system

Abstract

The system according to this embodiment aims to automate the review and scheduling of real estate properties. [Solution] The system according to the embodiment comprises a data collection unit, a scoring unit, a reporting unit, and a reservation unit. The data collection unit collects data from real estate brokerage websites. The scoring unit scores properties based on the data collected by the data collection unit. The reporting unit reports the properties scored by the scoring unit in a ranking format. The reservation unit automatically makes viewing reservations for the properties reported by the reporting unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it took a lot of time and labor to consider real estate properties and make interior viewing reservations.

[0005] The system according to the embodiment aims to automate the consideration of real estate properties and the reservation of interior viewings.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, a scoring unit, a reporting unit, and a reservation unit. The data collection unit collects data from real estate brokerage websites. The scoring unit scores properties based on the data collected by the data collection unit. The reporting unit reports the properties scored by the scoring unit in a ranking format. The reservation unit automatically makes viewing reservations for the properties reported by the reporting unit. [Effects of the Invention]

[0007] The system according to this embodiment can automate the review of real estate properties and the scheduling of viewings. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, 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), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] 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 only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a receiving 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 receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice 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 unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (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.

[0022] 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.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The automated real estate consideration system according to an embodiment of the present invention is a system that automates the process of considering purchasing a home using an AI agent, taking into account the current situation of rising real estate values ​​and stagnant average annual income in Tokyo's 23 wards. This automated real estate consideration system first obtains the latest data from real estate brokerage sites and scores properties based on the user's desired conditions. Next, it reports the most suitable properties from the scored properties in a ranking format and provides it to the user. Furthermore, if the user wishes, a viewing reservation can be automatically made with a single click. This mechanism saves busy modern people the trouble of searching for real estate every day and makes it possible to efficiently find their ideal property. For example, the automated real estate consideration system collects data from major real estate brokerage sites and scores properties based on the user's desired conditions. For example, it scores properties considering factors such as location convenience, future asset value, current market price, family structure and floor area, interest rates and down payment. Next, it reports the most suitable properties from the scored properties in a ranking format and provides it to the user. The user can receive the latest report every day and efficiently find their ideal property. For example, if a property that matches the user's desired conditions is found, that property will be displayed at the top of the ranking. Furthermore, if the user wishes, they can automatically schedule a viewing with a single click. The AI ​​agent analyzes web forms and automatically schedules viewings. This allows users to schedule viewings without any hassle. This system makes it easier to access the real estate market in Tokyo's 23 wards and efficiently find ideal properties. For example, taking into account the current situation such as soaring housing prices in Tokyo's 23 wards and net migration into Tokyo in the post-COVID era, the AI ​​agent can suggest the most suitable properties based on the user's desired conditions. In the future, it will also be possible to integrate with other real estate services and expand to areas outside of Tokyo's 23 wards. As a result, the automated real estate search system will enable users to efficiently find their ideal properties.

[0029] The automated real estate review system according to this embodiment comprises a data collection unit, a scoring unit, a reporting unit, and a reservation unit. The data collection unit collects data from real estate brokerage websites. The data collection unit can, for example, collect the latest property information, price information, surrounding environment information, etc., from major real estate brokerage websites. The data collection unit can, for example, automatically collect data from real estate brokerage websites using web scraping technology. The data collection unit can also obtain data from real estate brokerage websites using APIs. For example, the data collection unit can obtain the latest property information using the API of a specific real estate brokerage website. The data collection unit can, for example, store the collected data in a database and use it for subsequent processing. The scoring unit scores properties based on the data collected by the data collection unit. The scoring unit scores properties considering factors such as location convenience, future asset value, current market price, family structure and floor area, interest rates and down payment. The scoring unit can, for example, score properties using AI. For example, the scoring unit evaluates properties using a machine learning algorithm and calculates a score. The scoring unit can, for example, adjust the scoring criteria based on the user's desired conditions. The reporting unit reports the properties scored by the scoring unit in a ranking format. The reporting unit, for example, ranks the scored properties and provides this information to the user. The reporting unit, for example, displays property information in a ranking format to make it easier for the user to compare properties. The reporting unit, for example, displays properties that match the user's desired conditions at the top of the ranking. The reporting unit can, for example, generate and provide the user with the latest report daily. The reservation unit automatically makes viewing reservations for properties reported by the reporting unit. The reservation unit can, for example, analyze a web form and automatically make viewing reservations. The reservation unit can, for example, make viewing reservations for the date and time desired by the user. The reservation unit can, for example, automatically send confirmation emails for viewing reservations. As a result, the automated real estate review system according to this embodiment allows users to efficiently find their ideal property.

[0030] The data collection unit collects data from real estate brokerage websites. For example, the data collection unit can collect the latest property information, price information, and surrounding environment information from major real estate brokerage websites. Specifically, the data collection unit automatically collects data from real estate brokerage websites using web scraping technology. Web scraping technology is a technique that analyzes the HTML structure of a specific web page and extracts the necessary information. For example, the data collection unit accesses the property details page and obtains information such as the property name, location, price, floor plan, year built, and surrounding facilities. The data collection unit can also obtain data from real estate brokerage websites using APIs. Using APIs allows for more efficient and accurate data acquisition. For example, the data collection unit uses the API of a specific real estate brokerage website to obtain the latest property information. Using APIs increases the frequency of property information updates, allowing for the collection of always up-to-date information. The data collection unit stores the collected data in a database and uses it for subsequent processing. The database stores property information, price information, surrounding environment information, etc., and searches and filtering are performed as needed. This allows the data collection unit to collect a wide range of data from diverse sources and improve the overall accuracy of the system. Furthermore, the data collection unit can adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. For example, by focusing data collection on specific regions or price ranges, it becomes possible to provide information tailored to user needs. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The scoring unit scores properties based on data collected by the data collection unit. The scoring unit considers factors such as location convenience, future asset value, current market price, family size and floor area, interest rates, and down payment when scoring properties. Specifically, the scoring unit can use AI to score properties. The AI ​​uses machine learning algorithms to evaluate properties and calculate scores. For example, the scoring unit learns from past property data and builds a model to predict future asset value. Using this model, it evaluates collected property data and predicts the future asset value of each property. Furthermore, to evaluate location convenience, the scoring unit considers information such as surrounding transportation, commercial facilities, and schools. This allows it to highly rate properties that meet the user's desired conditions. In addition, the scoring unit can adjust the scoring criteria based on the user's desired conditions. For example, if a user inputs conditions related to family size and lifestyle, the scoring unit evaluates properties considering these conditions. This enables property scoring tailored to the user's needs. The scoring unit can quickly and accurately evaluate properties based on collected data, providing users with the most suitable properties. Furthermore, the scoring unit stores the evaluation results in a database, making them available to subsequent reporting and reservation units. This allows the scoring unit to improve the overall accuracy and efficiency of the system.

