system

Abstract

The system according to this embodiment aims to evaluate the competitiveness of a property in real time and provide this information to the user. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, and a presentation unit. The data collection unit collects market data. The analysis unit analyzes the data collected by the data collection unit. The presentation unit presents the competitiveness score of the property.

Description

Technical Field

[0006] , , ,

[0005] , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, the competitiveness of properties has not been fully evaluated in real time and provided to users, and there is room for improvement.

[0005] The system according to the embodiment aims to evaluate the competitiveness of properties in real time and provide it to users.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a presentation unit. The collection unit collects market data. The analysis unit analyzes the data collected by the collection unit. The presentation unit presents a competitiveness score of a property.

Effects of the Invention

[0007] The system according to this embodiment can evaluate the competitiveness of a property in real time and provide this information to the user. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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 property competitiveness evaluation system according to an embodiment of the present invention is a system that uses AI to evaluate the competitiveness of properties and displays in real time how popular a property found by the user is. This property competitiveness evaluation system uses an AI agent to periodically collect market data, automatically updates the competitiveness score of properties in real time, and provides the user with the latest information. The property competitiveness evaluation system also provides recommendations and specific negotiation strategies based on the user's interests and notifies the user of competing properties with autonomous alerts. For example, the property competitiveness evaluation system collects data such as the price of a property, its location, the surrounding environment, and past transaction history. For example, if the price of a property is rising, it is evaluated as having high competitiveness. Next, the property competitiveness evaluation system uses AI to calculate the competitiveness score of a property based on the collected data. The AI ​​analyzes the collected data and evaluates the popularity and competitiveness of the property. For example, if the price of a property is higher than that of surrounding properties, it is evaluated as having high competitiveness. Furthermore, the property competitiveness evaluation system displays the competitiveness score of properties found by the user in real time. The user can check the competitiveness score of a property and develop a property selection and negotiation strategy. For example, for highly competitive properties, the system provides reminders to encourage quick action and advice on factors advantageous in negotiations (such as market price comparison data and seller background information). The property competitiveness evaluation system also provides recommendations and specific negotiation strategies based on user interests. The AI ​​analyzes the user's past search history and interests to recommend the most suitable properties. For example, it recommends similar properties based on the characteristics of properties the user has previously searched for. Furthermore, the property competitiveness evaluation system provides autonomous alerts for competitive properties. The AI ​​notifies users when highly competitive properties appear or when the competitiveness of existing properties suddenly increases. For example, if a new highly competitive property enters the market, it notifies the user to encourage quick action. This mechanism allows the property competitiveness evaluation system to always base property selection and negotiations on the latest competitiveness score, increasing the success rate of property acquisition. In this way, the property competitiveness evaluation system can support users in property selection and negotiation strategies.

[0029] The property competitiveness evaluation system according to the embodiment comprises a data collection unit, an analysis unit, and a presentation unit. The data collection unit collects market data. Market data includes, but is not limited to, real estate market data and economic indicator data. The data collection unit collects, for example, real estate market data, and collects data such as property price, location, surrounding environment, and past transaction history. The data collection unit can also collect economic indicator data and provide data for evaluating the competitiveness of a property. For example, the data collection unit collects property price data and evaluates that a property is highly competitive if its price is rising. The analysis unit analyzes the data collected by the data collection unit. The analysis unit analyzes the collected data using, for example, statistical analysis or machine learning algorithms. The analysis unit evaluates factors such as property price, location, and surrounding environment using, for example, statistical analysis. The analysis unit can also evaluate the popularity and competitiveness of a property using machine learning algorithms. For example, the analysis unit uses machine learning algorithms to evaluate that a property is highly competitive if its price is higher than that of surrounding properties. The presentation unit presents the competitiveness score of the property. The presentation unit, for example, presents the competitiveness score of a property in real time. The presentation unit presents the competitiveness score of a property to the user, enabling the user to select a property and develop a negotiation strategy. For example, the presentation unit checks the competitiveness score of a property and provides a reminder function to encourage quick action on highly competitive properties, as well as advice on factors advantageous in negotiations (such as market price comparison data and seller background information). In this way, the property competitiveness evaluation system according to the embodiment can support the user in selecting a property and developing a negotiation strategy. Some or all of the above processing in the collection unit, analysis unit and presentation unit may be performed using AI, for example, or not using AI. For example, the collection unit collects property price data, the analysis unit analyzes the collected data, and the presentation unit presents the competitiveness score of the property.

[0030] The data collection unit collects market data. Market data includes, but is not limited to, real estate market data and economic indicator data. For example, the data collection unit collects real estate market data, such as property prices, location, surrounding environment, and past transaction history. Specifically, real estate market data includes property sales history, rental history, property valuation, nearby transaction examples, property age, and renovation history. The data collection unit can also collect economic indicator data to provide data for evaluating the competitiveness of properties. Economic indicator data includes regional economic growth rate, unemployment rate, inflation rate, interest rate trends, and consumer confidence index. For example, the data collection unit collects property price data and evaluates a property as highly competitive if its price is rising. The data collection unit collects this data in real time and transmits it to a central database. Data collection uses methods such as web scraping and APIs to automatically acquire data from various data sources. Furthermore, the data collection unit performs data integrity checks and detects outliers to ensure data quality. This allows the data collection unit to efficiently collect high-quality data from diverse data sources, thereby improving the overall accuracy of the system.

[0031] The analysis unit analyzes the data collected by the collection unit. The analysis unit uses statistical analysis and machine learning algorithms to analyze the collected data. Specifically, it uses statistical analysis to evaluate factors such as property price, location, and surrounding environment. For example, it uses regression analysis to identify factors influencing property prices and build price prediction models. The analysis unit can also use machine learning algorithms to evaluate the popularity and competitiveness of properties. For example, it uses clustering techniques to group properties with similar characteristics and evaluate the competitiveness of each group. Furthermore, it can use deep learning to evaluate the exterior and interior of properties from image data, quantifying the property's attractiveness. Based on these analysis results, the analysis unit calculates a competitiveness score for each property. The competitiveness score is a comprehensive evaluation of factors such as property price, location, surrounding environment, past transaction history, and economic indicators, and serves as an important indicator for users when selecting properties and developing negotiation strategies. The analysis unit continuously updates the competitiveness score based on real-time updated data, enabling it to respond to the latest market trends. This allows the analysis unit to quickly and accurately analyze collected data and provide users with reliable information.

[0032] The presentation unit displays the competitiveness score of a property. For example, the presentation unit displays the competitiveness score of a property in real time. Specifically, it visually displays the competitiveness score of a property through a user interface. For example, it displays the competitiveness score of a property in a dashboard format using graphs and charts, allowing users to grasp the competitiveness of a property at a glance. It also presents the competitiveness score of a property to the user, enabling them to select properties and develop negotiation strategies. For example, the presentation unit checks the competitiveness score of a property and provides a reminder function to encourage quick action on highly competitive properties, as well as advice on factors that are advantageous in negotiations (such as market price comparison data and seller background information). Furthermore, the presentation unit can collect user feedback and continuously improve the accuracy and effectiveness of its presentations. For example, it collects feedback on the results of actions taken by users based on the presented competitiveness score and works with the analysis unit to improve the accuracy of the analysis model. In addition, the presentation unit supports multiple devices and platforms, allowing users to check the competitiveness score of a property anytime, anywhere. This enables the presentation unit to provide users with quick and accurate information and support them in selecting properties and developing negotiation strategies.

