lack the functionality to suggest the most suitable properties based on user preferences and behavior. Furthermore, the means by which users can concretely experience detailed property information are limited, resulting in users having to expend considerable effort in selecting a property. In addition, the provision of information necessary to predict market trends in real time and enable real estate businesses to respond quickly is insufficient.

[0267] 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.

[0268] In this invention, the server includes a device that receives user input data and infers preferences based on said data, a device that extracts optimal data from an information database based on said preferences, and a device that displays the extracted information to the user and collects feedback from the user. As a result, users can efficiently obtain property information that best suits their preferences, and real estate businesses can quickly formulate business plans based on market trend predictions.

[0269] "User input data" refers to information provided by the user via their device to identify their property search criteria and preferences.

[0270] A "preference prediction device" is a system component that has the function of predicting a user's preferences and tendencies based on the user's input data and past behavioral history.

[0271] An "information database" is a collection of information about properties, and serves as a foundation for providing necessary information based on search criteria.

[0272] A "feedback collection device" is a function that receives comments and ratings provided by users, and the system analyzes them to update the user model.

[0273] "Market trend forecasting" is the process of predicting future market demand and trends by utilizing accumulated data and user preference models.

[0274] A "device for supplying information to related businesses" refers to a system component that provides predicted market trend information to real estate-related business operators and supports their business activities.

[0275] This real estate information matching system consists of three elements: server, terminal, and user. The specific roles and processing functions of each are described below.

[0276] First, the server plays a central role in this system and uses a cloud computing platform to process large amounts of data. Specific examples include Amazon Web Services and Microsoft Azure. This server utilizes the Python programming language and machine learning libraries such as TensorFlow and Scikit-learn to analyze user input data and infer preferences. It also executes SQL queries against a property information database to search for properties that match the specified criteria. The results of this analysis are used to update the user preference model and predict market trends.

[0277] Next, the terminal provides an interface for direct interaction with the user. On the terminal, the user can enter property search criteria, which are then sent to the server. In addition, it has the functionality to display detailed property information and search results to the user, and to provide a property experience through virtual reality (VR) and augmented reality (AR). User feedback is transferred from the terminal to the server as important data, further contributing to the improvement of the system's accuracy.

[0278] Users can use the system to efficiently search for their ideal property. Users input specific criteria such as "newly built," "3LDK," and "within a 10-minute walk from the station" via their terminal, and then search for properties based on those criteria. The feedback data accumulated during the search process evolves the user preference model, improving the accuracy of subsequent searches.

[0279] As a concrete example, let's assume the user enters the following prompt:

[0280] "Users are searching for properties that meet the following criteria: 'newly built,' '3LDK,' and 'within a 10-minute walk from the station.' We extract properties that match these criteria from our real estate database and provide detailed information. Past user behavior data is also taken into consideration."

[0281] With this system, users can receive property information that best suits their conditions, and real estate-related businesses can build strategies to quickly respond to market fluctuations.

[0282] The flow of the specific process in Example 1 will be described using FIG. 11.

[0283] Step 1:

[0284] The user uses the terminal to input the conditions for property search. Specifically, for example, conditions such as "new construction", "3LDK", and "within a 10-minute walk from the station" are specified. The input data is structured and prepared to be sent to the server through the user interface of the terminal.

[0285] Step 2:

[0286] The terminal sends the conditions received from the user to the server. Generally, HTTP is used as the communication protocol, and the data is encoded in JSON format and securely sent to the server. When sending, encrypted communication is performed using SSL / TLS.

[0287] ]]Step 3:

[0288] The server receives the condition data sent from the terminal and starts analysis. Using these conditions as input, the server performs data analysis through a generated AI model by leveraging Python and TensorFlow. A preference model considering the user's past behavior data is developed to generate a list of suitable property information.

[0289] Step 4:

[0290] [

[0290] The server executes an SQL query against the information database to extract the optimal property information based on the generated preference model. As a result, the property information that best matches the user's conditions and preferences is selected and output in a structured format.

[0291] Step 5:

[0292] The server returns the selected property information to the terminal. The output information includes detailed property information and links that allow the user to experience the property through virtual reality (VR) or augmented reality (AR).

[0293] Step 6:

[0294] The terminal displays property information received from the server to the user. Through the user interface, the user can browse the property list and examine properties of interest in detail. Using VR and AR, the user can virtually experience a more concrete image of the property.

[0295] Step 7:

[0296] Users enter feedback about a property and send it to the server via their device. This feedback includes their impression of the property, areas for improvement, and any additional requests.

[0297] Step 8:

[0298] The server analyzes the received feedback and updates the user preference model. Machine learning algorithms are used for data processing, and the model is trained as needed to improve its accuracy.

[0299] Step 9:

[0300] The server predicts market trends based on updated preference models and notifies real estate businesses. The predicted information is used to adjust business strategies and discover new market opportunities.

[0301] (Application Example 1)

[0302] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0303] In conventional real estate information provision systems, it is difficult for users to efficiently discover ideal properties. In particular, in addition to presenting optimal properties considering user preferences and conditions, there is a lack of means to make the experience of properties more realistic. Also, effective information provision means for quickly responding to market trend changes are required.

[0304] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following respective means.

[0305] In this invention, the server includes means for receiving the input information of the user, predicting the user's preferences based on the information, extracting optimal property information from the property information database, and visually presenting real estate in a virtual environment. As a result, it becomes possible for the user to intuitively operate and experience real estate properties that meet the conditions via a smart device.

[0306] The "means for predicting the user's preferences" is a mechanism that analyzes the information input or provided by the user and predicts the user's preferences and trends regarding properties.

[0307] The "property information database" is a dataset in which various information regarding real estate properties is accumulated, and includes property details, location, price, and other related information.

[0308] The "means for collecting feedback information" is a method of recording evaluations and opinions made by the user regarding the presented property information and using it for future data analysis and service improvement.

[0309] The "means for updating the preference model" is a mechanism that utilizes the feedback information obtained from the user, re-evaluates the user's preference pattern, and improves the prediction accuracy of preferences in a form corresponding to new data.

[0310] The "means for predicting market trends" is an algorithm for analyzing the trends in the real estate market based on the collected data and predicting future market changes and demands.

[0311] "Means of visually presenting real estate in a virtual environment" refers to methods that use VR and AR technologies to visually present properties to users in a computer-generated 3D space, providing a realistic experience.

[0312] "A means of intuitively operating and experiencing real estate properties via smart devices" refers to a method in which users can easily search for properties and intuitively manipulate property information in a virtual space using digital devices such as smartphones and smart glasses.

[0313] This invention is a real estate information matching system for users to efficiently find their ideal property, and consists of a server, a terminal, and a user. The server implements an algorithm that processes user input information and predicts preferences using a program written in Python or JavaScript. This algorithm analyzes past user behavior data and feedback information to predict user preferences and extracts the most suitable property information from the property information database.

[0314] The device functions as the user interface, enabling intuitive operation using smartphones or smart glasses. By utilizing software such as Unity and Unreal Engine, an environment is created that visually presents properties through virtual reality or augmented reality technology. This allows users to experience properties in a way that closely resembles the real world.

[0315] For example, when a user enters criteria such as "newly built," "3LDK," and "within a 10-minute walk from the station" via a device and starts a search, the server extracts matching properties from its database, taking into account the user's past browsing history, and sends the information to the device. The user can then virtually visit the displayed properties through smart glasses and experience the atmosphere of the actual property.

[0316] User feedback is automatically transferred to the server and used as data to generate new preference models. In this way, the system can continuously improve the user experience. It also predicts market trends and provides useful information to real estate businesses.

[0317] An example of a prompt message generated using an AI model might be: "The user is searching for a pet-friendly 2LDK property. Please retrieve the best matching property information and set it up so that the user can experience it in a virtual space." Based on this prompt message, the AI ​​can quickly suggest the most suitable property information.

[0318] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0319] Step 1:

[0320] The user uses a terminal to enter property search criteria. These criteria include location, floor plan, and building type. The terminal sends this input information to the server.

[0321] Step 2:

[0322] The server queries the property information database based on the received search criteria to extract candidate properties. The database uses SQL queries and other methods to quickly search for properties that match the criteria, organizes the information, and outputs it. The obtained property information is then processed in conjunction with user preference prediction parameters.

[0323] Step 3:

[0324] The server uses a generative AI model to select the property that best suits the user's preferences from extracted property information, taking into account the user's past behavior data and feedback. In this process, a data analysis algorithm combines preference parameters and property information to output individually optimized results.

[0325] Step 4:

[0326] The server sends the selected property information to the terminal. The terminal visually presents the received property information to the user. When using VR or AR technology, a virtual space is constructed using an engine such as Unity, allowing the user to realistically experience the property.

[0327] Step 5:

[0328] Users view the presented properties and virtually visit them through their smart devices. They then input feedback about this virtual visit into their devices. This feedback includes the user's level of interest and specific comments.

[0329] Step 6:

[0330] The device collects user feedback and sends it to the server. The server analyzes this feedback information and initiates a process to update the user preference model. Machine learning algorithms are used to optimize the model based on user preferences.

[0331] Step 7:

[0332] The server predicts market trends based on updated preference models and provides the latest trend information to real estate businesses. This information is provided in real time via API, allowing businesses to adjust their market strategies accordingly.

[0333] 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.