[0032] The reporting unit reports properties scored by the scoring unit in a ranking format. For example, the reporting unit ranks the scored properties and provides this information to the user. Specifically, the reporting unit displays property information in a ranking format, making it easier for users to compare properties. The ranking is based on the scores calculated by the scoring unit, with properties matching the user's desired conditions displayed at the top. For example, the reporting unit filters scored properties and generates a ranking based on the user's entered desired conditions (price range, floor plan, location, etc.). The reporting unit can generate and provide the latest report to the user daily. This allows users to always obtain the latest property information and efficiently compare properties. Furthermore, the reporting unit not only displays properties matching the user's desired conditions at the top of the ranking, but also provides detailed property information (price, location, floor plan, year built, surrounding facilities, etc.). This allows users to quickly check and compare detailed property information. The reporting unit also provides functions for users to save and share property information. For example, users can add their favorite properties to a favorites list and review them later. Furthermore, property information can be shared via email and social media. This allows the reporting department to help users efficiently compare and consider properties and find the best fit for them.

[0033] The reservation department automatically makes viewing reservations for properties reported by the reporting department. For example, the reservation department can analyze web forms and automatically make viewing reservations. Specifically, the reservation department can make viewing reservations for the date and time desired by the user. For example, when a user enters their desired date and time, the reservation department automatically enters that information into the viewing reservation form on the real estate brokerage site and completes the reservation. The reservation department can also automatically send confirmation emails for viewing reservations. This allows users to receive confirmation of their viewing reservation quickly. Furthermore, the reservation department also has a function to send viewing reservation reminders. For example, it can send a reminder the day before the viewing to allow the user to reconfirm their viewing schedule. This allows users to view properties smoothly without forgetting their appointments. The reservation department also provides a function to manage viewing reservations for multiple properties at once. For example, if a user wishes to view multiple properties, the reservation department automatically makes viewing reservations for each property and adjusts the schedule. This allows users to view multiple properties efficiently. Furthermore, the reservation system also includes a function to collect feedback after viewings. For example, it can send questionnaires to users after viewings to collect their evaluations and impressions of the property. This can be used to improve the entire system. As a result, the reservation system can help users efficiently find their ideal property and significantly reduce the effort involved in making viewing reservations.

[0034] The data collection unit collects data from major real estate brokerage websites. For example, the data collection unit can collect the latest property information, price information, and surrounding environment information from major real estate brokerage websites. For example, the data collection unit can automatically collect data from real estate brokerage websites using web scraping technology. The data collection unit can also obtain data from real estate brokerage websites using APIs. For example, the data collection unit can obtain the latest property information using the API of a specific real estate brokerage website. The data collection unit can store the collected data in a database and use it for subsequent processing. This allows for obtaining the latest information by collecting data from major real estate brokerage websites. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the data collected from major real estate brokerage websites into a generating AI and have the generating AI perform data analysis.

[0035] The scoring unit scores properties by considering factors such as location convenience, future asset value, current market price, family structure and floor area, interest rates and down payments. For example, to evaluate location convenience, the scoring unit considers transportation access and the availability of surrounding facilities. For example, to evaluate future asset value, the scoring unit considers regional development potential and economic indicators. For example, to evaluate current market price, the scoring unit considers past transaction data and the prices of surrounding properties. For example, to evaluate family structure and floor area, the scoring unit considers the number of family members and the number of rooms needed. For example, to evaluate interest rates and down payments, the scoring unit considers loan terms and payment plans. By scoring properties by considering multiple factors, the system can provide users with the most suitable properties. Some or all of the above processing in the scoring unit may be performed using AI, for example, or not. For example, the scoring unit can input data collected by the collection unit into a generating AI and have the generating AI perform property scoring.

[0036] The reporting unit reports scored properties in a ranking format and provides it to the user. For example, the reporting unit ranks the scored properties and provides this ranking to the user. For example, the reporting unit displays property information in a ranking format to make it easier for users to compare properties. For example, the reporting unit displays properties that match the user's desired conditions at the top of the ranking. For example, the reporting unit can generate and provide the latest report to the user every day. This makes it easier for users to compare properties by providing reports in a ranking format. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input property information scored by the scoring unit into a generating AI and have the generating AI execute a ranking-format report.

[0037] The reservation department analyzes web forms and automatically makes viewing reservations. For example, the reservation department analyzes the structure of the web form and automatically enters the necessary information. For example, the reservation department can make viewing reservations for the date and time desired by the user. The reservation department can also automatically send confirmation emails for viewing reservations. This reduces the effort required of the user by automating the viewing reservation process. Some or all of the above processes in the reservation department may be performed using AI, for example, or not using AI. For example, the reservation department can have a generation AI perform the analysis of the web form and have the generation AI perform the automation of viewing reservations.

[0038] The reporting unit displays properties that match the user's desired conditions at the top of the ranking. For example, the reporting unit filters properties based on the user's desired conditions and displays them at the top of the ranking. For example, the reporting unit prioritizes displaying properties that match the user's desired conditions, such as price range, floor plan, and location. This makes it easier for the user to find their ideal property by displaying properties that match the user's desired conditions at the top. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input the user's desired conditions into a generating AI and have the generating AI perform filtering and ranking of properties that match the conditions.

[0039] The data collection unit analyzes the user's past search history during data collection and selects the optimal collection method. For example, the data collection unit prioritizes collecting property types that the user has frequently searched for in the past. For example, the data collection unit collects data narrowed down to a specific area from the user's past search history. For example, the data collection unit collects relevant new property information based on the conditions the user has previously searched for. This allows the optimal collection method to be selected by analyzing the user's past search history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past search history data into a generating AI and have the generating AI select the optimal collection method.

[0040] The data collection unit filters the data based on the user's current living situation and areas of interest during collection. For example, if the user changes their family structure, the data collection unit filters property information based on that change. For example, if the user changes jobs to a new workplace, the data collection unit prioritizes collecting property information close to that workplace. For example, if the user has a particular hobby or interest, the data collection unit collects property information in areas related to that hobby or interest. By filtering based on the user's current living situation and areas of interest, the data collection unit can provide information that is highly relevant to the user. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's living situation and areas of interest into a generating AI and have the generating AI perform the filtering.

[0041] The data collection unit prioritizes collecting highly relevant data, taking into account the user's geographical location information during data collection. For example, the data collection unit prioritizes collecting property information close to the user's current location. For example, if the user is interested in a particular area, the data collection unit prioritizes collecting property information in that area. For example, if the user is on the move, the data collection unit collects property information in the area the user is moving to. By collecting data while considering the user's geographical location information, the data collection unit can provide the user with highly relevant information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0042] The data collection unit analyzes the user's social media activity and collects relevant data during data collection. For example, the data collection unit collects property information in areas the user has shown interest in on social media. For example, the data collection unit collects information on real estate-related accounts the user follows on social media. For example, the data collection unit collects new relevant property information based on property information the user has shared on social media. In this way, relevant data can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0043] The scoring unit improves the accuracy of scoring by considering the interrelationships between properties during the scoring process. For example, the scoring unit scores by considering the price trends of neighboring properties. For example, the scoring unit scores by considering the availability of facilities around the property. For example, the scoring unit scores by predicting the future asset value of the property. By considering the interrelationships between properties, the accuracy of scoring is improved. Some or all of the above processing in the scoring unit may be performed using AI, for example, or without AI. For example, the scoring unit can input interrelationship data between properties into a generating AI and have the generating AI perform the task of improving the accuracy of scoring.