[0033] The recommendation department provides recommendations based on user interests. For example, it analyzes the user's past search history and interests to recommend the most suitable properties. For example, it recommends similar properties based on the characteristics of properties the user has previously searched for. The recommendation department can also support property selection and negotiation strategies based on user interests. For example, it advises on property selection and negotiation strategies based on user interests. In this way, it can support the user's property selection by recommending the most suitable properties based on user interests. Some or all of the above processes in the recommendation department may be performed using, for example, generative AI, or without generative AI. For example, the recommendation department inputs the user's past search history into a generative AI, and the generative AI recommends the most suitable properties to the user.

[0034] The notification unit notifies users of competitive properties. For example, the notification unit notifies users when highly competitive properties appear or when the competitiveness of existing properties suddenly increases. For example, the notification unit notifies users when a new highly competitive property comes onto the market, prompting them to take swift action. The notification unit can also notify users when the competitiveness score of a property they have found suddenly increases. For example, the notification unit notifies users when the competitiveness score of a property they have found suddenly increases, prompting them to take swift action. This allows users to take swift action by notifying them of competitive properties. Some or all of the above processing in the notification unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the generative AI may notify users when a highly competitive property appears or when the competitiveness of an existing property suddenly increases.

[0035] The data collection unit can collect data such as property price, location, surrounding environment, and past transaction history. For example, the unit can collect property price data and evaluate a property as highly competitive if its price is rising. The unit can also collect property location data and evaluate factors such as transportation access and surrounding facilities. For example, the unit can collect property location data and evaluate properties with good transportation access as highly competitive. The unit can also collect property surrounding environment data and evaluate factors such as safety and noise levels. For example, the unit can collect property surrounding environment data and evaluate properties with good safety as highly competitive. The unit can also collect property past transaction history data and evaluate factors such as past sales prices and number of transactions. For example, the unit can collect property past transaction history data and evaluate properties with high past sales prices as highly competitive. In this way, by collecting data such as property price, location, surrounding environment, and past transaction history, the system can provide data to evaluate the competitiveness of a property. Some or all of the processing described above in the data collection unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the data collection unit inputs property price data into a generating AI, which then provides data for evaluating the competitiveness of the property.

[0036] The analysis unit can analyze the collected data and evaluate the popularity and competitiveness of properties. For example, the analysis unit can analyze the collected data using statistical analysis or machine learning algorithms. For example, the analysis unit can use statistical analysis to evaluate factors such as the property's price, location, and surrounding environment. The analysis unit can also use machine learning algorithms to evaluate the popularity and competitiveness of properties. For example, the analysis unit can use machine learning algorithms to evaluate a property as highly competitive if its price is higher than that of surrounding properties. The analysis unit can also evaluate the popularity of properties based on the collected data. For example, the analysis unit can analyze data such as the number of views and inquiries for properties to evaluate their popularity. In this way, by analyzing the collected data and evaluating the popularity and competitiveness of properties, useful information can be provided to users. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit inputs the collected data into a generative AI, and the generative AI evaluates the popularity and competitiveness of properties.

[0037] The presentation unit can display the competitiveness score of a property in real time. For example, the presentation unit can display the competitiveness score of a property to the user in real time, enabling the user to select a property and develop a negotiation strategy. For example, the presentation unit can check the competitiveness score of a property and provide a reminder function to encourage quick action on highly competitive properties, as well as advice on factors advantageous in negotiations (such as market price comparison data and seller background information). By displaying the competitiveness score of a property in real time, the user can quickly select a property and develop a negotiation strategy. Some or all of the above processing in the presentation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the presentation unit inputs the competitiveness score of a property into a generative AI, and the generative AI displays the competitiveness score of the property in real time.

[0038] The data collection unit can analyze past market data fluctuation patterns and determine the optimal data collection frequency. For example, during periods of high historical data fluctuation, the collection unit can increase the collection frequency to obtain more detailed data. Conversely, during periods of low historical data fluctuation, the collection unit can reduce the collection frequency for more efficient data collection. Furthermore, the collection unit can dynamically adjust the collection frequency according to specific events or seasons. This enables efficient data collection by analyzing past market data fluctuation patterns. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit inputs historical market data into a generative AI, which then determines the optimal data collection frequency.

[0039] The data collection unit can dynamically change the types of data it collects based on the user's interests and past search history. For example, the data collection unit prioritizes collecting relevant data based on the characteristics of properties the user has previously searched for. Furthermore, if the user's interests change, the data collection unit can also change the types of data collected based on those new interests. In addition, the data collection unit can analyze the user's search history and collect the most relevant data. This allows for the collection of more relevant data by changing the types of data collected based on the user's interests and past search history. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without one. For example, the data collection unit inputs the user's search history into a generative AI, which then dynamically changes the types of data it collects.

[0040] The data collection unit can prioritize the collection of highly relevant market data by considering the user's geographical location information during the collection process. For example, the data collection unit can prioritize the collection of market data for the area where the user is currently located. It can also prioritize the collection of market data for areas of interest to the user. Furthermore, the data collection unit can collect highly relevant market data for areas based on the user's past travel history. This allows for the priority collection of highly relevant market data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using, for example, a generating AI, or without a generating AI. For example, the data collection unit inputs the user's geographical location information into a generating AI, which then prioritizes the collection of highly relevant market data.

[0041] The data collection unit can analyze the user's social media activity and collect relevant market data during the collection process. For example, the data collection unit can collect market data related to properties that the user has shown interest in on social media. The data collection unit can also collect market data on areas of interest based on the user's social media activity. Furthermore, the data collection unit can collect relevant market data by referring to the activities of the user's social media followers and friends. In this way, relevant market 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, for example, a generative AI, or without a generative AI. For example, the data collection unit inputs the user's social media activity into a generative AI, and the generative AI collects relevant market data.

[0042] The analysis unit can improve the accuracy of its analysis by referring to past data analysis results during the analysis process. For example, the analysis unit can improve the accuracy of the current data analysis based on past analysis results. The analysis unit can also refer to past data analysis results to grasp trends and patterns and perform analysis accordingly. Furthermore, the analysis unit can use past analysis results as feedback to improve its analysis algorithm. As a result, the accuracy of the analysis is improved by referring to past data analysis results. Some or all of the above processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit inputs past data analysis results into a generative AI, and the generative AI improves the accuracy of the analysis.

[0043] The analysis unit can apply different analysis methods to each property category during analysis. For example, the analysis unit can apply analysis methods that emphasize price and location to residential properties. It can also apply analysis methods that emphasize profitability and accessibility to commercial properties. Furthermore, it can apply analysis methods that emphasize return rate and risk to investment properties. By applying different analysis methods to each property category, it is possible to provide more accurate analysis results. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without a generating AI. For example, the analysis unit inputs property category data into a generating AI, and the generating AI applies different analysis methods to each category.

[0044] The analysis unit can perform analysis while considering the geographical distribution of properties. For example, the analysis unit can evaluate the competitiveness of each region based on the geographical distribution of properties. The analysis unit can also analyze the price and demand of properties while considering the geographical distribution. Furthermore, the analysis unit can predict the future value of properties based on the geographical distribution. In this way, by considering the geographical distribution of properties, the competitiveness of each region can be evaluated. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the analysis unit inputs the geographical distribution data of properties into a generative AI, and the generative AI performs the analysis while considering the geographical distribution.