[0334] This invention is a real estate information system that takes into account the user's preferences and emotions when choosing a property, and provides more personalized property information. This system incorporates an emotion engine that recognizes the user's emotional state, creating a profile that includes the user's emotions, and contributing to property matching.

[0335] The core of the system consists of the server, terminal, and user. First, the user enters property search criteria via the terminal, and the resulting operations and reactions to the search results are sent to the server in real time. The terminal uses data such as the user's facial expressions, voice tone, and input speed to perform sentiment analysis using an emotion engine. For example, if a user is looking at the details of a property and is smiling while intently viewing the screen, the emotion engine recognizes that emotion as "interest."

[0336] The server creates a preference model that reflects the user's tastes and emotions based on the collected data. Based on this model, it extracts the most suitable property information from the property database and sends it to the terminal. This information is displayed to the user, and the order of the suggested property list is dynamically adjusted according to their emotions. For example, properties for which the emotion engine detects a "positive emotion" are displayed higher in the list.

[0337] The device sends back information to the server, including feedback and ratings that users have given about properties. The server analyzes the feedback and sentiment information to update the preference model, making it more accurate. In addition, market trends are analyzed based on this model and sentiment information, and this information is provided to real estate businesses, which can then use it to develop strategies for providing personalized services to their customers.

[0338] This system facilitates the streamlining and personalization of property selection for users, enabling smarter real estate choices. Furthermore, for real estate businesses, it allows for the provision of differentiated customer services by utilizing detailed customer profiles that include users' emotional tendencies. Thus, a property information provision system that integrates user preferences and emotions represents the primary form of this invention.

[0339] The following describes the processing flow.

[0340] Step 1:

[0341] The user enters property search criteria using their device and starts the search. The user sets conditions such as "newly built," "2LDK," and "with balcony."

[0342] Step 2:

[0343] The device sends the entered search criteria to the server via an emotion engine. It also simultaneously collects the user's facial expressions and voice for emotion recognition.

[0344] Step 3:

[0345] The emotion engine analyzes the user's facial expressions and voice to generate emotion labels such as "interested," "indifferent," and "dissatisfied."

[0346] Step 4:

[0347] The server receives emotion labels and search criteria, and searches the property database, taking into account the user's preference model. Relevant properties are prioritized based on the user's positive emotions.

[0348] Step 5:

[0349] The server generates an optimized property list, adjusts the order of the list based on sentiment information, and then sends it to the terminal.

[0350] Step 6:

[0351] The device displays a list of properties to the user. When the user selects an item from the list, detailed information about that property is displayed.

[0352] Step 7:

[0353] Users input their feelings and evaluations about a property and provide feedback through their device. The emotion engine re-analyzes the emotional information as needed.

[0354] Step 8:

[0355] The feedback information and re-analyzed sentiment labels are sent to the server and used to further improve the preference model.

[0356] Step 9:

[0357] The server predicts market trends based on updated preference models and sentiment data, and provides this information to real estate businesses. These businesses then use this information to personalize customer service.

[0358] In this way, a property information provision system incorporating an emotion engine operates effectively by taking into account both the user's preferences and emotions.

[0359] (Example 2)

[0360] 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".

[0361] Traditional real estate information systems only suggest properties based on the user's search criteria, resulting in insufficient personalization that takes into account the user's emotional tendencies and detailed preferences. Furthermore, mechanisms for effectively collecting user feedback and using it to improve future recommendations were limited, making it difficult to provide an advanced customer experience.

[0362] 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.

[0363] In this invention, the server includes means for receiving user input information and sentiment data and predicting the user's preferences based on these, means for extracting optimal property information from a property information database, and means for prioritizing and presenting properties to the user and collecting feedback information from the user. This enables highly accurate property recommendations that take the user's emotions into consideration.

[0364] "User input information" refers to data indicating the conditions and preferences that users provide to the system when searching for properties.

[0365] "Emotional data" refers to data that indicates the user's emotional state, obtained from the user's facial expressions, voice tone, input speed, etc.

[0366] "Methods for predicting preferences" refer to methods that analyze user input information and emotional data to infer what characteristics of properties a user will prefer.

[0367] A "property information database" is a collection of data that systematically stores information related to real estate.

[0368] "Methods for extracting optimal property information" refers to methods of selecting suitable property information from a property information database based on user preference data.

[0369] "Prioritization" is the process of rearranging extracted property information according to the user's preferences.

[0370] "Feedback information" refers to the evaluations and opinions that users provide regarding property information.

[0371] A "preference model" is a profile generated based on data analysis, representing user preferences as a machine learning model.

[0372] "Methods for predicting market trends" refer to methods that use updated preference models and sentiment data to infer future changes in the real estate market.

[0373] "Related businesses" refers to companies and professionals in the real estate industry that utilize the information provided by this system.

[0374] This invention is a real estate information system that more accurately understands user preferences and provides personalized property information. The system consists of three elements: a server, a terminal, and a user. First, the user inputs property search criteria using the terminal. Specifically, they select conditions such as location, floor plan, and price range in the input fields.

[0375] The device utilizes its built-in camera and microphone to collect emotional data, such as facial expressions, voice tone, and input speed, in addition to the entered search criteria. This data is analyzed in real time by software called an emotion engine. The emotion engine can recognize the user's emotional state from their gaze, smile, etc., and classify it as "interested" or "anxious," etc.

[0376] The server plays a central role in collecting and analyzing diverse data. Based on user input and sentiment data, it creates a user preference model using a generative AI model. The generative AI model learns from past data using machine learning algorithms to predict what types of properties the user will be interested in.

[0377] The server extracts suitable properties from the property information database based on a preference model. This selection process utilizes machine learning algorithms, creating a property list with priorities that reflect the user's sentiment. The selected property information is then sent to the terminal and displayed to the user.

[0378] When a user provides feedback on a displayed property list, the device sends that information to the server. The server re-analyzes the feedback information and sentiment data to further refine the preference model. This improves the accuracy of property recommendations in the future.

[0379] A concrete example of a prompt message would be: "Please enter the property criteria the user is looking for, so that the device can analyze their emotions while they are viewing the property details. Also, please enter any other information about the user's emotions and preferences that should be considered."

[0380] This system allows users to select properties based on their essential needs, saving time and resulting in higher satisfaction. Furthermore, real estate businesses can leverage detailed user preference and emotional data to provide more effective services.

[0381] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0382] Step 1:

[0383] Users enter property search criteria using their own devices. This information includes, for example, "region," "price range," and "floor plan." This criteria information is converted into a data format on the device and stored on the client-side application.

[0384] Step 2:

[0385] In addition to input information, the device captures the user's facial expressions with its built-in camera and analyzes their voice tone with its microphone. It also records the user's input speed and mouse movements in real time. This data is processed by an emotion engine to estimate the user's emotional state. For example, a smiling expression or a gentle voice tone is recognized as a "positive emotion."

[0386] Step 3:

[0387] The terminal sends the user's search criteria and sentiment data to the server. The server receives this data and analyzes it using a generative AI model. In this process, a user preference model is generated based on the input data. The preference model learns the characteristics and sentimental tendencies of properties that the user has shown interest in in the past.

[0388] Step 4:

[0389] The server searches the property database using the generated preference model. The database contains detailed information on numerous properties. The server uses a machine learning algorithm to select the properties that best match the user's preferences and sets their priorities. This result is generated in list format and sent to the terminal.

[0390] Step 5:

[0391] The terminal displays a list of properties sent from the server to the user. The list is sorted based on the user's preferences, with higher-priority properties displayed at the top. The user then views this list of properties and provides further evaluations and feedback.

[0392] Step 6:

[0393] User feedback and ratings are resent from the device to the server. The server analyzes this feedback and updates the previously generated preference model. This update further improves the accuracy of property recommendations in the future.

[0394] Step 7:

[0395] The server predicts trends in the real estate market based on updated preference models and collected sentiment data. This analysis is provided to relevant businesses, enabling them to enhance their customer service and marketing strategies.

[0396] (Application Example 2)

[0397] 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."

[0398] Traditional real estate information systems have a low degree of personalization in property selection because they only consider user preferences and fail to adequately reflect emotions in their information provision. Furthermore, they do not dynamically present properties that take into account the user's emotional reactions, resulting in a lack of ability to capture user interest.

[0399] 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.

[0400] In this invention, the server includes means for receiving user input information and emotional information and predicting preferences, means for extracting property information based on preferences and emotional information, and means for recognizing the user's emotional state in real time. This makes it possible to provide personalized property information based on the user's emotions.

[0401] "User input information" refers to information such as requirements, conditions, and keywords that users provide when searching for properties.

[0402] "Emotional information" refers to data on a user's emotional state, analyzed through their facial expressions, voice tone, and other factors.

[0403] "Means of predicting preferences" refer to processes or functions that predict user preferences based on user input information and emotional information.

[0404] "Methods for extracting property information" refers to a mechanism that selects the most suitable property information from a database based on the user's preferences and emotional information.

[0405] "Means of recognizing a user's emotional state" refers to technologies and devices that read a user's facial expressions, voice, etc., in real time and determine their emotions.

[0406] "Recognizing in real time" means processing data instantly and generating results without delay.

[0407] "Personalized property information" refers to customized property information that reflects the individual user's preferences and feelings.