[0044] The scoring unit considers the attribute information of the property submitter when scoring. For example, the scoring unit assigns a high score if the submitter is a trustworthy real estate agent. For example, the scoring unit assigns a low score if the submitter has caused problems in the past. For example, the scoring unit considers the submitter's past transaction history when scoring. This allows for highly reliable scoring by considering the attribute information of the property submitter. Some or all of the above processing in the scoring unit may be performed using AI, for example, or without AI. For example, the scoring unit can input the submitter's attribute information into a generating AI and have the generating AI perform the scoring.

[0045] The scoring unit considers the geographical distribution of properties when scoring. For example, the scoring unit sets higher scores for areas where properties are concentrated. For example, the scoring unit sets lower scores for areas where properties are dispersed. For example, the scoring unit considers the geographical convenience of properties when scoring. This improves the accuracy of the scoring by considering the geographical distribution of properties. Some or all of the above processing in the scoring unit may be performed using AI, for example, or without AI. For example, the scoring unit can input geographical distribution data of properties into a generating AI and have the generating AI perform the scoring.

[0046] The scoring unit improves the accuracy of its scoring by referring to relevant literature on the property during the scoring process. For example, the scoring unit scores by referring to academic papers on the property. For example, the scoring unit scores by referring to market reports on the property. For example, the scoring unit scores by referring to user reviews on the property. By referring to relevant literature on the property during the scoring process, the accuracy of the scoring is improved. Some or all of the above processing in the scoring unit may be performed using AI, for example, or without AI. For example, the scoring unit can input relevant literature data on the property into a generating AI and have the generating AI perform the task of improving the accuracy of the scoring.

[0047] The reporting unit optimizes the current report by referring to past report data when generating a report. For example, the reporting unit prioritizes displaying information on properties that the user was interested in from past report data. For example, the reporting unit excludes information on properties that the user skipped from past report data. For example, the reporting unit analyzes past report data and generates a report tailored to the user's preferences. This allows the current report to be optimized by referring to past report data. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input past report data into a generation AI and have the generation AI perform the optimization of the current report.

[0048] The reporting unit applies different report formats depending on the property category when generating reports. For example, for residential properties, the reporting unit provides detailed information on floor plans and surrounding environment. For commercial properties, the reporting unit provides detailed information on profitability and location conditions. For investment properties, the reporting unit provides detailed information on future asset value and risks. By applying different report formats for each property category, the reporting unit can provide users with the most relevant information. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input property category data into a generating AI and have the generating AI apply the appropriate report format for each category.

[0049] The reporting unit determines the priority of reports based on the property submission date when generating reports. For example, the reporting unit prioritizes displaying newly submitted properties. For example, the reporting unit excludes properties that have been submitted for a certain period of time. For example, the reporting unit generates reports considering the freshness of properties based on the submission date. This ensures that the latest information is provided preferentially by prioritizing reports based on the property submission date. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input property submission date data into a generating AI and have the generating AI perform the report priority determination.

[0050] The reporting unit analyzes the report by referring to relevant market data for the property when generating the report. For example, the reporting unit evaluates the reasonableness of the price by referring to market data surrounding the property. For example, the reporting unit predicts the future asset value by analyzing market trends for the property. For example, the reporting unit performs comparative analysis by referring to information on competing properties. In this way, by analyzing the report by referring to relevant market data for the property, useful information can be provided to the user. Some or all of the above processing in the reporting unit may be performed using AI, for example, or not using AI. For example, the reporting unit can input relevant market data for the property into a generating AI and have the generating AI perform the analysis of the report.

[0051] The reservation department analyzes the user's past reservation history to select the optimal reservation method at the time of reservation. For example, the reservation department may prioritize suggesting reservation methods the user has used in the past. For example, the reservation department may suggest the optimal reservation time slot based on the user's past reservation history. For example, the reservation department may analyze the user's past reservation history and suggest the optimal reservation method. In this way, the optimal reservation method can be provided by analyzing the user's past reservation history. Some or all of the above processes in the reservation department may be performed using AI, for example, or not using AI. For example, the reservation department may input the user's past reservation history data into a generating AI and have the generating AI perform the selection of the optimal reservation method.

[0052] The reservation department customizes the reservation process based on the user's current lifestyle. For example, if the user is busy, the reservation department provides a way to complete the reservation in the shortest possible time. If the user is relaxed, the reservation department provides detailed reservation options. If the user can only make a reservation during a specific time slot, the reservation department provides a reservation method tailored to that time slot. By customizing the reservation process based on the user's current lifestyle, the system can provide the user with the most suitable reservation method. Some or all of the above processing in the reservation department may be performed using AI, for example, or not. For example, the reservation department can input user lifestyle data into a generating AI and have the generating AI perform the customization of the reservation method.

[0053] The reservation unit selects the optimal reservation method when a reservation is made, taking into account the user's geographical location. For example, the reservation unit prioritizes scheduling viewings of properties close to the user's current location. For example, if the user is interested in a particular area, the reservation unit prioritizes scheduling viewings of properties in that area. For example, if the user is on the move, the reservation unit schedules viewings of properties in the area the user is currently traveling to. By selecting a reservation method that takes the user's geographical location into account, the system can provide the user with the most suitable reservation method. Some or all of the above processing in the reservation unit may be performed using AI, for example, or without AI. For example, the reservation unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal reservation method.

[0054] The reservation department analyzes the user's social media activity when a reservation is made and suggests a reservation method. For example, the reservation department may suggest a viewing appointment for a property the user has shown interest in on social media. For example, the reservation department may suggest a reservation based on information from real estate-related accounts the user follows on social media. For example, the reservation department may suggest a viewing appointment for a related property based on property information the user has shared on social media. In this way, by analyzing the user's social media activity, it is possible to provide the user with a reservation method that is highly relevant to them. Some or all of the above processing in the reservation department may be performed using AI, for example, or not using AI. For example, the reservation department may input the user's social media activity data into a generating AI and have the generating AI execute the suggestion of a reservation method.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The data collection unit can analyze the user's past search history during data collection and select the optimal collection method. For example, it can prioritize collecting property types that the user has frequently searched for in the past. It can also collect data narrowed down to specific areas based on the user's past search history. Furthermore, it can collect relevant new property information based on the conditions the user has previously searched for. In this way, the optimal collection method can be selected by analyzing the user's past search history.

[0057] The data collection unit can filter data based on the user's current living situation and areas of interest during the collection process. For example, if a user changes their family structure, property information can be filtered accordingly. Similarly, if a user changes jobs, property information near their new workplace can be prioritized. Furthermore, if a user has specific hobbies or interests, property information in areas related to those hobbies or interests can be collected. This allows the system to provide users with highly relevant information by filtering data based on their current living situation and areas of interest.

[0058] The scoring unit can improve the accuracy of scoring by considering the interrelationships between properties during the scoring process. For example, it can score properties by considering the price trends of neighboring properties. It can also score properties by considering the availability of surrounding facilities. Furthermore, it can score properties by predicting their future asset value. In this way, the accuracy of scoring is improved by considering the interrelationships between properties.