[0045] The analysis unit can improve the accuracy of its analysis by referring to relevant literature on the property during the analysis process. For example, the analysis unit can perform its analysis by referring to academic papers and market reports related to the property. It can also perform its analysis by referring to the property's past transaction history and valuation reports. Furthermore, the analysis unit can perform its analysis by referring to news articles and expert opinions related to the property. In this way, the accuracy of the analysis is improved by referring to relevant literature on the property. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without a generating AI. For example, the analysis unit inputs the property's relevant literature data into a generating AI, and the generating AI improves the accuracy of the analysis by referring to the relevant literature.

[0046] The presentation unit can adjust the level of detail of the score based on the importance of the property at the time of presentation. For example, the presentation unit can provide detailed score information for properties of high importance. It can also provide concise score information for properties of low importance. Furthermore, the presentation unit can dynamically adjust how the score is displayed according to the importance of the property. This allows for the provision of more important information by adjusting the level of detail of the score based on the importance of the property. Some or all of the above processing in the presentation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the presentation unit inputs property importance data into a generative AI, and the generative AI adjusts the level of detail of the score.

[0047] The display unit can apply different display algorithms depending on the property category at the time of display. For example, the display unit can apply a display algorithm that emphasizes price and location to residential properties. It can also apply a display algorithm that emphasizes profitability and accessibility to commercial properties. Furthermore, it can apply a display algorithm that emphasizes return rate and risk to investment properties. By applying different display algorithms depending on the property category, more appropriate display becomes possible. Some or all of the above processing in the display unit may be performed using, for example, a generation AI, or without a generation AI. For example, the display unit inputs property category data into a generation AI, and the generation AI applies different display algorithms depending on the category.

[0048] The presentation unit can determine the priority of scores based on the property submission date at the time of presentation. For example, the presentation unit may prioritize the display of scores for recently submitted properties. It can also postpone the display of scores for older properties. Furthermore, the presentation unit can dynamically adjust the display order of scores according to the submission date. This allows for the provision of more important information by prioritizing scores based on the property submission date. Some or all of the above processing in the presentation unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the presentation unit inputs property submission date data into a generating AI, and the generating AI determines the priority of scores.

[0049] The presentation unit can adjust the order of scores based on the relevance of the properties when presenting them. For example, the presentation unit may prioritize displaying the scores of highly relevant properties based on the user's search history. It can also postpone the display of less relevant properties based on the user's interests. Furthermore, the presentation unit can dynamically adjust the display order of scores according to the relevance of the properties. This allows for the provision of more relevant information by adjusting the order of scores based on the relevance of the properties. Some or all of the above processing in the presentation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the presentation unit inputs property relevance data into a generative AI, and the generative AI adjusts the order of the scores.

[0050] The recommendation unit can suggest the most suitable properties by referring to the user's past search history during the recommendation process. For example, the recommendation unit can recommend similar properties based on the characteristics of properties the user has previously searched for. Furthermore, the recommendation unit can prioritize suggesting properties in specific regions or price ranges based on the user's past search history. In addition, the recommendation unit can analyze the user's search history and recommend the most relevant properties. This allows for the suggestion of more relevant properties by referring to the user's past search history. Some or all of the above processing in the recommendation unit may be performed using, for example, a generative AI, or without one. For example, the recommendation unit inputs the user's search history data into a generative AI, which then suggests the most suitable properties.

[0051] The recommendation unit can customize its recommendation methods based on the user's current living situation. For example, if the user lives with family, the recommendation unit will prioritize suggesting properties suitable for families. Similarly, if the user lives alone, the recommendation unit can prioritize suggesting properties suitable for single individuals. Furthermore, the recommendation unit can recommend the most suitable property according to the user's living situation. This allows for the suggestion of more appropriate properties by customizing the recommendation methods based on the user's current living situation. Some or all of the above processing in the recommendation unit may be performed using, for example, a generative AI, or without one. For example, the recommendation unit inputs the user's living situation data into a generative AI, which then customizes the recommendation methods.

[0052] The recommendation unit can suggest the most suitable properties by considering the user's geographical location information during the recommendation process. For example, the recommendation unit can prioritize recommending properties in the area where the user is currently located. It can also prioritize recommending properties in areas that the user is interested in. Furthermore, the recommendation unit can recommend properties in highly relevant areas by referring to the user's past travel history. This allows for the suggestion of more relevant properties by considering the user's geographical location information. Some or all of the above processing in the recommendation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the recommendation unit inputs the user's geographical location information into a generative AI, which then suggests the most suitable properties.

[0053] The recommendation unit can analyze the user's social media activity and propose recommendation methods when making recommendations. For example, the recommendation unit can recommend properties related to properties the user has shown interest in on social media. It can also recommend properties in areas the user is interested in, based on their social media activity. Furthermore, the recommendation unit can recommend related properties by referring to the activities of the user's followers and friends on social media. In this way, by analyzing the user's social media activity, it can propose properties that are more relevant. Some or all of the above processing in the recommendation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the recommendation unit inputs the user's social media activity data into a generative AI, and the generative AI proposes recommendation methods.

[0054] The notification unit can select the optimal notification method by referring to the user's past response history when sending a notification. For example, the notification unit may prioritize using notification methods that the user has preferred to respond to in the past. The notification unit can also select the most effective notification method from the user's past response history. Furthermore, the notification unit can analyze the user's response history and determine the optimal notification timing. This allows for the selection of a more effective notification method by referring to the user's past response history. Some or all of the above processing in the notification unit may be performed using, for example, a generative AI, or without a generative AI. For example, the notification unit inputs the user's response history data into a generative AI, and the generative AI selects the optimal notification method.

[0055] The notification unit can select the optimal notification method by considering the user's device information when sending a notification. For example, if the user is using a smartphone, the notification unit will prioritize push notifications. It can also prioritize email notifications if the user is using a tablet. Furthermore, if the user is using a smartwatch, the notification unit can prioritize vibration notifications. This allows for the selection of a more effective notification method by considering the user's device information. Some or all of the above processing in the notification unit may be performed using, for example, a generative AI, or without one. For example, the notification unit inputs the user's device information into the generative AI, which then selects the optimal notification method.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The property competitiveness evaluation system analyzes a user's past browsing history and can predict and recommend properties that the user might be interested in. For example, it can prioritize recommending similar properties based on the characteristics of properties the user has previously searched for. It can also prioritize recommending properties in specific areas or price ranges if the user has shown interest in those areas or price ranges. Furthermore, by analyzing the user's browsing history, it can recommend new properties that the user might be interested in. In this way, it can support users in selecting properties by recommending the most suitable properties based on their interests.

[0058] The property competitiveness evaluation system can prioritize the collection of highly relevant market data by considering the user's geographical location. For example, it can prioritize the collection of market data for the area the user is currently in. It can also prioritize the collection of market data for areas the user is interested in. Furthermore, it can collect highly relevant market data for areas based on the user's past travel history. In this way, by considering the user's geographical location, it can prioritize the collection of highly relevant market data.

[0059] The property competitiveness evaluation system can analyze users' social media activity and collect relevant market data. For example, it can collect market data related to properties that users have shown interest in on social media. It can also collect market data for areas of interest based on users' social media activity. Furthermore, it can collect relevant market data by referring to the activity of users' followers and friends on social media. In this way, relevant market data can be collected by analyzing users' social media activity.

[0060] The property competitiveness evaluation system can customize its recommendation methods based on the user's current living situation. For example, if the user lives with family, it can prioritize recommending family-friendly properties. Similarly, if the user lives alone, it can prioritize recommending properties suitable for single individuals. Furthermore, it can recommend the most suitable property based on the user's specific living situation. This allows for the suggestion of more appropriate properties by customizing the recommendation methods based on the user's current living circumstances.