[0408] The system to realize this application example includes a program that analyzes the user's emotions and preferences in real time when selecting a real estate property and provides personalized property information.

[0409] The device uses smart glasses to capture the user's facial expressions and voice in real time. This utilizes hardware such as a facial recognition camera and microphone, and analyzes emotional states using software such as the "EmotionRecognitionAPI." The user also inputs their requests into the device and sends them to the server along with emotional information.

[0410] The server receives this data and uses a generative AI model to create a user preference model. This generative AI model extracts matching properties from the property information database based on real-time sentiment analysis and user profiles.

[0411] For example, if a user smiles when viewing a large living room during a virtual tour, this emotional information is analyzed as "interested," and the server receives feedback to prioritize displaying properties of similar size. By making choices based on this information, users can select properties that lead to a deeper level of satisfaction.

[0412] An example of a prompt for a generative AI model is: "When a user is impressed by a spacious living room, suggest other properties that might be suitable."

[0413] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0414] Step 1:

[0415] The device captures the user's facial expressions and voice in real time through smart glasses. Input data comes from the camera and microphone, which is converted into emotional information using the "EmotionRecognitionAPI". The output is the user's current emotional state. At this stage, the device prepares to send the emotional data to the server.

[0416] Step 2:

[0417] Users enter property search criteria via a terminal. This input includes keywords, conditions, and selections from lists. Based on this, the terminal formats the user's request and sends it to the server. This process organizes search data based on the user's preferences.

[0418] Step 3:

[0419] The server integrates the emotional information and search criteria received from the terminal and begins processing. The input consists of emotional data and search data, and a generative AI model is used to create a user preference model based on these. Through data calculations, the most suitable property information for the user is extracted.

[0420] Step 4:

[0421] The server narrows down the property database based on the user's preferences and sends the extracted property list to the terminal. The terminal then presents the user with customized property information. The output at this stage is the display of the property list to the user.

[0422] Step 5:

[0423] Users provide feedback on the presented properties. This feedback is provided as input, based on their selections. This information is then sent back to the server via the terminal. The user's feedback verifies the consistency between their emotions and actual preferences.

[0424] Step 6:

[0425] The server updates its preference model using collected feedback information. Feedback data serves as input, and data processing refines the preference model. As a result, new market trend information is generated and used to inform future property recommendations.

[0426] 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.

[0427] 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.

[0428] 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.

[0429] [Third Embodiment]

[0430] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0431] 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.

[0432] 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).

[0433] 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.

[0434] 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.

[0435] 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).

[0436] 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.

[0437] 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.

[0438] 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.

[0439] 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.

[0440] 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.

[0441] 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".

[0442] This invention is a real estate information matching system designed to enable users to efficiently find their ideal property. This system utilizes user input information and behavioral data to suggest properties that match the user's preferences and provides feedback on the results to real estate-related businesses.

[0443] The main components of this system consist of three elements: a server, a terminal, and a user. The server plays a central role in data collection, analysis, and preference prediction. Based on user input, it creates a preference model and extracts the most suitable properties from the property information database. The terminal provides an interface with the user, allowing them to search and view properties as requested. Feedback information entered by the user is sent to the server via the terminal and used to update the preference model.

[0444] As a concrete example, when a user sets conditions such as "newly built," "3LDK," and "within a 10-minute walk from the station" on their device and starts a property search, the device sends this information to the server. The server, taking into account the user's past browsing history, extracts properties that match these conditions from its database and sends the most suitable property information to the device. The user then views the displayed property list and examines properties of interest in detail. During the detailed examination, it is also possible to experience the property using virtual reality (VR) or augmented reality (AR). This allows the user to obtain a more concrete image of the property.

[0445] When users leave comments or ratings about a property, that feedback information is sent back to the server. The server analyzes this information and updates the user preference model. By repeating this process, the entire system refines its property information delivery to better match users and enables more accurate predictions of market trends. Real estate businesses are notified of the trend prediction information generated by the server, allowing them to respond quickly to market changes.

[0446] Thus, the present invention realizes a system that provides users with efficient property search and a rich experience, and real estate-related businesses with market insights, thereby providing valuable information to both parties.

[0447] The following describes the processing flow.

[0448] Step 1:

[0449] The user enters their desired property criteria using their device and begins the property search. The user then sets detailed conditions such as "newly built," "3LDK," and "within a 10-minute walk from the station."

[0450] Step 2:

[0451] The terminal sends the search criteria received from the user to the server and makes a request.

[0452] Step 3:

[0453] The server receives the request and searches its property database for properties that match the criteria. The server also analyzes the user's past behavior history and takes into account a preference model to narrow down the properties.

[0454] Step 4:

[0455] The server generates an optimized property list and sends this list to the terminal. The properties selected are prioritized based on the user's needs.

[0456] Step 5:

[0457] The device displays the received property list to the user. The user can view detailed property information and open the details screen for properties that interest them.

[0458] Step 6:

[0459] If a user selects a specific property and desires a visual experience through virtual reality (VR) or augmented reality (AR), the device will perform this action and provide the user with a three-dimensional image of the property.

[0460] Step 7:

[0461] Users enter feedback and ratings about properties on their devices, and this information is sent to the server.

[0462] Step 8:

[0463] The server analyzes user feedback and updates the user preference model. This data is used to improve the accuracy of future property recommendations.

[0464] Step 9:

[0465] The server collects and analyzes data from all users to predict market trends. This information is regularly provided as feedback to real estate businesses, serving as reference material for their business strategies.

[0466] The above describes the processing flow of the program in this system.

[0467] (Example 1)

[0468] 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."

[0469] Conventional real estate information search systems lack the functionality to suggest the most suitable properties based on user preferences and behavior. Furthermore, the means by which users can concretely experience detailed property information are limited, resulting in users having to expend considerable effort in selecting a property. In addition, the provision of information necessary to predict market trends in real time and enable real estate businesses to respond quickly is insufficient.

[0470] 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.

[0471] In this invention, the server includes a device that receives user input data and infers preferences based on said data, a device that extracts optimal data from an information database based on said preferences, and a device that displays the extracted information to the user and collects feedback from the user. As a result, users can efficiently obtain property information that best suits their preferences, and real estate businesses can quickly formulate business plans based on market trend predictions.

[0472] "User input data" refers to information provided by the user via their device to identify their property search criteria and preferences.

[0473] A "preference prediction device" is a system component that has the function of predicting a user's preferences and tendencies based on the user's input data and past behavioral history.

[0474] An "information database" is a collection of information about properties, and serves as a foundation for providing necessary information based on search criteria.

[0475] A "feedback collection device" is a function that receives comments and ratings provided by users, and the system analyzes them to update the user model.

[0476] "Market trend forecasting" is the process of predicting future market demand and trends by utilizing accumulated data and user preference models.

[0477] A "device for supplying information to related businesses" refers to a system component that provides predicted market trend information to real estate-related business operators and supports their business activities.

[0478] This real estate information matching system consists of three elements: server, terminal, and user. The specific roles and processing functions of each are described below.

[0479] First, the server plays a central role in this system and uses a cloud computing platform to process large amounts of data. Specific examples include Amazon Web Services and Microsoft Azure. This server utilizes the Python programming language and machine learning libraries such as TensorFlow and Scikit-learn to analyze user input data and infer preferences. It also executes SQL queries against a property information database to search for properties that match the specified criteria. The results of this analysis are used to update the user preference model and predict market trends.

[0480] Next, the terminal provides an interface for direct interaction with the user. On the terminal, the user can enter property search criteria, which are then sent to the server. In addition, it has the functionality to display detailed property information and search results to the user, and to provide a property experience through virtual reality (VR) and augmented reality (AR). User feedback is transferred from the terminal to the server as important data, further contributing to the improvement of the system's accuracy.

[0481] Users can use the system to efficiently search for their ideal property. Users input specific criteria such as "newly built," "3LDK," and "within a 10-minute walk from the station" via their terminal, and then search for properties based on those criteria. The feedback data accumulated during the search process evolves the user preference model, improving the accuracy of subsequent searches.

[0482] As a concrete example, let's assume the user enters the following prompt:

[0483] "Users are searching for properties that meet the following criteria: 'newly built,' '3LDK,' and 'within a 10-minute walk from the station.' We extract properties that match these criteria from our real estate database and provide detailed information. Past user behavior data is also taken into consideration."

[0484] This system allows users to receive property information that best matches their criteria, and enables real estate businesses to develop strategies to respond quickly to market fluctuations.

[0485] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0486] Step 1:

[0487] Users enter property search criteria using a terminal. Specifically, they specify conditions such as "newly built," "3LDK," and "within a 10-minute walk from the station." The entered data is structured and prepared to be sent to the server via the terminal's user interface.

[0488] Step 2:

[0489] The terminal sends the conditions received from the user to the server. HTTP is generally used as the communication protocol, and the data is encoded in JSON format and securely sent to the server. The transmission is encrypted using SSL / TLS.

[0490] Step 3:

[0491] The server receives conditional data sent from the terminal and begins analysis. Using these conditions as input, the server performs data analysis through a generative AI model utilizing Python and TensorFlow. A preference model that takes into account the user's past behavior data is developed, and a list of suitable property information is generated.

[0492] Step 4:

[0493] The server executes SQL queries against the information database and extracts optimal property information based on the generated preference model. This selects the property information that best matches the user's conditions and preferences, and outputs it in a structured format.