[0059] The reporting function can optimize the current report by referencing past report data during report generation. For example, it can prioritize displaying information on properties that the user was interested in based on past report data. It can also exclude information on properties that the user skipped based on past report data. Furthermore, it can analyze past report data to generate reports tailored to the user's preferences. In this way, the current report can be optimized by referencing past report data.

[0060] The reservation system can analyze a user's past reservation history to select the most suitable reservation method. For example, it can prioritize suggesting reservation methods the user has used in the past. It can also suggest the most suitable reservation time slot based on the user's past reservation history. Furthermore, it can analyze the user's past reservation history to suggest the most suitable reservation method. In this way, by analyzing the user's past reservation history, the system can provide the most suitable reservation method.

[0061] The reservation system can select the most suitable reservation method by considering the user's geographical location during the reservation process. For example, it can prioritize reservations for viewing properties close to the user's current location. Furthermore, if the user is interested in a specific area, it can prioritize reservations for viewing properties in that area. Additionally, if the user is on the move, it can reserve viewings for properties in their destination area. By selecting the reservation method based on the user's geographical location, the system can provide the user with the most optimal reservation method.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The data collection unit collects data from real estate brokerage websites. For example, it can collect the latest property information, price information, and surrounding environment information from major real estate brokerage websites. The data collection unit automatically collects data using web scraping technology and APIs and stores it in a database. Step 2: The scoring unit scores properties based on the data collected by the data collection unit. For example, it scores properties considering factors such as location convenience, future asset value, current market price, family size and floor area, interest rates and down payment. The scoring unit can also use AI and machine learning algorithms to evaluate properties and adjust the scoring criteria based on the user's desired conditions. Step 3: The reporting unit reports the properties scored by the scoring unit in a ranking format. For example, it ranks the scored properties and provides this information to the user. The reporting unit displays property information in a ranking format, making it easier for users to compare properties and displaying properties that match their desired conditions at the top of the ranking. Step 4: The reservation department automatically makes viewing reservations for properties reported by the reporting department. For example, it can analyze a web form, make a viewing reservation for the date and time requested by the user, and automatically send a confirmation email for the viewing reservation.

[0064] (Example of form 2) The automated real estate consideration system according to an embodiment of the present invention is a system that automates the process of considering purchasing a home using an AI agent, taking into account the current situation of rising real estate values ​​and stagnant average annual income in Tokyo's 23 wards. This automated real estate consideration system first obtains the latest data from real estate brokerage sites and scores properties based on the user's desired conditions. Next, it reports the most suitable properties from the scored properties in a ranking format and provides it to the user. Furthermore, if the user wishes, a viewing reservation can be automatically made with a single click. This mechanism saves busy modern people the trouble of searching for real estate every day and makes it possible to efficiently find their ideal property. For example, the automated real estate consideration system collects data from major real estate brokerage sites and scores properties based on the user's desired conditions. For example, it scores properties considering factors such as location convenience, future asset value, current market price, family structure and floor area, interest rates and down payment. Next, it reports the most suitable properties from the scored properties in a ranking format and provides it to the user. The user can receive the latest report every day and efficiently find their ideal property. For example, if a property that matches the user's desired conditions is found, that property will be displayed at the top of the ranking. Furthermore, if the user wishes, they can automatically schedule a viewing with a single click. The AI ​​agent analyzes web forms and automatically schedules viewings. This allows users to schedule viewings without any hassle. This system makes it easier to access the real estate market in Tokyo's 23 wards and efficiently find ideal properties. For example, taking into account the current situation such as soaring housing prices in Tokyo's 23 wards and net migration into Tokyo in the post-COVID era, the AI ​​agent can suggest the most suitable properties based on the user's desired conditions. In the future, it will also be possible to integrate with other real estate services and expand to areas outside of Tokyo's 23 wards. As a result, the automated real estate search system will enable users to efficiently find their ideal properties.

[0065] The automated real estate review system according to this embodiment comprises a data collection unit, a scoring unit, a reporting unit, and a reservation unit. The data collection unit collects data from real estate brokerage websites. The data collection unit can, for example, collect the latest property information, price information, surrounding environment information, etc., from major real estate brokerage websites. The data collection unit can, for example, automatically collect data from real estate brokerage websites using web scraping technology. The data collection unit can also obtain data from real estate brokerage websites using APIs. For example, the data collection unit can obtain the latest property information using the API of a specific real estate brokerage website. The data collection unit can, for example, store the collected data in a database and use it for subsequent processing. The scoring unit scores properties based on the data collected by the data collection unit. The scoring unit scores properties considering factors such as location convenience, future asset value, current market price, family structure and floor area, interest rates and down payment. The scoring unit can, for example, score properties using AI. For example, the scoring unit evaluates properties using a machine learning algorithm and calculates a score. The scoring unit can, for example, adjust the scoring criteria based on the user's desired conditions. The reporting unit reports the properties scored by the scoring unit in a ranking format. The reporting unit, for example, ranks the scored properties and provides this information to the user. The reporting unit, for example, displays property information in a ranking format to make it easier for the user to compare properties. The reporting unit, for example, displays properties that match the user's desired conditions at the top of the ranking. The reporting unit can, for example, generate and provide the user with the latest report daily. The reservation unit automatically makes viewing reservations for properties reported by the reporting unit. The reservation unit can, for example, analyze a web form and automatically make viewing reservations. The reservation unit can, for example, make viewing reservations for the date and time desired by the user. The reservation unit can, for example, automatically send confirmation emails for viewing reservations. As a result, the automated real estate review system according to this embodiment allows users to efficiently find their ideal property.

[0066] The data collection unit collects data from real estate brokerage websites. For example, the data collection unit can collect the latest property information, price information, and surrounding environment information from major real estate brokerage websites. Specifically, the data collection unit automatically collects data from real estate brokerage websites using web scraping technology. Web scraping technology is a technique that analyzes the HTML structure of a specific web page and extracts the necessary information. For example, the data collection unit accesses the property details page and obtains information such as the property name, location, price, floor plan, year built, and surrounding facilities. The data collection unit can also obtain data from real estate brokerage websites using APIs. Using APIs allows for more efficient and accurate data acquisition. For example, the data collection unit uses the API of a specific real estate brokerage website to obtain the latest property information. Using APIs increases the frequency of property information updates, allowing for the collection of always up-to-date information. The data collection unit stores the collected data in a database and uses it for subsequent processing. The database stores property information, price information, surrounding environment information, etc., and searches and filtering are performed as needed. This allows the data collection unit to collect a wide range of data from diverse sources and improve the overall accuracy of the system. Furthermore, the data collection unit can adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. For example, by focusing data collection on specific regions or price ranges, it becomes possible to provide information tailored to user needs. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0067] The scoring unit scores properties based on data collected by the data collection unit. The scoring unit considers factors such as location convenience, future asset value, current market price, family size and floor area, interest rates, and down payment when scoring properties. Specifically, the scoring unit can use AI to score properties. The AI ​​uses machine learning algorithms to evaluate properties and calculate scores. For example, the scoring unit learns from past property data and builds a model to predict future asset value. Using this model, it evaluates collected property data and predicts the future asset value of each property. Furthermore, to evaluate location convenience, the scoring unit considers information such as surrounding transportation, commercial facilities, and schools. This allows it to highly rate properties that meet the user's desired conditions. In addition, the scoring unit can adjust the scoring criteria based on the user's desired conditions. For example, if a user inputs conditions related to family size and lifestyle, the scoring unit evaluates properties considering these conditions. This enables property scoring tailored to the user's needs. The scoring unit can quickly and accurately evaluate properties based on collected data, providing users with the most suitable properties. Furthermore, the scoring unit stores the evaluation results in a database, making them available to subsequent reporting and reservation units. This allows the scoring unit to improve the overall accuracy and efficiency of the system.