[0061] The property competitiveness evaluation system can select the optimal notification method by referring to the user's past response history. For example, it can prioritize using notification methods that the user has preferred to respond to in the past. It can also select the most effective notification method based on the user's past response history. Furthermore, it can analyze the user's response history to determine the optimal notification timing. This allows for the selection of more effective notification methods by referring to the user's past response history.

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

[0063] Step 1: The data collection unit collects market data. This market data includes real estate market data, economic indicator data, etc. For example, the data collection unit collects real estate market data, such as property prices, location, surrounding environment, and past transaction history. The data collection unit can also collect economic indicator data to provide data for evaluating the competitiveness of properties. Step 2: The analysis unit analyzes the data collected by the data collection unit. The analysis unit analyzes the collected data using statistical analysis and machine learning algorithms. For example, the analysis unit uses statistical analysis to evaluate factors such as property price, location, and surrounding environment. The analysis unit can also use machine learning algorithms to evaluate the popularity and competitiveness of properties. Step 3: The presentation section displays the property's competitiveness score. The presentation section displays the property's competitiveness score in real time, enabling users to select properties and develop negotiation strategies. For example, the presentation section checks the property's competitiveness score and provides a reminder function to encourage quick action on highly competitive properties, as well as advice on factors that are advantageous in negotiations (such as market price comparison data and seller background information).

[0064] (Example of form 2) The property competitiveness evaluation system according to an embodiment of the present invention is a system that uses AI to evaluate the competitiveness of properties and displays in real time how popular a property found by the user is. This property competitiveness evaluation system uses an AI agent to periodically collect market data, automatically updates the competitiveness score of properties in real time, and provides the user with the latest information. The property competitiveness evaluation system also provides recommendations and specific negotiation strategies based on the user's interests and notifies the user of competing properties with autonomous alerts. For example, the property competitiveness evaluation system collects data such as the price of a property, its location, the surrounding environment, and past transaction history. For example, if the price of a property is rising, it is evaluated as having high competitiveness. Next, the property competitiveness evaluation system uses AI to calculate the competitiveness score of a property based on the collected data. The AI ​​analyzes the collected data and evaluates the popularity and competitiveness of the property. For example, if the price of a property is higher than that of surrounding properties, it is evaluated as having high competitiveness. Furthermore, the property competitiveness evaluation system displays the competitiveness score of properties found by the user in real time. The user can check the competitiveness score of a property and develop a property selection and negotiation strategy. For example, for highly competitive properties, the system provides reminders to encourage quick action and advice on factors advantageous in negotiations (such as market price comparison data and seller background information). The property competitiveness evaluation system also provides recommendations and specific negotiation strategies based on user interests. The AI ​​analyzes the user's past search history and interests to recommend the most suitable properties. For example, it recommends similar properties based on the characteristics of properties the user has previously searched for. Furthermore, the property competitiveness evaluation system provides autonomous alerts for competitive properties. The AI ​​notifies users when highly competitive properties appear or when the competitiveness of existing properties suddenly increases. For example, if a new highly competitive property enters the market, it notifies the user to encourage quick action. This mechanism allows the property competitiveness evaluation system to always base property selection and negotiations on the latest competitiveness score, increasing the success rate of property acquisition. In this way, the property competitiveness evaluation system can support users in property selection and negotiation strategies.

[0065] The property competitiveness evaluation system according to the embodiment comprises a data collection unit, an analysis unit, and a presentation unit. The data collection unit collects market data. Market data includes, but is not limited to, real estate market data and economic indicator data. The data collection unit collects, for example, real estate market data, and collects data such as property price, location, surrounding environment, and past transaction history. The data collection unit can also collect economic indicator data and provide data for evaluating the competitiveness of a property. For example, the data collection unit collects property price data and evaluates that a property is highly competitive if its price is rising. The analysis unit analyzes the data collected by the data collection unit. The analysis unit analyzes the collected data using, for example, statistical analysis or machine learning algorithms. The analysis unit evaluates factors such as property price, location, and surrounding environment using, for example, statistical analysis. The analysis unit can also evaluate the popularity and competitiveness of a property using machine learning algorithms. For example, the analysis unit uses machine learning algorithms to evaluate that a property is highly competitive if its price is higher than that of surrounding properties. The presentation unit presents the competitiveness score of the property. The presentation unit, for example, presents the competitiveness score of a property in real time. The presentation unit presents the competitiveness score of a property to the user, enabling the user to select a property and develop a negotiation strategy. For example, the presentation unit checks the competitiveness score of a property and provides a reminder function to encourage quick action on highly competitive properties, as well as advice on factors advantageous in negotiations (such as market price comparison data and seller background information). In this way, the property competitiveness evaluation system according to the embodiment can support the user in selecting a property and developing a negotiation strategy. Some or all of the above processing in the collection unit, analysis unit and presentation unit may be performed using AI, for example, or not using AI. For example, the collection unit collects property price data, the analysis unit analyzes the collected data, and the presentation unit presents the competitiveness score of the property.

[0066] The data collection unit collects market data. Market data includes, but is not limited to, real estate market data and economic indicator data. For example, the data collection unit collects real estate market data, such as property prices, location, surrounding environment, and past transaction history. Specifically, real estate market data includes property sales history, rental history, property valuation, nearby transaction examples, property age, and renovation history. The data collection unit can also collect economic indicator data to provide data for evaluating the competitiveness of properties. Economic indicator data includes regional economic growth rate, unemployment rate, inflation rate, interest rate trends, and consumer confidence index. For example, the data collection unit collects property price data and evaluates a property as highly competitive if its price is rising. The data collection unit collects this data in real time and transmits it to a central database. Data collection uses methods such as web scraping and APIs to automatically acquire data from various data sources. Furthermore, the data collection unit performs data integrity checks and detects outliers to ensure data quality. This allows the data collection unit to efficiently collect high-quality data from diverse data sources, thereby improving the overall accuracy of the system.

[0067] The analysis unit analyzes the data collected by the collection unit. The analysis unit uses statistical analysis and machine learning algorithms to analyze the collected data. Specifically, it uses statistical analysis to evaluate factors such as property price, location, and surrounding environment. For example, it uses regression analysis to identify factors influencing property prices and build price prediction models. The analysis unit can also use machine learning algorithms to evaluate the popularity and competitiveness of properties. For example, it uses clustering techniques to group properties with similar characteristics and evaluate the competitiveness of each group. Furthermore, it can use deep learning to evaluate the exterior and interior of properties from image data, quantifying the property's attractiveness. Based on these analysis results, the analysis unit calculates a competitiveness score for each property. The competitiveness score is a comprehensive evaluation of factors such as property price, location, surrounding environment, past transaction history, and economic indicators, and serves as an important indicator for users when selecting properties and developing negotiation strategies. The analysis unit continuously updates the competitiveness score based on real-time updated data, enabling it to respond to the latest market trends. This allows the analysis unit to quickly and accurately analyze collected data and provide users with reliable information.