[0494] Step 5:

[0495] The server returns the selected property information to the terminal. The output information includes detailed property information and links that allow the user to experience the property through virtual reality (VR) or augmented reality (AR).

[0496] Step 6:

[0497] The terminal displays property information received from the server to the user. Through the user interface, the user can browse the property list and examine properties of interest in detail. Using VR and AR, the user can virtually experience a more concrete image of the property.

[0498] Step 7:

[0499] Users enter feedback about a property and send it to the server via their device. This feedback includes their impression of the property, areas for improvement, and any additional requests.

[0500] Step 8:

[0501] The server analyzes the received feedback and updates the user preference model. Machine learning algorithms are used for data processing, and the model is trained as needed to improve its accuracy.

[0502] Step 9:

[0503] The server predicts market trends based on updated preference models and notifies real estate businesses. The predicted information is used to adjust business strategies and discover new market opportunities.

[0504] (Application Example 1)

[0505] 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."

[0506] Traditional real estate information systems make it difficult for users to efficiently find their ideal property. In particular, they lack the means to present optimal properties that take into account user preferences and conditions, as well as to make the property experience more realistic. Furthermore, there is a need for effective information delivery methods that can quickly respond to changes in market trends.

[0507] 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.

[0508] In this invention, the server includes means for receiving user input information and predicting the user's preferences based on that information, means for extracting optimal property information from a property information database, and means for visually presenting real estate in a virtual environment. This makes it possible for the user to intuitively operate and experience real estate properties that meet their criteria via a smart device.

[0509] "Means for predicting user preferences" refers to a system that analyzes information entered or provided by the user to predict the user's preferences and tendencies regarding properties.

[0510] A "property information database" is a dataset containing various information about real estate properties, including property details, location, price, and other related information.

[0511] "Means of collecting feedback information" refers to methods of recording evaluations and opinions that users give regarding the property information presented, and using this information for future data analysis and service improvement.

[0512] A "means for updating preference models" refers to a mechanism that utilizes feedback information obtained from users to re-evaluate user preference patterns and improve the accuracy of preference predictions in response to new data.

[0513] "Methods for predicting market trends" refer to algorithms that analyze real estate market trends based on collected data and predict future market changes and demand.

[0514] "Means of visually presenting real estate in a virtual environment" refers to methods that use VR and AR technologies to visually present properties to users in a computer-generated 3D space, providing a realistic experience.

[0515] "A means of intuitively operating and experiencing real estate properties via smart devices" refers to a method in which users can easily search for properties and intuitively manipulate property information in a virtual space using digital devices such as smartphones and smart glasses.

[0516] This invention is a real estate information matching system for users to efficiently find their ideal property, and consists of a server, a terminal, and a user. The server implements an algorithm that processes user input information and predicts preferences using a program written in Python or JavaScript. This algorithm analyzes past user behavior data and feedback information to predict user preferences and extracts the most suitable property information from the property information database.

[0517] The device functions as the user interface, enabling intuitive operation using smartphones or smart glasses. By utilizing software such as Unity and Unreal Engine, an environment is created that visually presents properties through virtual reality or augmented reality technology. This allows users to experience properties in a way that closely resembles the real world.

[0518] For example, when a user enters criteria such as "newly built," "3LDK," and "within a 10-minute walk from the station" via a device and starts a search, the server extracts matching properties from its database, taking into account the user's past browsing history, and sends the information to the device. The user can then virtually visit the displayed properties through smart glasses and experience the atmosphere of the actual property.

[0519] User feedback is automatically transferred to the server and used as data to generate new preference models. In this way, the system can continuously improve the user experience. It also predicts market trends and provides useful information to real estate businesses.

[0520] An example of a prompt message generated using an AI model might be: "The user is searching for a pet-friendly 2LDK property. Please retrieve the best matching property information and set it up so that the user can experience it in a virtual space." Based on this prompt message, the AI ​​can quickly suggest the most suitable property information.

[0521] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0522] Step 1:

[0523] The user uses a terminal to enter property search criteria. These criteria include location, floor plan, and building type. The terminal sends this input information to the server.

[0524] Step 2:

[0525] The server queries the property information database based on the received search criteria to extract candidate properties. The database uses SQL queries and other methods to quickly search for properties that match the criteria, organizes the information, and outputs it. The obtained property information is then processed in conjunction with user preference prediction parameters.

[0526] Step 3:

[0527] The server uses a generative AI model to select the property that best suits the user's preferences from extracted property information, taking into account the user's past behavior data and feedback. In this process, a data analysis algorithm combines preference parameters and property information to output individually optimized results.

[0528] Step 4:

[0529] The server sends the selected property information to the terminal. The terminal visually presents the received property information to the user. When using VR or AR technology, a virtual space is constructed using an engine such as Unity, allowing the user to realistically experience the property.

[0530] Step 5:

[0531] Users view the presented properties and virtually visit them through their smart devices. They then input feedback about this virtual visit into their devices. This feedback includes the user's level of interest and specific comments.

[0532] Step 6:

[0533] The device collects user feedback and sends it to the server. The server analyzes this feedback information and initiates a process to update the user preference model. Machine learning algorithms are used to optimize the model based on user preferences.

[0534] Step 7:

[0535] The server predicts market trends based on updated preference models and provides the latest trend information to real estate businesses. This information is provided in real time via API, allowing businesses to adjust their market strategies accordingly.

[0536] 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.

[0537] This invention is a real estate information system that takes into account the user's preferences and emotions when choosing a property, and provides more personalized property information. This system incorporates an emotion engine that recognizes the user's emotional state, creating a profile that includes the user's emotions, and contributing to property matching.

[0538] The core of the system consists of the server, terminal, and user. First, the user enters property search criteria via the terminal, and the resulting operations and reactions to the search results are sent to the server in real time. The terminal uses data such as the user's facial expressions, voice tone, and input speed to perform sentiment analysis using an emotion engine. For example, if a user is looking at the details of a property and is smiling while intently viewing the screen, the emotion engine recognizes that emotion as "interest."

[0539] The server creates a preference model that reflects the user's tastes and emotions based on the collected data. Based on this model, it extracts the most suitable property information from the property database and sends it to the terminal. This information is displayed to the user, and the order of the suggested property list is dynamically adjusted according to their emotions. For example, properties for which the emotion engine detects a "positive emotion" are displayed higher in the list.

[0540] The device sends back information to the server, including feedback and ratings that users have given about properties. The server analyzes the feedback and sentiment information to update the preference model, making it more accurate. In addition, market trends are analyzed based on this model and sentiment information, and this information is provided to real estate businesses, which can then use it to develop strategies for providing personalized services to their customers.

[0541] This system facilitates the streamlining and personalization of property selection for users, enabling smarter real estate choices. Furthermore, for real estate businesses, it allows for the provision of differentiated customer services by utilizing detailed customer profiles that include users' emotional tendencies. Thus, a property information provision system that integrates user preferences and emotions represents the primary form of this invention.

[0542] The following describes the processing flow.

[0543] Step 1:

[0544] The user enters property search criteria using their device and starts the search. The user sets conditions such as "newly built," "2LDK," and "with balcony."

[0545] Step 2:

[0546] The device sends the entered search criteria to the server via an emotion engine. It also simultaneously collects the user's facial expressions and voice for emotion recognition.

[0547] Step 3:

[0548] The emotion engine analyzes the user's facial expressions and voice to generate emotion labels such as "interested," "indifferent," and "dissatisfied."

[0549] Step 4:

[0550] The server receives emotion labels and search criteria, and searches the property database, taking into account the user's preference model. Relevant properties are prioritized based on the user's positive emotions.

[0551] Step 5:

[0552] The server generates an optimized property list, adjusts the order of the list based on sentiment information, and then sends it to the terminal.

[0553] Step 6:

[0554] The device displays a list of properties to the user. When the user selects an item from the list, detailed information about that property is displayed.

[0555] Step 7:

[0556] Users input their feelings and evaluations about a property and provide feedback through their device. The emotion engine re-analyzes the emotional information as needed.

[0557] Step 8:

[0558] The feedback information and re-analyzed sentiment labels are sent to the server and used to further improve the preference model.

[0559] Step 9:

[0560] The server predicts market trends based on updated preference models and sentiment data, and provides this information to real estate businesses. These businesses then use this information to personalize customer service.

[0561] In this way, a property information provision system incorporating an emotion engine operates effectively by taking into account both the user's preferences and emotions.

[0562] (Example 2)

[0563] 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."

[0564] Traditional real estate information systems only suggest properties based on the user's search criteria, resulting in insufficient personalization that takes into account the user's emotional tendencies and detailed preferences. Furthermore, mechanisms for effectively collecting user feedback and using it to improve future recommendations were limited, making it difficult to provide an advanced customer experience.

[0565] 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.

[0566] In this invention, the server includes means for receiving user input information and sentiment data and predicting the user's preferences based on these, means for extracting optimal property information from a property information database, and means for prioritizing and presenting properties to the user and collecting feedback information from the user. This enables highly accurate property recommendations that take the user's emotions into consideration.

[0567] "User input information" refers to data indicating the conditions and preferences that users provide to the system when searching for properties.

[0568] "Emotional data" refers to data that indicates the user's emotional state, obtained from the user's facial expressions, voice tone, input speed, etc.