[0068] The reporting unit reports properties scored by the scoring unit in a ranking format. For example, the reporting unit ranks the scored properties and provides this information to the user. Specifically, the reporting unit displays property information in a ranking format, making it easier for users to compare properties. The ranking is based on the scores calculated by the scoring unit, with properties matching the user's desired conditions displayed at the top. For example, the reporting unit filters scored properties and generates a ranking based on the user's entered desired conditions (price range, floor plan, location, etc.). The reporting unit can generate and provide the latest report to the user daily. This allows users to always obtain the latest property information and efficiently compare properties. Furthermore, the reporting unit not only displays properties matching the user's desired conditions at the top of the ranking, but also provides detailed property information (price, location, floor plan, year built, surrounding facilities, etc.). This allows users to quickly check and compare detailed property information. The reporting unit also provides functions for users to save and share property information. For example, users can add their favorite properties to a favorites list and review them later. Furthermore, property information can be shared via email and social media. This allows the reporting department to help users efficiently compare and consider properties and find the best fit for them.

[0069] The reservation department automatically makes viewing reservations for properties reported by the reporting department. For example, the reservation department can analyze web forms and automatically make viewing reservations. Specifically, the reservation department can make viewing reservations for the date and time desired by the user. For example, when a user enters their desired date and time, the reservation department automatically enters that information into the viewing reservation form on the real estate brokerage site and completes the reservation. The reservation department can also automatically send confirmation emails for viewing reservations. This allows users to receive confirmation of their viewing reservation quickly. Furthermore, the reservation department also has a function to send viewing reservation reminders. For example, it can send a reminder the day before the viewing to allow the user to reconfirm their viewing schedule. This allows users to view properties smoothly without forgetting their appointments. The reservation department also provides a function to manage viewing reservations for multiple properties at once. For example, if a user wishes to view multiple properties, the reservation department automatically makes viewing reservations for each property and adjusts the schedule. This allows users to view multiple properties efficiently. Furthermore, the reservation system also includes a function to collect feedback after viewings. For example, it can send questionnaires to users after viewings to collect their evaluations and impressions of the property. This can be used to improve the entire system. As a result, the reservation system can help users efficiently find their ideal property and significantly reduce the effort involved in making viewing reservations.

[0070] The data collection unit collects data from major real estate brokerage websites. For example, the data collection unit can collect the latest property information, price information, and surrounding environment information from major real estate brokerage websites. For example, the data collection unit can automatically collect data from real estate brokerage websites using web scraping technology. The data collection unit can also obtain data from real estate brokerage websites using APIs. For example, the data collection unit can obtain the latest property information using the API of a specific real estate brokerage website. The data collection unit can store the collected data in a database and use it for subsequent processing. This allows for obtaining the latest information by collecting data from major real estate brokerage websites. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the data collected from major real estate brokerage websites into a generating AI and have the generating AI perform data analysis.

[0071] The scoring unit scores properties by considering factors such as location convenience, future asset value, current market price, family structure and floor area, interest rates and down payments. For example, to evaluate location convenience, the scoring unit considers transportation access and the availability of surrounding facilities. For example, to evaluate future asset value, the scoring unit considers regional development potential and economic indicators. For example, to evaluate current market price, the scoring unit considers past transaction data and the prices of surrounding properties. For example, to evaluate family structure and floor area, the scoring unit considers the number of family members and the number of rooms needed. For example, to evaluate interest rates and down payments, the scoring unit considers loan terms and payment plans. By scoring properties by considering multiple factors, the system can provide users with the most suitable properties. Some or all of the above processing in the scoring unit may be performed using AI, for example, or not. For example, the scoring unit can input data collected by the collection unit into a generating AI and have the generating AI perform property scoring.

[0072] The reporting unit reports scored properties in a ranking format and provides it to the user. For example, the reporting unit ranks the scored properties and provides this ranking to the user. For example, the reporting unit displays property information in a ranking format to make it easier for users to compare properties. For example, the reporting unit displays properties that match the user's desired conditions at the top of the ranking. For example, the reporting unit can generate and provide the latest report to the user every day. This makes it easier for users to compare properties by providing reports in a ranking format. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input property information scored by the scoring unit into a generating AI and have the generating AI execute a ranking-format report.

[0073] The reservation department analyzes web forms and automatically makes viewing reservations. For example, the reservation department analyzes the structure of the web form and automatically enters the necessary information. For example, the reservation department can make viewing reservations for the date and time desired by the user. The reservation department can also automatically send confirmation emails for viewing reservations. This reduces the effort required of the user by automating the viewing reservation process. Some or all of the above processes in the reservation department may be performed using AI, for example, or not using AI. For example, the reservation department can have a generation AI perform the analysis of the web form and have the generation AI perform the automation of viewing reservations.

[0074] The reporting unit displays properties that match the user's desired conditions at the top of the ranking. For example, the reporting unit filters properties based on the user's desired conditions and displays them at the top of the ranking. For example, the reporting unit prioritizes displaying properties that match the user's desired conditions, such as price range, floor plan, and location. This makes it easier for the user to find their ideal property by displaying properties that match the user's desired conditions at the top. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input the user's desired conditions into a generating AI and have the generating AI perform filtering and ranking of properties that match the conditions.

[0075] The data collection unit estimates the user's emotions and adjusts the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit reduces the frequency of data collection and collects data during times when the user is relaxed. For example, if the user is relaxed, the data collection unit increases the frequency of data collection to provide up-to-date information. For example, if the user is in a hurry, the data collection unit collects data immediately and provides information quickly. In this way, by adjusting the timing of data collection according to the user's emotions, information can be provided at the optimal time for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and adjustment of data collection timing.

[0076] The data collection unit analyzes the user's past search history during data collection and selects the optimal collection method. For example, the data collection unit prioritizes collecting property types that the user has frequently searched for in the past. For example, the data collection unit collects data narrowed down to a specific area from the user's past search history. For example, the data collection unit collects relevant new property information based on the conditions the user has previously searched for. This allows the optimal collection method to be selected by analyzing the user's past search history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past search history data into a generating AI and have the generating AI select the optimal collection method.

[0077] The data collection unit filters the data based on the user's current living situation and areas of interest during collection. For example, if the user changes their family structure, the data collection unit filters property information based on that change. For example, if the user changes jobs to a new workplace, the data collection unit prioritizes collecting property information close to that workplace. For example, if the user has a particular hobby or interest, the data collection unit collects property information in areas related to that hobby or interest. By filtering based on the user's current living situation and areas of interest, the data collection unit can provide information that is highly relevant to the user. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's living situation and areas of interest into a generating AI and have the generating AI perform the filtering.