[0068] The presentation unit displays the competitiveness score of a property. For example, the presentation unit displays the competitiveness score of a property in real time. Specifically, it visually displays the competitiveness score of a property through a user interface. For example, it displays the competitiveness score of a property in a dashboard format using graphs and charts, allowing users to grasp the competitiveness of a property at a glance. It also presents the competitiveness score of a property to the user, enabling them to select properties and develop negotiation strategies. For example, the presentation unit checks the competitiveness score of a property and provides a reminder function to encourage quick action on highly competitive properties, as well as advice on factors that are advantageous in negotiations (such as market price comparison data and seller background information). Furthermore, the presentation unit can collect user feedback and continuously improve the accuracy and effectiveness of its presentations. For example, it collects feedback on the results of actions taken by users based on the presented competitiveness score and works with the analysis unit to improve the accuracy of the analysis model. In addition, the presentation unit supports multiple devices and platforms, allowing users to check the competitiveness score of a property anytime, anywhere. This enables the presentation unit to provide users with quick and accurate information and support them in selecting properties and developing negotiation strategies.

[0069] The recommendation department provides recommendations based on user interests. For example, it analyzes the user's past search history and interests to recommend the most suitable properties. For example, it recommends similar properties based on the characteristics of properties the user has previously searched for. The recommendation department can also support property selection and negotiation strategies based on user interests. For example, it advises on property selection and negotiation strategies based on user interests. In this way, it can support the user's property selection by recommending the most suitable properties based on user interests. Some or all of the above processes in the recommendation department may be performed using, for example, generative AI, or without generative AI. For example, the recommendation department inputs the user's past search history into a generative AI, and the generative AI recommends the most suitable properties to the user.

[0070] The notification unit notifies users of competitive properties. For example, the notification unit notifies users when highly competitive properties appear or when the competitiveness of existing properties suddenly increases. For example, the notification unit notifies users when a new highly competitive property comes onto the market, prompting them to take swift action. The notification unit can also notify users when the competitiveness score of a property they have found suddenly increases. For example, the notification unit notifies users when the competitiveness score of a property they have found suddenly increases, prompting them to take swift action. This allows users to take swift action by notifying them of competitive properties. Some or all of the above processing in the notification unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the generative AI may notify users when a highly competitive property appears or when the competitiveness of an existing property suddenly increases.

[0071] The data collection unit can collect data such as property price, location, surrounding environment, and past transaction history. For example, the unit can collect property price data and evaluate a property as highly competitive if its price is rising. The unit can also collect property location data and evaluate factors such as transportation access and surrounding facilities. For example, the unit can collect property location data and evaluate properties with good transportation access as highly competitive. The unit can also collect property surrounding environment data and evaluate factors such as safety and noise levels. For example, the unit can collect property surrounding environment data and evaluate properties with good safety as highly competitive. The unit can also collect property past transaction history data and evaluate factors such as past sales prices and number of transactions. For example, the unit can collect property past transaction history data and evaluate properties with high past sales prices as highly competitive. In this way, by collecting data such as property price, location, surrounding environment, and past transaction history, the system can provide data to evaluate the competitiveness of a property. Some or all of the processing described above in the data collection unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the data collection unit inputs property price data into a generating AI, which then provides data for evaluating the competitiveness of the property.

[0072] The analysis unit can analyze the collected data and evaluate the popularity and competitiveness of properties. For example, the analysis unit can analyze the collected data using statistical analysis or machine learning algorithms. For example, the analysis unit can use statistical analysis to evaluate factors such as the property's price, location, and surrounding environment. The analysis unit can also use machine learning algorithms to evaluate the popularity and competitiveness of properties. For example, the analysis unit can use machine learning algorithms to evaluate a property as highly competitive if its price is higher than that of surrounding properties. The analysis unit can also evaluate the popularity of properties based on the collected data. For example, the analysis unit can analyze data such as the number of views and inquiries for properties to evaluate their popularity. In this way, by analyzing the collected data and evaluating the popularity and competitiveness of properties, useful information can be provided to users. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit inputs the collected data into a generative AI, and the generative AI evaluates the popularity and competitiveness of properties.

[0073] The presentation unit can display the competitiveness score of a property in real time. For example, the presentation unit can display the competitiveness score of a property to the user in real time, enabling the user to select a property and develop a negotiation strategy. For example, the presentation unit can check the competitiveness score of a property and provide a reminder function to encourage quick action on highly competitive properties, as well as advice on factors advantageous in negotiations (such as market price comparison data and seller background information). By displaying the competitiveness score of a property in real time, the user can quickly select a property and develop a negotiation strategy. Some or all of the above processing in the presentation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the presentation unit inputs the competitiveness score of a property into a generative AI, and the generative AI displays the competitiveness score of the property in real time.

[0074] The data collection unit can estimate the user's emotions and adjust the timing of market data collection based on the estimated emotions. For example, if the user is excited, the data collection unit can increase the collection frequency to quickly acquire the latest market data. If the user is relaxed, the data collection unit can maintain the normal collection frequency to ensure stable data collection. Furthermore, if the user is stressed, the data collection unit can reduce the collection frequency to alleviate the user's burden. This allows for data to be collected at a more appropriate time by adjusting the timing of market data collection according to the user's 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 inputs user emotion data into the generative AI, which then adjusts the timing of market data collection.

[0075] The data collection unit can analyze past market data fluctuation patterns and determine the optimal data collection frequency. For example, during periods of high historical data fluctuation, the collection unit can increase the collection frequency to obtain more detailed data. Conversely, during periods of low historical data fluctuation, the collection unit can reduce the collection frequency for more efficient data collection. Furthermore, the collection unit can dynamically adjust the collection frequency according to specific events or seasons. This enables efficient data collection by analyzing past market data fluctuation patterns. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit inputs historical market data into a generative AI, which then determines the optimal data collection frequency.

[0076] The data collection unit can dynamically change the types of data it collects based on the user's interests and past search history. For example, the data collection unit prioritizes collecting relevant data based on the characteristics of properties the user has previously searched for. Furthermore, if the user's interests change, the data collection unit can also change the types of data collected based on those new interests. In addition, the data collection unit can analyze the user's search history and collect the most relevant data. This allows for the collection of more relevant data by changing the types of data collected based on the user's interests and past search history. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without one. For example, the data collection unit inputs the user's search history into a generative AI, which then dynamically changes the types of data it collects.

[0077] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated user emotions. For example, if the user is excited, the data collection unit may prioritize collecting the latest market data. It may also prioritize collecting stable data if the user is relaxed. Furthermore, if the user is stressed, the data collection unit may prioritize collecting only important data. This allows for the collection of more important data by prioritizing data collection according to the user's 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 inputs the user's emotion data into the generative AI, which then determines the priority of data to collect.

[0078] The data collection unit can prioritize the collection of highly relevant market data by considering the user's geographical location information during the collection process. For example, the data collection unit can prioritize the collection of market data for the area where the user is currently located. It can also prioritize the collection of market data for areas of interest to the user. Furthermore, the data collection unit can collect highly relevant market data for areas based on the user's past travel history. This allows for the priority collection of highly relevant market data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using, for example, a generating AI, or without a generating AI. For example, the data collection unit inputs the user's geographical location information into a generating AI, which then prioritizes the collection of highly relevant market data.

[0079] The data collection unit can analyze the user's social media activity and collect relevant market data during the collection process. For example, the data collection unit can collect market data related to properties that the user has shown interest in on social media. The data collection unit can also collect market data on areas of interest based on the user's social media activity. Furthermore, the data collection unit can collect relevant market data by referring to the activities of the user's social media followers and friends. In this way, relevant market 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, for example, a generative AI, or without a generative AI. For example, the data collection unit inputs the user's social media activity into a generative AI, and the generative AI collects relevant market data.