[0569] "Methods for predicting preferences" refer to methods that analyze user input information and emotional data to infer what characteristics of properties a user will prefer.

[0570] A "property information database" is a collection of data that systematically stores information related to real estate.

[0571] "Methods for extracting optimal property information" refers to methods of selecting suitable property information from a property information database based on user preference data.

[0572] "Prioritization" is the process of rearranging extracted property information according to the user's preferences.

[0573] "Feedback information" refers to the evaluations and opinions that users provide regarding property information.

[0574] A "preference model" is a profile generated based on data analysis, representing user preferences as a machine learning model.

[0575] "Methods for predicting market trends" refer to methods that use updated preference models and sentiment data to infer future changes in the real estate market.

[0576] "Related businesses" refers to companies and professionals in the real estate industry that utilize the information provided by this system.

[0577] This invention is a real estate information system that more accurately understands user preferences and provides personalized property information. The system consists of three elements: a server, a terminal, and a user. First, the user inputs property search criteria using the terminal. Specifically, they select conditions such as location, floor plan, and price range in the input fields.

[0578] The device utilizes its built-in camera and microphone to collect emotional data, such as facial expressions, voice tone, and input speed, in addition to the entered search criteria. This data is analyzed in real time by software called an emotion engine. The emotion engine can recognize the user's emotional state from their gaze, smile, etc., and classify it as "interested" or "anxious," etc.

[0579] The server plays a central role in collecting and analyzing diverse data. Based on user input and sentiment data, it creates a user preference model using a generative AI model. The generative AI model learns from past data using machine learning algorithms to predict what types of properties the user will be interested in.

[0580] The server extracts suitable properties from the property information database based on a preference model. This selection process utilizes machine learning algorithms, creating a property list with priorities that reflect the user's sentiment. The selected property information is then sent to the terminal and displayed to the user.

[0581] When a user provides feedback on a displayed property list, the device sends that information to the server. The server re-analyzes the feedback information and sentiment data to further refine the preference model. This improves the accuracy of property recommendations in the future.

[0582] A concrete example of a prompt message would be: "Please enter the property criteria the user is looking for, so that the device can analyze their emotions while they are viewing the property details. Also, please enter any other information about the user's emotions and preferences that should be considered."

[0583] This system allows users to select properties based on their essential needs, saving time and resulting in higher satisfaction. Furthermore, real estate businesses can leverage detailed user preference and emotional data to provide more effective services.

[0584] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0585] Step 1:

[0586] Users enter property search criteria using their own devices. This information includes, for example, "region," "price range," and "floor plan." This criteria information is converted into a data format on the device and stored on the client-side application.

[0587] Step 2:

[0588] In addition to input information, the device captures the user's facial expressions with its built-in camera and analyzes their voice tone with its microphone. It also records the user's input speed and mouse movements in real time. This data is processed by an emotion engine to estimate the user's emotional state. For example, a smiling expression or a gentle voice tone is recognized as a "positive emotion."

[0589] Step 3:

[0590] The terminal sends the user's search criteria and sentiment data to the server. The server receives this data and analyzes it using a generative AI model. In this process, a user preference model is generated based on the input data. The preference model learns the characteristics and sentimental tendencies of properties that the user has shown interest in in the past.

[0591] Step 4:

[0592] The server searches the property database using the generated preference model. The database contains detailed information on numerous properties. The server uses a machine learning algorithm to select the properties that best match the user's preferences and sets their priorities. This result is generated in list format and sent to the terminal.

[0593] Step 5:

[0594] The terminal displays a list of properties sent from the server to the user. The list is sorted based on the user's preferences, with higher-priority properties displayed at the top. The user then views this list of properties and provides further evaluations and feedback.

[0595] Step 6:

[0596] User feedback and ratings are resent from the device to the server. The server analyzes this feedback and updates the previously generated preference model. This update further improves the accuracy of property recommendations in the future.

[0597] Step 7:

[0598] The server predicts trends in the real estate market based on updated preference models and collected sentiment data. This analysis is provided to relevant businesses, enabling them to enhance their customer service and marketing strategies.

[0599] (Application Example 2)

[0600] 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."

[0601] Traditional real estate information systems have a low degree of personalization in property selection because they only consider user preferences and fail to adequately reflect emotions in their information provision. Furthermore, they do not dynamically present properties that take into account the user's emotional reactions, resulting in a lack of ability to capture user interest.

[0602] 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.

[0603] In this invention, the server includes means for receiving user input information and emotional information and predicting preferences, means for extracting property information based on preferences and emotional information, and means for recognizing the user's emotional state in real time. This makes it possible to provide personalized property information based on the user's emotions.

[0604] "User input information" refers to information such as requirements, conditions, and keywords that users provide when searching for properties.

[0605] "Emotional information" refers to data on a user's emotional state, analyzed through their facial expressions, voice tone, and other factors.

[0606] "Means of predicting preferences" refer to processes or functions that predict user preferences based on user input information and emotional information.

[0607] "Methods for extracting property information" refers to a mechanism that selects the most suitable property information from a database based on the user's preferences and emotional information.

[0608] "Means of recognizing a user's emotional state" refers to technologies and devices that read a user's facial expressions, voice, etc., in real time and determine their emotions.

[0609] "Recognizing in real time" means processing data instantly and generating results without delay.

[0610] "Personalized property information" refers to customized property information that reflects the individual user's preferences and feelings.

[0611] The system to realize this application example includes a program that analyzes the user's emotions and preferences in real time when selecting a real estate property and provides personalized property information.

[0612] The device uses smart glasses to capture the user's facial expressions and voice in real time. This utilizes hardware such as a facial recognition camera and microphone, and analyzes emotional states using software such as the "EmotionRecognitionAPI." The user also inputs their requests into the device and sends them to the server along with emotional information.

[0613] The server receives this data and uses a generative AI model to create a user preference model. This generative AI model extracts matching properties from the property information database based on real-time sentiment analysis and user profiles.

[0614] For example, if a user smiles when viewing a large living room during a virtual tour, this emotional information is analyzed as "interested," and the server receives feedback to prioritize displaying properties of similar size. By making choices based on this information, users can select properties that lead to a deeper level of satisfaction.

[0615] An example of a prompt for a generative AI model is: "When a user is impressed by a spacious living room, suggest other properties that might be suitable."

[0616] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0617] Step 1:

[0618] The device captures the user's facial expressions and voice in real time through smart glasses. Input data comes from the camera and microphone, which is converted into emotional information using the "EmotionRecognitionAPI". The output is the user's current emotional state. At this stage, the device prepares to send the emotional data to the server.

[0619] Step 2:

[0620] Users enter property search criteria via a terminal. This input includes keywords, conditions, and selections from lists. Based on this, the terminal formats the user's request and sends it to the server. This process organizes search data based on the user's preferences.

[0621] Step 3:

[0622] The server integrates the emotional information and search criteria received from the terminal and begins processing. The input consists of emotional data and search data, and a generative AI model is used to create a user preference model based on these. Through data calculations, the most suitable property information for the user is extracted.

[0623] Step 4:

[0624] The server narrows down the property database based on the user's preferences and sends the extracted property list to the terminal. The terminal then presents the user with customized property information. The output at this stage is the display of the property list to the user.

[0625] Step 5:

[0626] Users provide feedback on the presented properties. This feedback is provided as input, based on their selections. This information is then sent back to the server via the terminal. The user's feedback verifies the consistency between their emotions and actual preferences.

[0627] Step 6:

[0628] The server updates its preference model using collected feedback information. Feedback data serves as input, and data processing refines the preference model. As a result, new market trend information is generated and used to inform future property recommendations.

[0629] 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.

[0630] 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.

[0631] 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.

[0632] [Fourth Embodiment]

[0633] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0634] 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.

[0635] 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).

[0636] 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.

[0637] 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.

[0638] 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).

[0639] 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.

[0640] 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.

[0641] 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.

[0642] 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.

[0643] 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.

[0644] 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.

[0645] 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".

[0646] This invention is a real estate information matching system designed to enable users to efficiently find their ideal property. This system utilizes user input information and behavioral data to suggest properties that match the user's preferences and provides feedback on the results to real estate-related businesses.

[0647] The main components of this system consist of three elements: a server, a terminal, and a user. The server plays a central role in data collection, analysis, and preference prediction. Based on user input, it creates a preference model and extracts the most suitable properties from the property information database. The terminal provides an interface with the user, allowing them to search and view properties as requested. Feedback information entered by the user is sent to the server via the terminal and used to update the preference model.

[0648] As a concrete example, when a user sets conditions such as "newly built," "3LDK," and "within a 10-minute walk from the station" on their device and starts a property search, the device sends this information to the server. The server, taking into account the user's past browsing history, extracts properties that match these conditions from its database and sends the most suitable property information to the device. The user then views the displayed property list and examines properties of interest in detail. During the detailed examination, it is also possible to experience the property using virtual reality (VR) or augmented reality (AR). This allows the user to obtain a more concrete image of the property.

[0649] When users leave comments or ratings about a property, that feedback information is sent back to the server. The server analyzes this information and updates the user preference model. By repeating this process, the entire system refines its property information delivery to better match users and enables more accurate predictions of market trends. Real estate businesses are notified of the trend prediction information generated by the server, allowing them to respond quickly to market changes.