[0078] The data collection unit estimates the user's emotions and determines the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit prioritizes collecting high-priority property information. For example, if the user is relaxed, the data collection unit collects a wide range of property information. For example, if the user is in a hurry, the data collection unit prioritizes collecting immediately available property information. This allows the system to prioritize providing users with information that is important to them by prioritizing the data to be collected according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and data collection priority determination.

[0079] The data collection unit prioritizes collecting highly relevant data, taking into account the user's geographical location information during data collection. For example, the data collection unit prioritizes collecting property information close to the user's current location. For example, if the user is interested in a particular area, the data collection unit prioritizes collecting property information in that area. For example, if the user is on the move, the data collection unit collects property information in the area the user is moving to. By collecting data while considering the user's geographical location information, the data collection unit can provide the user with highly relevant information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0080] The data collection unit analyzes the user's social media activity and collects relevant data during data collection. For example, the data collection unit collects property information in areas the user has shown interest in on social media. For example, the data collection unit collects information on real estate-related accounts the user follows on social media. For example, the data collection unit collects new relevant property information based on property information the user has shared on social media. In this way, relevant data can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0081] The scoring unit estimates the user's emotions and adjusts the scoring criteria based on the estimated emotions. For example, if the user is stressed, the scoring unit will focus on high-importance factors. If the user is relaxed, the scoring unit will consider a wide range of factors. If the user is in a hurry, the scoring unit will focus on factors that allow for quick decision-making. By adjusting the scoring criteria according to the user's emotions, the system can provide the optimal score for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the scoring unit may be performed using AI or not. For example, the scoring unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and adjustment of scoring criteria.

[0082] The scoring unit improves the accuracy of scoring by considering the interrelationships between properties during the scoring process. For example, the scoring unit scores by considering the price trends of neighboring properties. For example, the scoring unit scores by considering the availability of facilities around the property. For example, the scoring unit scores by predicting the future asset value of the property. By considering the interrelationships between properties, the accuracy of scoring is improved. Some or all of the above processing in the scoring unit may be performed using AI, for example, or without AI. For example, the scoring unit can input interrelationship data between properties into a generating AI and have the generating AI perform the task of improving the accuracy of scoring.

[0083] The scoring unit considers the attribute information of the property submitter when scoring. For example, the scoring unit assigns a high score if the submitter is a trustworthy real estate agent. For example, the scoring unit assigns a low score if the submitter has caused problems in the past. For example, the scoring unit considers the submitter's past transaction history when scoring. This allows for highly reliable scoring by considering the attribute information of the property submitter. Some or all of the above processing in the scoring unit may be performed using AI, for example, or without AI. For example, the scoring unit can input the submitter's attribute information into a generating AI and have the generating AI perform the scoring.

[0084] The scoring unit estimates the user's emotions and adjusts the order in which the scoring results are displayed based on the estimated emotions. For example, if the user is stressed, the scoring unit will display properties of high importance at the top. For example, if the user is relaxed, the scoring unit will display a wide range of properties in a balanced manner. For example, if the user is in a hurry, the scoring unit will display properties that are immediately available at the top. In this way, by adjusting the order in which the scoring results are displayed according to the user's emotions, the system can provide the user with the most relevant information. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the scoring unit may be performed using AI or not using AI. For example, the scoring unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and adjustment of the display order of the scoring results.

[0085] The scoring unit considers the geographical distribution of properties when scoring. For example, the scoring unit sets higher scores for areas where properties are concentrated. For example, the scoring unit sets lower scores for areas where properties are dispersed. For example, the scoring unit considers the geographical convenience of properties when scoring. This improves the accuracy of the scoring by considering the geographical distribution of properties. Some or all of the above processing in the scoring unit may be performed using AI, for example, or without AI. For example, the scoring unit can input geographical distribution data of properties into a generating AI and have the generating AI perform the scoring.

[0086] The scoring unit improves the accuracy of its scoring by referring to relevant literature on the property during the scoring process. For example, the scoring unit scores by referring to academic papers on the property. For example, the scoring unit scores by referring to market reports on the property. For example, the scoring unit scores by referring to user reviews on the property. By referring to relevant literature on the property during the scoring process, the accuracy of the scoring is improved. Some or all of the above processing in the scoring unit may be performed using AI, for example, or without AI. For example, the scoring unit can input relevant literature data on the property into a generating AI and have the generating AI perform the task of improving the accuracy of the scoring.

[0087] The reporting unit estimates the user's emotions and adjusts how the report is displayed based on the estimated emotions. For example, if the user is stressed, the reporting unit provides a simple and easy-to-read report. If the user is relaxed, the reporting unit provides a report with detailed information. If the user is in a hurry, the reporting unit provides a concise report. By adjusting how the report is displayed according to the user's emotions, the system can provide the user with the most relevant information. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the reporting unit may be performed using AI or not. For example, the reporting unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and adjust the report display method.

[0088] The reporting unit optimizes the current report by referring to past report data when generating a report. For example, the reporting unit prioritizes displaying information on properties that the user was interested in from past report data. For example, the reporting unit excludes information on properties that the user skipped from past report data. For example, the reporting unit analyzes past report data and generates a report tailored to the user's preferences. This allows the current report to be optimized by referring to past report data. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input past report data into a generation AI and have the generation AI perform the optimization of the current report.

[0089] The reporting unit applies different report formats depending on the property category when generating reports. For example, for residential properties, the reporting unit provides detailed information on floor plans and surrounding environment. For commercial properties, the reporting unit provides detailed information on profitability and location conditions. For investment properties, the reporting unit provides detailed information on future asset value and risks. By applying different report formats for each property category, the reporting unit can provide users with the most relevant information. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input property category data into a generating AI and have the generating AI apply the appropriate report format for each category.

[0090] The reporting unit estimates the user's emotions and adjusts the importance of the report based on the estimated emotions. For example, if the user is stressed, the reporting unit will highlight and display high-importance properties. For example, if the user is relaxed, the reporting unit will display a wide range of properties in a balanced manner. For example, if the user is in a hurry, the reporting unit will highlight and display properties that are immediately available. In this way, by adjusting the importance of the report according to the user's emotions, information that is important to the user can be highlighted and provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reporting unit may be performed using AI or not using AI. For example, the reporting unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and report importance adjustment.

[0091] The reporting unit determines the priority of reports based on the property submission date when generating reports. For example, the reporting unit prioritizes displaying newly submitted properties. For example, the reporting unit excludes properties that have been submitted for a certain period of time. For example, the reporting unit generates reports considering the freshness of properties based on the submission date. This ensures that the latest information is provided preferentially by prioritizing reports based on the property submission date. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input property submission date data into a generating AI and have the generating AI perform the report priority determination.

[0092] The reporting unit analyzes the report by referring to relevant market data for the property when generating the report. For example, the reporting unit evaluates the reasonableness of the price by referring to market data surrounding the property. For example, the reporting unit predicts the future asset value by analyzing market trends for the property. For example, the reporting unit performs comparative analysis by referring to information on competing properties. In this way, by analyzing the report by referring to relevant market data for the property, useful information can be provided to the user. Some or all of the above processing in the reporting unit may be performed using AI, for example, or not using AI. For example, the reporting unit can input relevant market data for the property into a generating AI and have the generating AI perform the analysis of the report.