[0080] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis and provide highly accurate results. If the user is in a hurry, the analysis unit can perform a rapid analysis and provide immediate results. Furthermore, if the user is excited, the analysis unit can provide visually easy-to-understand analysis results. This allows for more appropriate analysis results by adjusting the analysis algorithm according to the user's 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-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit inputs user emotion data into the generative AI, which then adjusts the analysis algorithm.

[0081] The analysis unit can improve the accuracy of its analysis by referring to past data analysis results during the analysis process. For example, the analysis unit can improve the accuracy of the current data analysis based on past analysis results. The analysis unit can also refer to past data analysis results to grasp trends and patterns and perform analysis accordingly. Furthermore, the analysis unit can use past analysis results as feedback to improve its analysis algorithm. As a result, the accuracy of the analysis is improved by referring to past data analysis results. Some or all of the above processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit inputs past data analysis results into a generative AI, and the generative AI improves the accuracy of the analysis.

[0082] The analysis unit can apply different analysis methods to each property category during analysis. For example, the analysis unit can apply analysis methods that emphasize price and location to residential properties. It can also apply analysis methods that emphasize profitability and accessibility to commercial properties. Furthermore, it can apply analysis methods that emphasize return rate and risk to investment properties. By applying different analysis methods to each property category, it is possible to provide more accurate analysis results. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without a generating AI. For example, the analysis unit inputs property category data into a generating AI, and the generating AI applies different analysis methods to each category.

[0083] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is tense, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a concise display method. By adjusting the display method of the analysis results according to the user's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit inputs the user's emotion data into the generative AI, and the generative AI adjusts the display method of the analysis results.

[0084] The analysis unit can perform analysis while considering the geographical distribution of properties. For example, the analysis unit can evaluate the competitiveness of each region based on the geographical distribution of properties. The analysis unit can also analyze the price and demand of properties while considering the geographical distribution. Furthermore, the analysis unit can predict the future value of properties based on the geographical distribution. In this way, by considering the geographical distribution of properties, the competitiveness of each region can be evaluated. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the analysis unit inputs the geographical distribution data of properties into a generative AI, and the generative AI performs the analysis while considering the geographical distribution.

[0085] The analysis unit can improve the accuracy of its analysis by referring to relevant literature on the property during the analysis process. For example, the analysis unit can perform its analysis by referring to academic papers and market reports related to the property. It can also perform its analysis by referring to the property's past transaction history and valuation reports. Furthermore, the analysis unit can perform its analysis by referring to news articles and expert opinions related to the property. In this way, the accuracy of the analysis is improved by referring to relevant literature on the property. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without a generating AI. For example, the analysis unit inputs the property's relevant literature data into a generating AI, and the generating AI improves the accuracy of the analysis by referring to the relevant literature.

[0086] The presentation unit can estimate the user's emotions and adjust the display method of the competitive score based on the estimated user emotions. For example, if the user is nervous, the presentation unit can provide a simple and highly visible display method. If the user is relaxed, the presentation unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the presentation unit can provide a concise display method. This allows for a more appropriate display by adjusting the display method of the competitive score according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The 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 presentation unit may be performed using AI or not using AI. For example, the presentation unit inputs user emotion data into the generative AI, and the generative AI adjusts the display method of the competitive score.

[0087] The presentation unit can adjust the level of detail of the score based on the importance of the property at the time of presentation. For example, the presentation unit can provide detailed score information for properties of high importance. It can also provide concise score information for properties of low importance. Furthermore, the presentation unit can dynamically adjust how the score is displayed according to the importance of the property. This allows for the provision of more important information by adjusting the level of detail of the score based on the importance of the property. Some or all of the above processing in the presentation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the presentation unit inputs property importance data into a generative AI, and the generative AI adjusts the level of detail of the score.

[0088] The display unit can apply different display algorithms depending on the property category at the time of display. For example, the display unit can apply a display algorithm that emphasizes price and location to residential properties. It can also apply a display algorithm that emphasizes profitability and accessibility to commercial properties. Furthermore, it can apply a display algorithm that emphasizes return rate and risk to investment properties. By applying different display algorithms depending on the property category, more appropriate display becomes possible. Some or all of the above processing in the display unit may be performed using, for example, a generation AI, or without a generation AI. For example, the display unit inputs property category data into a generation AI, and the generation AI applies different display algorithms depending on the category.

[0089] The display unit can estimate the user's emotions and adjust the display order of scores based on the estimated emotions. For example, if the user is excited, the display unit may prioritize displaying the most recent competitive score. It may also prioritize displaying stable scores if the user is relaxed. Furthermore, if the user is stressed, the display unit may prioritize displaying only important scores. This allows for a more appropriate display by adjusting the score display order according to the user's 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 display unit may be performed using AI or not. For example, the display unit inputs user emotion data into the generative AI, which then adjusts the score display order.

[0090] The presentation unit can determine the priority of scores based on the property submission date at the time of presentation. For example, the presentation unit may prioritize the display of scores for recently submitted properties. It can also postpone the display of scores for older properties. Furthermore, the presentation unit can dynamically adjust the display order of scores according to the submission date. This allows for the provision of more important information by prioritizing scores based on the property submission date. Some or all of the above processing in the presentation unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the presentation unit inputs property submission date data into a generating AI, and the generating AI determines the priority of scores.

[0091] The presentation unit can adjust the order of scores based on the relevance of the properties when presenting them. For example, the presentation unit may prioritize displaying the scores of highly relevant properties based on the user's search history. It can also postpone the display of less relevant properties based on the user's interests. Furthermore, the presentation unit can dynamically adjust the display order of scores according to the relevance of the properties. This allows for the provision of more relevant information by adjusting the order of scores based on the relevance of the properties. Some or all of the above processing in the presentation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the presentation unit inputs property relevance data into a generative AI, and the generative AI adjusts the order of the scores.

[0092] The recommendation unit can estimate the user's emotions and adjust the way recommendations are presented based on the estimated emotions. For example, if the user is relaxed, the recommendation unit can provide detailed recommendations. If the user is in a hurry, the recommendation unit can provide concise recommendations. Furthermore, if the user is excited, the recommendation unit can provide visually easy-to-understand recommendations. By adjusting the way recommendations are presented according to the user's emotions, more appropriate recommendations become possible. 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 recommendation unit may be performed using AI or not using AI. For example, the recommendation unit inputs user emotion data into the generative AI, and the generative AI adjusts the way recommendations are presented.

[0093] The recommendation unit can suggest the most suitable properties by referring to the user's past search history during the recommendation process. For example, the recommendation unit can recommend similar properties based on the characteristics of properties the user has previously searched for. Furthermore, the recommendation unit can prioritize suggesting properties in specific regions or price ranges based on the user's past search history. In addition, the recommendation unit can analyze the user's search history and recommend the most relevant properties. This allows for the suggestion of more relevant properties by referring to the user's past search history. Some or all of the above processing in the recommendation unit may be performed using, for example, a generative AI, or without one. For example, the recommendation unit inputs the user's search history data into a generative AI, which then suggests the most suitable properties.

[0094] The recommendation unit can customize its recommendation methods based on the user's current living situation. For example, if the user lives with family, the recommendation unit will prioritize suggesting properties suitable for families. Similarly, if the user lives alone, the recommendation unit can prioritize suggesting properties suitable for single individuals. Furthermore, the recommendation unit can recommend the most suitable property according to the user's living situation. This allows for the suggestion of more appropriate properties by customizing the recommendation methods based on the user's current living situation. Some or all of the above processing in the recommendation unit may be performed using, for example, a generative AI, or without one. For example, the recommendation unit inputs the user's living situation data into a generative AI, which then customizes the recommendation methods.