[0650] Thus, the present invention realizes a system that provides users with efficient property search and a rich experience, and real estate-related businesses with market insights, thereby providing valuable information to both parties.

[0651] The following describes the processing flow.

[0652] Step 1:

[0653] The user enters their desired property criteria using their device and begins the property search. The user then sets detailed conditions such as "newly built," "3LDK," and "within a 10-minute walk from the station."

[0654] Step 2:

[0655] The terminal sends the search criteria received from the user to the server and makes a request.

[0656] Step 3:

[0657] The server receives the request and searches its property database for properties that match the criteria. The server also analyzes the user's past behavior history and takes into account a preference model to narrow down the properties.

[0658] Step 4:

[0659] The server generates an optimized property list and sends this list to the terminal. The properties selected are prioritized based on the user's needs.

[0660] Step 5:

[0661] The device displays the received property list to the user. The user can view detailed property information and open the details screen for properties that interest them.

[0662] Step 6:

[0663] If a user selects a specific property and desires a visual experience through virtual reality (VR) or augmented reality (AR), the device will perform this action and provide the user with a three-dimensional image of the property.

[0664] Step 7:

[0665] Users enter feedback and ratings about properties on their devices, and this information is sent to the server.

[0666] Step 8:

[0667] The server analyzes user feedback and updates the user preference model. This data is used to improve the accuracy of future property recommendations.

[0668] Step 9:

[0669] The server collects and analyzes data from all users to predict market trends. This information is regularly provided as feedback to real estate businesses, serving as reference material for their business strategies.

[0670] The above describes the processing flow of the program in this system.

[0671] (Example 1)

[0672] 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".

[0673] Conventional real estate information search systems lack the functionality to suggest the most suitable properties based on user preferences and behavior. Furthermore, the means by which users can concretely experience detailed property information are limited, resulting in users having to expend considerable effort in selecting a property. In addition, the provision of information necessary to predict market trends in real time and enable real estate businesses to respond quickly is insufficient.

[0674] 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.

[0675] In this invention, the server includes a device that receives user input data and infers preferences based on said data, a device that extracts optimal data from an information database based on said preferences, and a device that displays the extracted information to the user and collects feedback from the user. As a result, users can efficiently obtain property information that best suits their preferences, and real estate businesses can quickly formulate business plans based on market trend predictions.

[0676] "User input data" refers to information provided by the user via their device to identify their property search criteria and preferences.

[0677] A "preference prediction device" is a system component that has the function of predicting a user's preferences and tendencies based on the user's input data and past behavioral history.

[0678] An "information database" is a collection of information about properties, and serves as a foundation for providing necessary information based on search criteria.

[0679] A "feedback collection device" is a function that receives comments and ratings provided by users, and the system analyzes them to update the user model.

[0680] "Market trend forecasting" is the process of predicting future market demand and trends by utilizing accumulated data and user preference models.

[0681] A "device for supplying information to related businesses" refers to a system component that provides predicted market trend information to real estate-related business operators and supports their business activities.

[0682] This real estate information matching system consists of three elements: server, terminal, and user. The specific roles and processing functions of each are described below.

[0683] First, the server plays a central role in this system and uses a cloud computing platform to process large amounts of data. Specific examples include Amazon Web Services and Microsoft Azure. This server utilizes the Python programming language and machine learning libraries such as TensorFlow and Scikit-learn to analyze user input data and infer preferences. It also executes SQL queries against a property information database to search for properties that match the specified criteria. The results of this analysis are used to update the user preference model and predict market trends.

[0684] Next, the terminal provides an interface for direct interaction with the user. On the terminal, the user can enter property search criteria, which are then sent to the server. In addition, it has the functionality to display detailed property information and search results to the user, and to provide a property experience through virtual reality (VR) and augmented reality (AR). User feedback is transferred from the terminal to the server as important data, further contributing to the improvement of the system's accuracy.

[0685] Users can use the system to efficiently search for their ideal property. Users input specific criteria such as "newly built," "3LDK," and "within a 10-minute walk from the station" via their terminal, and then search for properties based on those criteria. The feedback data accumulated during the search process evolves the user preference model, improving the accuracy of subsequent searches.

[0686] As a concrete example, let's assume the user enters the following prompt:

[0687] "Users are searching for properties that meet the following criteria: 'newly built,' '3LDK,' and 'within a 10-minute walk from the station.' We extract properties that match these criteria from our real estate database and provide detailed information. Past user behavior data is also taken into consideration."

[0688] This system allows users to receive property information that best matches their criteria, and enables real estate businesses to develop strategies to respond quickly to market fluctuations.

[0689] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0690] Step 1:

[0691] Users enter property search criteria using a terminal. Specifically, they specify conditions such as "newly built," "3LDK," and "within a 10-minute walk from the station." The entered data is structured and prepared to be sent to the server via the terminal's user interface.

[0692] Step 2:

[0693] The terminal sends the conditions received from the user to the server. HTTP is generally used as the communication protocol, and the data is encoded in JSON format and securely sent to the server. The transmission is encrypted using SSL / TLS.

[0694] Step 3:

[0695] The server receives conditional data sent from the terminal and begins analysis. Using these conditions as input, the server performs data analysis through a generative AI model utilizing Python and TensorFlow. A preference model that takes into account the user's past behavior data is developed, and a list of suitable property information is generated.

[0696] Step 4:

[0697] The server executes SQL queries against the information database and extracts optimal property information based on the generated preference model. This selects the property information that best matches the user's conditions and preferences, and outputs it in a structured format.

[0698] Step 5:

[0699] The server returns the selected property information to the terminal. The output information includes detailed property information and links that allow the user to experience the property through virtual reality (VR) or augmented reality (AR).

[0700] Step 6:

[0701] The terminal displays property information received from the server to the user. Through the user interface, the user can browse the property list and examine properties of interest in detail. Using VR and AR, the user can virtually experience a more concrete image of the property.

[0702] Step 7:

[0703] Users enter feedback about a property and send it to the server via their device. This feedback includes their impression of the property, areas for improvement, and any additional requests.

[0704] Step 8:

[0705] The server analyzes the received feedback and updates the user preference model. Machine learning algorithms are used for data processing, and the model is trained as needed to improve its accuracy.

[0706] Step 9:

[0707] The server predicts market trends based on updated preference models and notifies real estate businesses. The predicted information is used to adjust business strategies and discover new market opportunities.

[0708] (Application Example 1)

[0709] 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".

[0710] Traditional real estate information systems make it difficult for users to efficiently find their ideal property. In particular, they lack the means to present optimal properties that take into account user preferences and conditions, as well as to make the property experience more realistic. Furthermore, there is a need for effective information delivery methods that can quickly respond to changes in market trends.

[0711] 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.

[0712] In this invention, the server includes means for receiving user input information and predicting the user's preferences based on that information, means for extracting optimal property information from a property information database, and means for visually presenting real estate in a virtual environment. This makes it possible for the user to intuitively operate and experience real estate properties that meet their criteria via a smart device.

[0713] "Means for predicting user preferences" refers to a system that analyzes information entered or provided by the user to predict the user's preferences and tendencies regarding properties.

[0714] A "property information database" is a dataset containing various information about real estate properties, including property details, location, price, and other related information.

[0715] "Means of collecting feedback information" refers to methods of recording evaluations and opinions that users give regarding the property information presented, and using this information for future data analysis and service improvement.

[0716] A "means for updating preference models" refers to a mechanism that utilizes feedback information obtained from users to re-evaluate user preference patterns and improve the accuracy of preference predictions in response to new data.

[0717] "Methods for predicting market trends" refer to algorithms that analyze real estate market trends based on collected data and predict future market changes and demand.

[0718] "Means of visually presenting real estate in a virtual environment" refers to methods that use VR and AR technologies to visually present properties to users in a computer-generated 3D space, providing a realistic experience.

[0719] "A means of intuitively operating and experiencing real estate properties via smart devices" refers to a method in which users can easily search for properties and intuitively manipulate property information in a virtual space using digital devices such as smartphones and smart glasses.

[0720] This invention is a real estate information matching system for users to efficiently find their ideal property, and consists of a server, a terminal, and a user. The server implements an algorithm that processes user input information and predicts preferences using a program written in Python or JavaScript. This algorithm analyzes past user behavior data and feedback information to predict user preferences and extracts the most suitable property information from the property information database.

[0721] The device functions as the user interface, enabling intuitive operation using smartphones or smart glasses. By utilizing software such as Unity and Unreal Engine, an environment is created that visually presents properties through virtual reality or augmented reality technology. This allows users to experience properties in a way that closely resembles the real world.

[0722] For example, when a user enters criteria such as "newly built," "3LDK," and "within a 10-minute walk from the station" via a device and starts a search, the server extracts matching properties from its database, taking into account the user's past browsing history, and sends the information to the device. The user can then virtually visit the displayed properties through smart glasses and experience the atmosphere of the actual property.

[0723] User feedback is automatically transferred to the server and used as data to generate new preference models. In this way, the system can continuously improve the user experience. It also predicts market trends and provides useful information to real estate businesses.

[0724] An example of a prompt message generated using an AI model might be: "The user is searching for a pet-friendly 2LDK property. Please retrieve the best matching property information and set it up so that the user can experience it in a virtual space." Based on this prompt message, the AI ​​can quickly suggest the most suitable property information.