[0093] The reservation unit estimates the user's emotions and adjusts the reservation method based on the estimated emotions. For example, if the user is stressed, the reservation unit provides a simple reservation method. If the user is relaxed, the reservation unit provides detailed reservation options. If the user is in a hurry, the reservation unit provides a way to complete the reservation quickly. In this way, by adjusting the reservation method according to the user's emotions, the system can provide the optimal reservation method for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reservation unit may be performed using AI or not using AI. For example, the reservation unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and adjustment of the reservation method.

[0094] The reservation department analyzes the user's past reservation history to select the optimal reservation method at the time of reservation. For example, the reservation department may prioritize suggesting reservation methods the user has used in the past. For example, the reservation department may suggest the optimal reservation time slot based on the user's past reservation history. For example, the reservation department may analyze the user's past reservation history and suggest the optimal reservation method. In this way, the optimal reservation method can be provided by analyzing the user's past reservation history. Some or all of the above processes in the reservation department may be performed using AI, for example, or not using AI. For example, the reservation department may input the user's past reservation history data into a generating AI and have the generating AI perform the selection of the optimal reservation method.

[0095] The reservation department customizes the reservation process based on the user's current lifestyle. For example, if the user is busy, the reservation department provides a way to complete the reservation in the shortest possible time. If the user is relaxed, the reservation department provides detailed reservation options. If the user can only make a reservation during a specific time slot, the reservation department provides a reservation method tailored to that time slot. By customizing the reservation process based on the user's current lifestyle, the system can provide the user with the most suitable reservation method. Some or all of the above processing in the reservation department may be performed using AI, for example, or not. For example, the reservation department can input user lifestyle data into a generating AI and have the generating AI perform the customization of the reservation method.

[0096] The booking unit estimates the user's emotions and determines booking priorities based on the estimated emotions. For example, if the user is stressed, the booking unit prioritizes high-priority bookings. For example, if the user is relaxed, the booking unit offers a wide range of booking options. For example, if the user is in a hurry, the booking unit provides a means to complete bookings quickly. This allows bookings that are important to the user to be prioritized by determining booking priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the booking unit may be performed using AI or not using AI. For example, the booking unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and booking priority determination.

[0097] The reservation unit selects the optimal reservation method when a reservation is made, taking into account the user's geographical location. For example, the reservation unit prioritizes scheduling viewings of properties close to the user's current location. For example, if the user is interested in a particular area, the reservation unit prioritizes scheduling viewings of properties in that area. For example, if the user is on the move, the reservation unit schedules viewings of properties in the area the user is currently traveling to. By selecting a reservation method that takes the user's geographical location into account, the system can provide the user with the most suitable reservation method. Some or all of the above processing in the reservation unit may be performed using AI, for example, or without AI. For example, the reservation unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal reservation method.

[0098] The reservation department analyzes the user's social media activity when a reservation is made and suggests a reservation method. For example, the reservation department may suggest a viewing appointment for a property the user has shown interest in on social media. For example, the reservation department may suggest a reservation based on information from real estate-related accounts the user follows on social media. For example, the reservation department may suggest a viewing appointment for a related property based on property information the user has shared on social media. In this way, by analyzing the user's social media activity, it is possible to provide the user with a reservation method that is highly relevant to them. Some or all of the above processing in the reservation department may be performed using AI, for example, or not using AI. For example, the reservation department may input the user's social media activity data into a generating AI and have the generating AI execute the suggestion of a reservation method.

[0099] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0100] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on those emotions. For example, if the user is stressed, the frequency of data collection can be reduced, and data can be collected during times when the user is relaxed. Conversely, if the user is relaxed, the frequency of data collection can be increased to provide the latest information. Furthermore, if the user is in a hurry, data can be collected immediately to provide information quickly. In this way, by adjusting the timing of data collection according to the user's emotions, information can be provided at the optimal time for the user.

[0101] The data collection unit can analyze the user's past search history during data collection and select the optimal collection method. For example, it can prioritize collecting property types that the user has frequently searched for in the past. It can also collect data narrowed down to specific areas based on the user's past search history. Furthermore, it can collect relevant new property information based on the conditions the user has previously searched for. In this way, the optimal collection method can be selected by analyzing the user's past search history.

[0102] The scoring unit can estimate the user's emotions and adjust the scoring criteria based on those emotions. For example, if the user is stressed, the scoring can focus on high-importance factors. If the user is relaxed, the scoring can consider a wide range of factors. Furthermore, if the user is in a hurry, the scoring can focus on factors that allow for quick decision-making. By adjusting the scoring criteria according to the user's emotions, the system can provide the optimal score for the user.

[0103] The reporting system can estimate the user's emotions and adjust how the report is displayed based on that estimation. For example, if the user is stressed, it can provide a simple and easy-to-read report. If the user is relaxed, it can provide a report with more detailed information. Furthermore, if the user is in a hurry, it can provide a concise report. By adjusting how the report is displayed according to the user's emotions, the system can provide the user with the most relevant information.

[0104] The reservation system can estimate the user's emotions and adjust the reservation method based on those emotions. For example, if the user is stressed, it can offer a simple reservation method. If the user is relaxed, it can offer more detailed reservation options. Furthermore, if the user is in a hurry, it can provide a way to complete the reservation quickly. In this way, by adjusting the reservation method according to the user's emotions, the system can provide the optimal reservation method for the user.

[0105] The data collection unit can filter data based on the user's current living situation and areas of interest during the collection process. For example, if a user changes their family structure, property information can be filtered accordingly. Similarly, if a user changes jobs, property information near their new workplace can be prioritized. Furthermore, if a user has specific hobbies or interests, property information in areas related to those hobbies or interests can be collected. This allows the system to provide users with highly relevant information by filtering data based on their current living situation and areas of interest.

[0106] The scoring unit can improve the accuracy of scoring by considering the interrelationships between properties during the scoring process. For example, it can score properties by considering the price trends of neighboring properties. It can also score properties by considering the availability of surrounding facilities. Furthermore, it can score properties by predicting their future asset value. In this way, the accuracy of scoring is improved by considering the interrelationships between properties.

[0107] The reporting function can optimize the current report by referencing past report data during report generation. For example, it can prioritize displaying information on properties that the user was interested in based on past report data. It can also exclude information on properties that the user skipped based on past report data. Furthermore, it can analyze past report data to generate reports tailored to the user's preferences. In this way, the current report can be optimized by referencing past report data.

[0108] The reservation system can analyze a user's past reservation history to select the most suitable reservation method. For example, it can prioritize suggesting reservation methods the user has used in the past. It can also suggest the most suitable reservation time slot based on the user's past reservation history. Furthermore, it can analyze the user's past reservation history to suggest the most suitable reservation method. In this way, by analyzing the user's past reservation history, the system can provide the most suitable reservation method.