[0095] The recommendation unit can estimate the user's emotions and determine the priority of recommendations based on the estimated emotions. For example, if the user is excited, the recommendation unit may prioritize recommending the newest properties. It may also prioritize recommending stable properties if the user is relaxed. Furthermore, if the user is stressed, the recommendation unit may prioritize recommending only important properties. This allows for the suggestion of more appropriate properties by prioritizing recommendations according to the user's 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 recommendation unit may be performed using AI or not. For example, the recommendation unit inputs user emotion data into a generative AI, which then determines the priority of recommendations.

[0096] The recommendation unit can suggest the most suitable properties by considering the user's geographical location information during the recommendation process. For example, the recommendation unit can prioritize recommending properties in the area where the user is currently located. It can also prioritize recommending properties in areas that the user is interested in. Furthermore, the recommendation unit can recommend properties in highly relevant areas by referring to the user's past travel history. This allows for the suggestion of more relevant properties by considering the user's geographical location information. Some or all of the above processing in the recommendation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the recommendation unit inputs the user's geographical location information into a generative AI, which then suggests the most suitable properties.

[0097] The recommendation unit can analyze the user's social media activity and propose recommendation methods when making recommendations. For example, the recommendation unit can recommend properties related to properties the user has shown interest in on social media. It can also recommend properties in areas the user is interested in, based on their social media activity. Furthermore, the recommendation unit can recommend related properties by referring to the activities of the user's followers and friends on social media. In this way, by analyzing the user's social media activity, it can propose properties that are more relevant. Some or all of the above processing in the recommendation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the recommendation unit inputs the user's social media activity data into a generative AI, and the generative AI proposes recommendation methods.

[0098] The notification unit can estimate the user's emotions and adjust the timing of notifications based on the estimated emotions. For example, if the user is excited, the notification unit will send an immediate notification. Alternatively, if the user is relaxed, the notification unit can send a notification at a normal time. Furthermore, if the user is stressed, the notification unit can reduce the frequency of notifications to alleviate the burden. This allows for more appropriate timing of notifications by adjusting the timing according to the user's 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-described processing in the notification unit may be performed using AI, or not. For example, the notification unit inputs user emotion data into the generative AI, which then adjusts the timing of notifications.

[0099] The notification unit can select the optimal notification method by referring to the user's past response history when sending a notification. For example, the notification unit may prioritize using notification methods that the user has preferred to respond to in the past. The notification unit can also select the most effective notification method from the user's past response history. Furthermore, the notification unit can analyze the user's response history and determine the optimal notification timing. This allows for the selection of a more effective notification method by referring to the user's past response history. Some or all of the above processing in the notification unit may be performed using, for example, a generative AI, or without a generative AI. For example, the notification unit inputs the user's response history data into a generative AI, and the generative AI selects the optimal notification method.

[0100] The notification unit can estimate the user's emotions and determine the priority of notifications based on the estimated emotions. For example, if the user is excited, the notification unit will prioritize important notifications. It can also deliver normal notifications if the user is relaxed. Furthermore, if the user is stressed, the notification unit can deliver only important notifications. This allows for prioritizing more important notifications based on the user's 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 notification unit may be performed using AI or not. For example, the notification unit inputs user emotion data into the generative AI, which then determines the priority of notifications.

[0101] The notification unit can select the optimal notification method by considering the user's device information when sending a notification. For example, if the user is using a smartphone, the notification unit will prioritize push notifications. It can also prioritize email notifications if the user is using a tablet. Furthermore, if the user is using a smartwatch, the notification unit can prioritize vibration notifications. This allows for the selection of a more effective notification method by considering the user's device information. Some or all of the above processing in the notification unit may be performed using, for example, a generative AI, or without one. For example, the notification unit inputs the user's device information into the generative AI, which then selects the optimal notification method.

[0102] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0103] The property competitiveness evaluation system can estimate the user's emotions and adjust how the property's competitiveness score is displayed based on those emotions. For example, if the user is excited, the score can be displayed using visually easy-to-understand graphs and icons. If the user is relaxed, a display method including detailed text information can be provided. Furthermore, if the user is stressed, only essential information can be displayed concisely. By providing the optimal display method according to the user's emotions, this system allows users to understand the property's competitiveness more effectively.

[0104] The property competitiveness evaluation system analyzes a user's past browsing history and can predict and recommend properties that the user might be interested in. For example, it can prioritize recommending similar properties based on the characteristics of properties the user has previously searched for. It can also prioritize recommending properties in specific areas or price ranges if the user has shown interest in those areas or price ranges. Furthermore, by analyzing the user's browsing history, it can recommend new properties that the user might be interested in. In this way, it can support users in selecting properties by recommending the most suitable properties based on their interests.

[0105] The property competitiveness evaluation system can estimate the user's emotions and adjust the timing of notifications based on those emotions. For example, if the user is excited, it can send an immediate notification. If the user is relaxed, it can send a notification at the usual time. Furthermore, if the user is stressed, it can reduce the frequency of notifications to alleviate their burden. In this way, by adjusting the timing of notifications according to the user's emotions, notifications can be sent at a more appropriate time.

[0106] The property competitiveness evaluation system can prioritize the collection of highly relevant market data by considering the user's geographical location. For example, it can prioritize the collection of market data for the area the user is currently in. It can also prioritize the collection of market data for areas the user is interested in. Furthermore, it can collect highly relevant market data for areas based on the user's past travel history. In this way, by considering the user's geographical location, it can prioritize the collection of highly relevant market data.

[0107] The property competitiveness evaluation system can estimate the user's emotions and adjust its analysis algorithm based on those emotions. For example, if the user is relaxed, it can perform a detailed analysis and provide highly accurate results. If the user is in a hurry, it can perform a rapid analysis and provide results immediately. Furthermore, if the user is excited, it can provide visually easy-to-understand analysis results. In this way, by adjusting the analysis algorithm according to the user's emotions, it can provide more appropriate analysis results.

[0108] The property competitiveness evaluation system can analyze users' social media activity and collect relevant market data. For example, it can collect market data related to properties that users have shown interest in on social media. It can also collect market data for areas of interest based on users' social media activity. Furthermore, it can collect relevant market data by referring to the activity of users' followers and friends on social media. In this way, relevant market data can be collected by analyzing users' social media activity.

[0109] The property competitiveness evaluation system can estimate the user's emotions and adjust the way recommendations are presented based on those emotions. For example, if the user is relaxed, it can provide detailed recommendations. If the user is in a hurry, it can provide concise recommendations. Furthermore, if the user is excited, it can provide visually easy-to-understand recommendations. By adjusting the way recommendations are presented according to the user's emotions, more appropriate recommendations become possible.

[0110] The property competitiveness evaluation system can customize its recommendation methods based on the user's current living situation. For example, if the user lives with family, it can prioritize recommending family-friendly properties. Similarly, if the user lives alone, it can prioritize recommending properties suitable for single individuals. Furthermore, it can recommend the most suitable property based on the user's specific living situation. This allows for the suggestion of more appropriate properties by customizing the recommendation methods based on the user's current living circumstances.

[0111] The property competitiveness evaluation system can estimate the user's emotions and adjust the display order of scores based on those emotions. For example, if the user is excited, the system will prioritize displaying the most recent competitiveness score. If the user is relaxed, it can prioritize displaying stable scores. Furthermore, if the user is stressed, it can prioritize displaying only important scores. This allows for a more appropriate display by adjusting the score display order according to the user's emotions.