[0725] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0726] Step 1:

[0727] The user uses a terminal to enter property search criteria. These criteria include location, floor plan, and building type. The terminal sends this input information to the server.

[0728] Step 2:

[0729] The server queries the property information database based on the received search criteria to extract candidate properties. The database uses SQL queries and other methods to quickly search for properties that match the criteria, organizes the information, and outputs it. The obtained property information is then processed in conjunction with user preference prediction parameters.

[0730] Step 3:

[0731] The server uses a generative AI model to select the property that best suits the user's preferences from extracted property information, taking into account the user's past behavior data and feedback. In this process, a data analysis algorithm combines preference parameters and property information to output individually optimized results.

[0732] Step 4:

[0733] The server sends the selected property information to the terminal. The terminal visually presents the received property information to the user. When using VR or AR technology, a virtual space is constructed using an engine such as Unity, allowing the user to realistically experience the property.

[0734] Step 5:

[0735] Users view the presented properties and virtually visit them through their smart devices. They then input feedback about this virtual visit into their devices. This feedback includes the user's level of interest and specific comments.

[0736] Step 6:

[0737] The device collects user feedback and sends it to the server. The server analyzes this feedback information and initiates a process to update the user preference model. Machine learning algorithms are used to optimize the model based on user preferences.

[0738] Step 7:

[0739] The server predicts market trends based on updated preference models and provides the latest trend information to real estate businesses. This information is provided in real time via API, allowing businesses to adjust their market strategies accordingly.

[0740] 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.

[0741] This invention is a real estate information system that takes into account the user's preferences and emotions when choosing a property, and provides more personalized property information. This system incorporates an emotion engine that recognizes the user's emotional state, creating a profile that includes the user's emotions, and contributing to property matching.

[0742] The core of the system consists of the server, terminal, and user. First, the user enters property search criteria via the terminal, and the resulting operations and reactions to the search results are sent to the server in real time. The terminal uses data such as the user's facial expressions, voice tone, and input speed to perform sentiment analysis using an emotion engine. For example, if a user is looking at the details of a property and is smiling while intently viewing the screen, the emotion engine recognizes that emotion as "interest."

[0743] The server creates a preference model that reflects the user's tastes and emotions based on the collected data. Based on this model, it extracts the most suitable property information from the property database and sends it to the terminal. This information is displayed to the user, and the order of the suggested property list is dynamically adjusted according to their emotions. For example, properties for which the emotion engine detects a "positive emotion" are displayed higher in the list.

[0744] The device sends back information to the server, including feedback and ratings that users have given about properties. The server analyzes the feedback and sentiment information to update the preference model, making it more accurate. In addition, market trends are analyzed based on this model and sentiment information, and this information is provided to real estate businesses, which can then use it to develop strategies for providing personalized services to their customers.

[0745] This system facilitates the streamlining and personalization of property selection for users, enabling smarter real estate choices. Furthermore, for real estate businesses, it allows for the provision of differentiated customer services by utilizing detailed customer profiles that include users' emotional tendencies. Thus, a property information provision system that integrates user preferences and emotions represents the primary form of this invention.

[0746] The following describes the processing flow.

[0747] Step 1:

[0748] The user enters property search criteria using their device and starts the search. The user sets conditions such as "newly built," "2LDK," and "with balcony."

[0749] Step 2:

[0750] The device sends the entered search criteria to the server via an emotion engine. It also simultaneously collects the user's facial expressions and voice for emotion recognition.

[0751] Step 3:

[0752] The emotion engine analyzes the user's facial expressions and voice to generate emotion labels such as "interested," "indifferent," and "dissatisfied."

[0753] Step 4:

[0754] The server receives emotion labels and search criteria, and searches the property database, taking into account the user's preference model. Relevant properties are prioritized based on the user's positive emotions.

[0755] Step 5:

[0756] The server generates an optimized property list, adjusts the order of the list based on sentiment information, and then sends it to the terminal.

[0757] Step 6:

[0758] The device displays a list of properties to the user. When the user selects an item from the list, detailed information about that property is displayed.

[0759] Step 7:

[0760] Users input their feelings and evaluations about a property and provide feedback through their device. The emotion engine re-analyzes the emotional information as needed.

[0761] Step 8:

[0762] The feedback information and re-analyzed sentiment labels are sent to the server and used to further improve the preference model.

[0763] Step 9:

[0764] The server predicts market trends based on updated preference models and sentiment data, and provides this information to real estate businesses. These businesses then use this information to personalize customer service.

[0765] In this way, a property information provision system incorporating an emotion engine operates effectively by taking into account both the user's preferences and emotions.

[0766] (Example 2)

[0767] 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".

[0768] Traditional real estate information systems only suggest properties based on the user's search criteria, resulting in insufficient personalization that takes into account the user's emotional tendencies and detailed preferences. Furthermore, mechanisms for effectively collecting user feedback and using it to improve future recommendations were limited, making it difficult to provide an advanced customer experience.

[0769] 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.

[0770] In this invention, the server includes means for receiving user input information and sentiment data and predicting the user's preferences based on these, means for extracting optimal property information from a property information database, and means for prioritizing and presenting properties to the user and collecting feedback information from the user. This enables highly accurate property recommendations that take the user's emotions into consideration.

[0771] "User input information" refers to data indicating the conditions and preferences that users provide to the system when searching for properties.

[0772] "Emotional data" refers to data that indicates the user's emotional state, obtained from the user's facial expressions, voice tone, input speed, etc.

[0773] "Methods for predicting preferences" refer to methods that analyze user input information and emotional data to infer what characteristics of properties a user will prefer.

[0774] A "property information database" is a collection of data that systematically stores information related to real estate.

[0775] "Methods for extracting optimal property information" refers to methods of selecting suitable property information from a property information database based on user preference data.

[0776] "Prioritization" is the process of rearranging extracted property information according to the user's preferences.

[0777] "Feedback information" refers to the evaluations and opinions that users provide regarding property information.

[0778] A "preference model" is a profile generated based on data analysis, representing user preferences as a machine learning model.

[0779] "Methods for predicting market trends" refer to methods that use updated preference models and sentiment data to infer future changes in the real estate market.

[0780] "Related businesses" refers to companies and professionals in the real estate industry that utilize the information provided by this system.

[0781] This invention is a real estate information system that more accurately understands user preferences and provides personalized property information. The system consists of three elements: a server, a terminal, and a user. First, the user inputs property search criteria using the terminal. Specifically, they select conditions such as location, floor plan, and price range in the input fields.

[0782] The device utilizes its built-in camera and microphone to collect emotional data, such as facial expressions, voice tone, and input speed, in addition to the entered search criteria. This data is analyzed in real time by software called an emotion engine. The emotion engine can recognize the user's emotional state from their gaze, smile, etc., and classify it as "interested" or "anxious," etc.

[0783] The server plays a central role in collecting and analyzing diverse data. Based on user input and sentiment data, it creates a user preference model using a generative AI model. The generative AI model learns from past data using machine learning algorithms to predict what types of properties the user will be interested in.

[0784] The server extracts suitable properties from the property information database based on a preference model. This selection process utilizes machine learning algorithms, creating a property list with priorities that reflect the user's sentiment. The selected property information is then sent to the terminal and displayed to the user.

[0785] When a user provides feedback on a displayed property list, the device sends that information to the server. The server re-analyzes the feedback information and sentiment data to further refine the preference model. This improves the accuracy of property recommendations in the future.

[0786] A concrete example of a prompt message would be: "Please enter the property criteria the user is looking for, so that the device can analyze their emotions while they are viewing the property details. Also, please enter any other information about the user's emotions and preferences that should be considered."

[0787] This system allows users to select properties based on their essential needs, saving time and resulting in higher satisfaction. Furthermore, real estate businesses can leverage detailed user preference and emotional data to provide more effective services.

[0788] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0789] Step 1:

[0790] Users enter property search criteria using their own devices. This information includes, for example, "region," "price range," and "floor plan." This criteria information is converted into a data format on the device and stored on the client-side application.

[0791] Step 2:

[0792] In addition to input information, the device captures the user's facial expressions with its built-in camera and analyzes their voice tone with its microphone. It also records the user's input speed and mouse movements in real time. This data is processed by an emotion engine to estimate the user's emotional state. For example, a smiling expression or a gentle voice tone is recognized as a "positive emotion."

[0793] Step 3:

[0794] The terminal sends the user's search criteria and sentiment data to the server. The server receives this data and analyzes it using a generative AI model. In this process, a user preference model is generated based on the input data. The preference model learns the characteristics and sentimental tendencies of properties that the user has shown interest in in the past.

[0795] Step 4:

[0796] The server searches the property database using the generated preference model. The database contains detailed information on numerous properties. The server uses a machine learning algorithm to select the properties that best match the user's preferences and sets their priorities. This result is generated in list format and sent to the terminal.

[0797] Step 5:

[0798] The terminal displays a list of properties sent from the server to the user. The list is sorted based on the user's preferences, with higher-priority properties displayed at the top. The user then views this list of properties and provides further evaluations and feedback.

[0799] Step 6:

[0800] User feedback and ratings are resent from the device to the server. The server analyzes this feedback and updates the previously generated preference model. This update further improves the accuracy of property recommendations in the future.