[0109] The reservation system can select the most suitable reservation method by considering the user's geographical location during the reservation process. For example, it can prioritize reservations for viewing properties close to the user's current location. Furthermore, if the user is interested in a specific area, it can prioritize reservations for viewing properties in that area. Additionally, if the user is on the move, it can reserve viewings for properties in their destination area. By selecting the reservation method based on the user's geographical location, the system can provide the user with the most optimal reservation method.

[0110] The following briefly describes the processing flow for example form 2.

[0111] Step 1: The data collection unit collects data from real estate brokerage websites. For example, it can collect the latest property information, price information, and surrounding environment information from major real estate brokerage websites. The data collection unit automatically collects data using web scraping technology and APIs and stores it in a database. Step 2: The scoring unit scores properties based on the data collected by the data collection unit. For example, it scores properties considering factors such as location convenience, future asset value, current market price, family size and floor area, interest rates and down payment. The scoring unit can also use AI and machine learning algorithms to evaluate properties and adjust the scoring criteria based on the user's desired conditions. Step 3: The reporting unit reports the properties scored by the scoring unit in a ranking format. For example, it ranks the scored properties and provides this information to the user. The reporting unit displays property information in a ranking format, making it easier for users to compare properties and displaying properties that match their desired conditions at the top of the ranking. Step 4: The reservation department automatically makes viewing reservations for properties reported by the reporting department. For example, it can analyze a web form, make a viewing reservation for the date and time requested by the user, and automatically send a confirmation email for the viewing reservation.

[0112] 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.

[0113] Data generation model 58 is a form of 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> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. 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 (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0114] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0115] Each of the multiple elements described above, including the data collection unit, scoring unit, reporting unit, and reservation unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the smart device 14 and collects data from real estate brokerage sites. The scoring unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and scores properties based on the collected data. The reporting unit is implemented by, for example, the control unit 46A of the smart device 14 and reports the scored properties in a ranking format. The reservation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automatically makes viewing reservations. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0116] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0117] 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.

[0118] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0119] 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.

[0120] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0121] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0122] 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.

[0123] 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 by the processor 28. The storage 32 stores the specific processing program 56.

[0124] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0125] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0126] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0127] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0128] 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.

[0129] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0130] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0131] Each of the multiple elements described above, including the data collection unit, scoring unit, reporting unit, and reservation unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the smart glasses 214 and collects data from real estate brokerage sites. The scoring unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and scores properties based on the collected data. The reporting unit is implemented by, for example, the control unit 46A of the smart glasses 214 and reports the scored properties in a ranking format. The reservation unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and automatically makes viewing reservations. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0132] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0133] 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.

[0134] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0135] 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.

[0136] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0137] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0138] 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.

[0139] 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.

[0140] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0141] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0142] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0143] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0144] 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.

[0145] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0146] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0147] Each of the multiple elements described above, including the data collection unit, scoring unit, reporting unit, and reservation unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the headset terminal 314 and collects data from real estate brokerage sites. The scoring unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and scores properties based on the collected data. The reporting unit is implemented by, for example, the control unit 46A of the headset terminal 314 and reports the scored properties in a ranking format. The reservation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automatically makes viewing reservations. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0148] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0149] 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.

[0150] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0151] 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.

[0152] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0153] 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 image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0154] 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.

[0155] 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. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0156] 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.

[0157] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0158] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0159] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0160] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0161] 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.

[0162] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0163] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0164] Each of the multiple elements described above, including the data collection unit, scoring unit, reporting unit, and reservation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the robot 414 and collects data from real estate brokerage sites. The scoring unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and scores properties based on the collected data. The reporting unit is implemented by, for example, the control unit 46A of the robot 414 and reports the scored properties in a ranking format. The reservation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automatically makes viewing reservations. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0165] 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.

[0166] Figure 9 shows the 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.

[0167] 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.

[0168] 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.

[0169] 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, and motorcycles, 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 based, for example, 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.

[0170] 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."

[0171] 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.

[0172] 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 method for the specific process may be used, which includes computer 22 and multiple other computers.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0181] 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 other things 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.

[0182] 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.

[0183] (Note 1) The data collection department collects data from real estate brokerage websites, A scoring unit that scores properties based on the data collected by the aforementioned collection unit, The reporting unit reports the properties scored by the aforementioned scoring unit in a ranking format, The system includes a reservation unit that automatically makes viewing reservations for properties reported by the reporting unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect data from major real estate brokerage websites. The system described in Appendix 1, characterized by the features described herein. (Note 3) The scoring unit is, Properties are scored by considering factors such as location convenience, future asset value, current market price, family size and floor area, interest rates, and down payment. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned report section is, We provide users with a report of scored properties in a ranking format. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reservation section is, Analyze web forms and automatically schedule viewings. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned report section is, The system displays properties that best match the user's desired criteria at the top of the rankings. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is During data collection, the system analyzes the user's past search history to select the most suitable collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is During data collection, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The scoring unit is, The system estimates the user's emotions and adjusts the scoring criteria based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The scoring unit is, When scoring, consider the interrelationships between properties to improve the accuracy of the scoring. The system described in Appendix 1, characterized by the features described herein. (Note 15) The scoring unit is, When scoring, the attribute information of the property submitter is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 16) The scoring unit is, It estimates the user's emotions and adjusts the order in which the scoring results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The scoring unit is, When scoring, the geographical distribution of properties is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 18) The scoring unit is, When scoring, refer to relevant literature on the property to improve the accuracy of the scoring. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned report section is, It estimates user sentiment and adjusts how reports are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned report section is, When generating reports, the system optimizes current reports by referencing past report data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned report section is, When generating reports, apply different report formats for each property category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned report section is, We estimate user sentiment and adjust the importance of the report based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned report section is, When generating reports, prioritize reports based on when the properties were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned report section is, When generating the report, we analyze the report by referring to relevant market data for the property. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned reservation section is, It estimates the user's emotions and adjusts the booking method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned reservation section is, When a reservation is made, the system analyzes the user's past reservation history to select the most suitable reservation method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned reservation section is, When making a reservation, the reservation method will be customized based on the user's current lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned reservation section is, The system estimates the user's emotions and determines reservation priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned reservation section is, When making a reservation, the system will select the most suitable reservation method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned reservation section is, When making a reservation, we analyze the user's social media activity and suggest a reservation method. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0184] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The data collection department collects data from real estate brokerage websites, A scoring unit that scores properties based on the data collected by the aforementioned collection unit, The reporting unit reports the properties scored by the aforementioned scoring unit in a ranking format, The system includes a reservation unit that automatically makes viewing reservations for properties reported by the reporting unit. A system characterized by the following features.

2. The scoring unit is, Properties are scored by considering factors such as location convenience, future asset value, current market price, family size and floor area, interest rates, and down payment. The system according to feature 1.

3. The aforementioned report section is, We provide users with a report of scored properties in a ranking format. The system according to feature 1.

4. The aforementioned reservation section is, Analyze web forms and automatically schedule viewings. The system according to feature 1.

5. The aforementioned report section is, The system displays properties that best match the user's desired criteria at the top of the rankings. The system according to feature 1.

6. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

7. The aforementioned collection unit is During data collection, the system analyzes the user's past search history to select the most suitable collection method. The system according to feature 1.

8. The aforementioned collection unit is During data collection, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

9. The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

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