[0112] The property competitiveness evaluation system can select the optimal notification method by referring to the user's past response history. For example, it can prioritize using notification methods that the user has preferred to respond to in the past. It can also select the most effective notification method based on the user's past response history. Furthermore, it can analyze the user's response history to determine the optimal notification timing. This allows for the selection of more effective notification methods by referring to the user's past response history.

[0113] The following briefly describes the processing flow for example form 2.

[0114] Step 1: The data collection unit collects market data. This market data includes real estate market data, economic indicator data, etc. For example, the data collection unit collects real estate market data, such as property prices, location, surrounding environment, and past transaction history. The data collection unit can also collect economic indicator data to provide data for evaluating the competitiveness of properties. Step 2: The analysis unit analyzes the data collected by the data collection unit. The analysis unit analyzes the collected data using statistical analysis and machine learning algorithms. For example, the analysis unit uses statistical analysis to evaluate factors such as property price, location, and surrounding environment. The analysis unit can also use machine learning algorithms to evaluate the popularity and competitiveness of properties. Step 3: The presentation section displays the property's competitiveness score. The presentation section displays the property's competitiveness score in real time, enabling users to select properties and develop negotiation strategies. For example, the presentation section checks the property's competitiveness score and provides a reminder function to encourage quick action on highly competitive properties, as well as advice on factors that are advantageous in negotiations (such as market price comparison data and seller background information).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] Each of the multiple elements described above, including the data collection unit, analysis unit, presentation unit, recommendation unit, and notification unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects market data using the camera 42 and communication I / F 44 of the smart device 14 and analyzes it using the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The presentation unit displays the competitiveness score of properties in real time using the display 40A of the smart device 14. The recommendation unit is implemented in the specific processing unit 290 of the data processing unit 12 and makes recommendations based on the user's interests. The notification unit is implemented in the control unit 46A of the smart device 14 and notifies users of competing properties. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0119] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] Each of the multiple elements described above, including the data collection unit, analysis unit, presentation unit, recommendation unit, and notification unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects market data using the camera 42 and communication I / F 44 of the smart glasses 214 and analyzes it using the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The presentation unit, for example, displays the competitiveness score of a property in real time using the display of the smart glasses 214. The recommendation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and makes recommendations based on the user's interests. The notification unit is implemented, for example, by the control unit 46A of the smart glasses 214 and notifies the user of competing properties. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0135] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] Each of the multiple elements described above, including the data collection unit, analysis unit, presentation unit, recommendation unit, and notification unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects market data using the camera 42 and communication I / F 44 of the headset terminal 314 and analyzes it using the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The presentation unit displays the competitiveness score of properties in real time using the display 343 of the headset terminal 314. The recommendation unit is implemented in the specific processing unit 290 of the data processing unit 12 and makes recommendations based on the user's interests. The notification unit is implemented in the control unit 46A of the headset terminal 314 and notifies users of competing properties. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0151] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.).

[0164] 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.

[0165] 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.

[0166] 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.

[0167] Each of the multiple elements described above, including the data collection unit, analysis unit, presentation unit, recommendation unit, and notification unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects market data using the camera 42 and communication I / F 44 of the robot 414 and analyzes it using the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The presentation unit, for example, uses the display of the robot 414 to display the competitiveness score of the properties in real time. The recommendation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and makes recommendations based on the user's interests. The notification unit is implemented, for example, by the control unit 46A of the robot 414 and notifies users of competing properties. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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."

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] (Note 1) The data collection department collects market data, An analysis unit analyzes the data collected by the aforementioned collection unit, It includes a display unit that presents a competitiveness score for the property. A system characterized by the following features. (Note 2) It includes a recommendation section that provides recommendations based on user interests. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a notification unit for notifying about competing properties. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is We collect data such as property price, location, surrounding environment, and past transaction history. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, The collected data is analyzed to evaluate the popularity and competitiveness of the property. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned display unit is, Provides real-time competitiveness scores for properties. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate user sentiment and adjust the timing of market data collection based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is We analyze past market data fluctuation patterns to determine the optimal data collection frequency. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is The types of data collected are dynamically changed based on the user's interests and past search history. 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 During data collection, the system prioritizes collecting highly relevant market 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, we analyze users' social media activity and gather relevant market data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, we improve the accuracy of the analysis by referring to past data analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During the analysis, different analysis methods are applied to each property category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During the analysis, the geographical distribution of the properties will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During the analysis, we refer to relevant literature on the property to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned display unit is, We estimate user sentiment and adjust how competitive scores are displayed based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned display unit is, When presenting, adjust the level of detail in the score based on the importance of the property. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned display unit is, When presenting properties, different display algorithms are applied depending on the property category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned display unit is, It estimates the user's emotions and adjusts the display order of scores based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned display unit is, When presenting a property, the scoring priority will be determined based on when the property was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned display unit is, When presenting, the order of scores will be adjusted based on the relevance of the properties. The system described in Appendix 1, characterized by the features described herein. (Note 25) The recommendation unit is, It estimates the user's emotions and adjusts the way recommendations are presented based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 26) The recommendation unit is, When making recommendations, the system uses the user's past search history to suggest the most suitable properties. The system described in Appendix 2, characterized by the features described herein. (Note 27) The recommendation unit is, When making recommendations, customize the recommendation method based on the user's current lifestyle. The system described in Appendix 2, characterized by the features described herein. (Note 28) The recommendation unit is, It estimates the user's emotions and determines the priority of recommendations based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 29) The recommendation unit is, When making recommendations, the system takes the user's geographical location into consideration to suggest the most suitable properties. The system described in Appendix 2, characterized by the features described herein. (Note 30) The recommendation unit is, When making recommendations, the system analyzes the user's social media activity to suggest appropriate recommendation methods. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned notification unit, It estimates the user's emotions and adjusts the timing of notifications based on those emotions. The system described in Appendix 3, characterized by the features described herein. (Note 32) The aforementioned notification unit, When sending a notification, the system will refer to the user's past response history to select the most suitable notification method. The system described in Appendix 3, characterized by the features described herein. (Note 33) The aforementioned notification unit, It estimates the user's emotions and prioritizes notifications based on those emotions. The system described in Appendix 3, characterized by the features described herein. (Note 34) The aforementioned notification unit, When sending notifications, the system selects the most suitable notification method, taking into account the user's device information. The system described in Appendix 3, characterized by the features described herein. [Explanation of symbols]

[0187] 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 market data, An analysis unit analyzes the data collected by the aforementioned collection unit, It includes a display unit that presents a competitiveness score for the property. A system characterized by the following features.

2. It includes a recommendation section that provides recommendations based on user interests. The system according to feature 1.

3. It includes a notification unit for notifying about competing properties. The system according to feature 1.

4. The aforementioned collection unit is We collect data such as property price, location, surrounding environment, and past transaction history. The system according to feature 1.

5. The aforementioned analysis unit, The collected data is analyzed to evaluate the popularity and competitiveness of the property. The system according to feature 1.

6. The aforementioned display unit is, Provides real-time competitiveness scores for properties. The system according to feature 1.

7. The aforementioned collection unit is We estimate user sentiment and adjust the timing of market data collection based on the estimated user sentiment. The system according to feature 1.

8. The aforementioned collection unit is We analyze past market data fluctuation patterns to determine the optimal data collection frequency. The system according to feature 1.

9. The aforementioned collection unit is The types of data collected are dynamically changed based on the user's interests and past search history. The system according to feature 1.

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