[0801] Step 7:

[0802] The server predicts trends in the real estate market based on updated preference models and collected sentiment data. This analysis is provided to relevant businesses, enabling them to enhance their customer service and marketing strategies.

[0803] (Application Example 2)

[0804] 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".

[0805] Traditional real estate information systems have a low degree of personalization in property selection because they only consider user preferences and fail to adequately reflect emotions in their information provision. Furthermore, they do not dynamically present properties that take into account the user's emotional reactions, resulting in a lack of ability to capture user interest.

[0806] 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.

[0807] In this invention, the server includes means for receiving user input information and emotional information and predicting preferences, means for extracting property information based on preferences and emotional information, and means for recognizing the user's emotional state in real time. This makes it possible to provide personalized property information based on the user's emotions.

[0808] "User input information" refers to information such as requirements, conditions, and keywords that users provide when searching for properties.

[0809] "Emotional information" refers to data on a user's emotional state, analyzed through their facial expressions, voice tone, and other factors.

[0810] "Means of predicting preferences" refer to processes or functions that predict user preferences based on user input information and emotional information.

[0811] "Methods for extracting property information" refers to a mechanism that selects the most suitable property information from a database based on the user's preferences and emotional information.

[0812] "Means of recognizing a user's emotional state" refers to technologies and devices that read a user's facial expressions, voice, etc., in real time and determine their emotions.

[0813] "Recognizing in real time" means processing data instantly and generating results without delay.

[0814] "Personalized property information" refers to customized property information that reflects the individual user's preferences and feelings.

[0815] The system to realize this application example includes a program that analyzes the user's emotions and preferences in real time when selecting a real estate property and provides personalized property information.

[0816] The device uses smart glasses to capture the user's facial expressions and voice in real time. This utilizes hardware such as a facial recognition camera and microphone, and analyzes emotional states using software such as the "EmotionRecognitionAPI." The user also inputs their requests into the device and sends them to the server along with emotional information.

[0817] The server receives this data and uses a generative AI model to create a user preference model. This generative AI model extracts matching properties from the property information database based on real-time sentiment analysis and user profiles.

[0818] For example, if a user smiles when viewing a large living room during a virtual tour, this emotional information is analyzed as "interested," and the server receives feedback to prioritize displaying properties of similar size. By making choices based on this information, users can select properties that lead to a deeper level of satisfaction.

[0819] An example of a prompt for a generative AI model is: "When a user is impressed by a spacious living room, suggest other properties that might be suitable."

[0820] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0821] Step 1:

[0822] The device captures the user's facial expressions and voice in real time through smart glasses. Input data comes from the camera and microphone, which is converted into emotional information using the "EmotionRecognitionAPI". The output is the user's current emotional state. At this stage, the device prepares to send the emotional data to the server.

[0823] Step 2:

[0824] Users enter property search criteria via a terminal. This input includes keywords, conditions, and selections from lists. Based on this, the terminal formats the user's request and sends it to the server. This process organizes search data based on the user's preferences.

[0825] Step 3:

[0826] The server integrates the emotional information and search criteria received from the terminal and begins processing. The input consists of emotional data and search data, and a generative AI model is used to create a user preference model based on these. Through data calculations, the most suitable property information for the user is extracted.

[0827] Step 4:

[0828] The server narrows down the property database based on the user's preferences and sends the extracted property list to the terminal. The terminal then presents the user with customized property information. The output at this stage is the display of the property list to the user.

[0829] Step 5:

[0830] Users provide feedback on the presented properties. This feedback is provided as input, based on their selections. This information is then sent back to the server via the terminal. The user's feedback verifies the consistency between their emotions and actual preferences.

[0831] Step 6:

[0832] The server updates its preference model using collected feedback information. Feedback data serves as input, and data processing refines the preference model. As a result, new market trend information is generated and used to inform future property recommendations.

[0833] 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.

[0834] 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.

[0835] 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.

[0836] 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.

[0837] 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.

[0838] 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.

[0839] 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.

[0840] 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.

[0841] 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."

[0842] 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.

[0843] 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.

[0844] 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.

[0845] 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.

[0846] 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.

[0847] 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.

[0848] 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.

[0849] 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.

[0850] 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.

[0851] 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.

[0852] 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.

[0853] 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.

[0854] The following is further disclosed regarding the embodiments described above.

[0855] (Claim 1)

[0856] A means for receiving user input information and predicting user preferences based on that information,

[0857] A means for extracting the most suitable property information from a property information database based on the aforementioned preferences,

[0858] A means for presenting the extracted property information to the user and collecting feedback information from the user,

[0859] A means for analyzing the aforementioned feedback information and updating the user preference model,

[0860] A means of predicting market trends based on the updated preference model and providing information to real estate-related businesses,

[0861] A system that includes this.

[0862] (Claim 2)

[0863] The system according to claim 1, further comprising means for providing a user with an experience of a property using virtual reality or augmented reality.

[0864] (Claim 3)

[0865] The system according to claim 1, further comprising means for recording the user's search criteria and browsing history in real time and transferring them to a server.

[0866] "Example 1"

[0867] (Claim 1)

[0868] A device that receives user input data and infers preferences based on that data,

[0869] A device for extracting optimal data from an information database based on the aforementioned preferences,

[0870] A device that displays the extracted information to the user and collects feedback from the user,

[0871] A device that analyzes the aforementioned feedback and updates the user preference model,

[0872] A device that predicts market trends based on the updated preference model and supplies information to relevant businesses,

[0873] A device that sends the search criteria entered by the user from the terminal to the server,

[0874] A system that includes the above.

[0875] (Claim 2)

[0876] The system according to claim 1, further comprising a device that provides a user with a data experience using virtual reality or augmented reality.

[0877] (Claim 3)

[0878] The system according to claim 1, further comprising a device for recording the user's search criteria and browsing history in real time and transferring them to a server.

[0879] "Application Example 1"

[0880] (Claim 1)

[0881] A means for receiving user input information and predicting user preferences based on that information,

[0882] A means for extracting the most suitable property information from a property information database based on the aforementioned preferences,

[0883] A means for presenting the extracted property information to the user and collecting feedback information from the user,

[0884] A means for analyzing the aforementioned feedback information and updating the user preference model,

[0885] A means of predicting market trends based on the updated preference model and providing information to real estate-related businesses,

[0886] A means of visually presenting real estate in a virtual environment,

[0887] A means of allowing users to virtually experience real estate properties that match their criteria,

[0888] A means of intuitively operating and experiencing real estate properties via smart devices,

[0889] A system that includes this.

[0890] (Claim 2)

[0891] The system according to claim 1, further comprising means for providing a user with a property experience using virtual reality or augmented reality, wherein the user can virtually visit the property using a smart device in a physical store.

[0892] (Claim 3)

[0893] The system according to claim 1, further comprising means for recording the user's search criteria and browsing history in real time and transferring them to a server, and utilizing user feedback obtained through the experience in the virtual environment.

[0894] "Example 2 of combining an emotion engine"

[0895] (Claim 1)

[0896] A means of receiving user input information and sentiment data, and predicting user preferences based on these,

[0897] A means for extracting optimal property information from a property information database based on the aforementioned preference and emotional data,

[0898] A means for prioritizing and presenting the extracted property information to the user, and for collecting feedback information from the user,

[0899] A means for analyzing the aforementioned feedback information and emotional data and updating the user preference model,

[0900] A means for predicting market trends based on the updated preference model and sentiment data, and for providing information to relevant businesses,

[0901] A system that includes this.

[0902] (Claim 2)

[0903] The system according to claim 1, further comprising means for providing a user with an experience of a property using virtual reality or augmented reality.

[0904] (Claim 3)

[0905] The system according to claim 1, further comprising means for recording user search criteria, browsing history, and sentiment data in real time and transferring them to a server.

[0906] "Application example 2 when combining with an emotional engine"

[0907] (Claim 1)

[0908] A means for receiving user input information and emotional information, and predicting user preferences based on said information,

[0909] A means for extracting the most suitable property information from a property information database based on the aforementioned preference and emotional information,

[0910] A means of recognizing facial expressions and voice in real time in order to analyze the user's emotional state,

[0911] A means for presenting the extracted property information to the user and collecting user feedback information and sentiment data,

[0912] A means for analyzing the aforementioned feedback information and emotional data and updating the user preference model,

[0913] A means of predicting market trends based on the updated preference model and providing information to real estate-related businesses,

[0914] A system that includes this.

[0915] (Claim 2)

[0916] The system according to claim 1, further comprising means for providing a user with a property experience using virtual reality or augmented reality, and for prioritizing the presentation of relevant property information based on the user's emotional response.

[0917] (Claim 3)

[0918] The system according to claim 1, further comprising means for recording the user's search criteria and browsing history in real time and transferring them to a server along with sentiment data. [Explanation of Symbols]

[0919] 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 for receiving user input information and predicting user preferences based on that information, A means for extracting the most suitable property information from a property information database based on the aforementioned preferences, A means for presenting the extracted property information to the user and collecting feedback information from the user, A means for analyzing the aforementioned feedback information and updating the user preference model, A means of predicting market trends based on the updated preference model and providing information to real estate-related businesses, A system that includes this.

2. The system according to claim 1, further comprising means for providing a user with an experience of a property using virtual reality or augmented reality.

3. The system according to claim 1, further comprising means for recording the user's search criteria and browsing history in real time and transferring them to a server.

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