system

Abstract

The system according to this embodiment aims to efficiently perform property searches and agent selection. [Solution] The system according to the embodiment comprises a reception unit, an update unit, a matching unit, an analysis unit, a recommendation unit, and a proposal unit. The reception unit receives the user's desired conditions. The update unit updates the property list based on the information received by the reception unit. The matching unit matches the user with the most suitable property based on the property list updated by the update unit. The analysis unit analyzes the agent's past transaction data and user reviews. The recommendation unit recommends a highly reliable agent based on the data analyzed by the analysis unit. The proposal unit analyzes market trends and proposes the optimal timing for buying and selling.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to efficiently search for a property and select an agent, and there is a problem that it is time-consuming for the user.

[0005] The system according to the embodiment aims to efficiently search for a property and select an agent.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an update unit, a matching unit, an analysis unit, a recommendation unit, and a proposal unit. The reception unit receives the user's desired conditions. The update unit updates the property list based on the information received by the reception unit. The matching unit matches the user with the most suitable property based on the property list updated by the update unit. The analysis unit analyzes the agent's past transaction data and user reviews. The recommendation unit recommends a highly reliable agent based on the data analyzed by the analysis unit. The proposal unit analyzes market trends and proposes the optimal timing for buying and selling. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently perform property searches and agent selection. [Brief explanation of the drawing]

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

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

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

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

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

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

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

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

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

[0028] (Example of form 1) An embodiment of the present invention provides a real estate matching system that automatically matches the user with the most suitable property based on their desired conditions and recommends a highly reliable real estate agent. In this system, the user inputs their desired conditions, and the AI ​​matches the user with the most suitable property based on those conditions. The AI ​​updates the property list in real time and recommends properties to the user. Furthermore, the AI ​​analyzes the agent's past transaction data and user reviews to recommend a highly reliable agent. The AI ​​also analyzes market trends and proposes the optimal timing for buying or selling. For example, the real estate matching system includes a reception unit where the user inputs their desired conditions. The reception unit allows the user to input conditions such as price, location, floor plan, and year of construction. Next, the update unit updates the property list based on the information received by the reception unit. The update unit updates the property list in real time, providing the latest property information. Furthermore, the matching unit matches the user with the most suitable property based on the property list updated by the update unit. The matching unit selects the most suitable property based on factors such as the degree of match with the user's desired conditions and the property's evaluation score. Next, the analysis unit analyzes the agent's past transaction data and user reviews. The analysis unit evaluates the reliability of the agents and identifies highly reliable agents. Next, the Recommendation Department recommends highly reliable agents based on data analyzed by the Analysis Department. The Recommendation Department personalizes and recommends agents that match the user's desired conditions. Finally, the Proposal Department analyzes market trends and proposes the optimal timing for buying and selling. The Proposal Department analyzes market trends based on price fluctuations and transaction volume trends and proposes the optimal timing for the user. As a result, the real estate matching system can efficiently find properties that meet the user's desired conditions and easily find a reliable real estate agent. It can also significantly reduce the time and effort required for property search and agent selection. In this way, the real estate matching system can automatically match the best properties based on the user's desired conditions and recommend highly reliable real estate agents.

[0029] The real estate matching system according to this embodiment comprises a reception unit, an update unit, a matching unit, an analysis unit, a recommendation unit, and a proposal unit. The reception unit receives input from the user regarding their desired conditions. These conditions include, but are not limited to, price, location, floor plan, and age of the building. For example, the reception unit can receive input from the user regarding their desired price range. The reception unit can also receive input from the user regarding their desired location. Furthermore, the reception unit can receive input from the user regarding their desired floor plan and age of the building. The update unit updates the property list based on the information received by the reception unit. For example, the update unit can update the property list in real time. The update unit can also register and delete properties. Furthermore, the update unit can periodically update property information. The matching unit matches the user with the most suitable property based on the property list updated by the update unit. For example, the matching unit can select a property based on its degree of match with the user's desired conditions. The matching unit can also select a property based on its evaluation score. Furthermore, the matching unit can also select a property based on the user's past search history. The Analysis Department analyzes agents' past transaction data and user reviews. For example, the Analysis Department can evaluate reliability based on an agent's transaction history. It can also evaluate reliability based on user review scores. Furthermore, the Analysis Department can evaluate reliability based on an agent's past transaction history. The Recommendation Department recommends highly reliable agents based on the data analyzed by the Analysis Department. For example, the Recommendation Department can personalize and recommend agents that match the user's desired conditions. It can also recommend agents based on their evaluation scores. Furthermore, the Recommendation Department can recommend agents based on their transaction history. The Proposal Department analyzes market trends and proposes the optimal timing for buying and selling. For example, the Proposal Department can analyze market trends based on price fluctuations. It can also analyze market trends based on the trend in the number of transactions. Furthermore, the Proposal Department can analyze market trends based on historical market data.As a result, the real estate matching system according to this embodiment can automatically match the user with the most suitable property based on their desired conditions and recommend a highly reliable real estate agent.

[0030] The reception desk allows users to input their desired criteria. These criteria may include, but are not limited to, price, location, floor plan, and age of the building. For example, the reception desk can allow users to input their desired price range, location, floor plan, and age of the building. Specifically, the reception desk is designed to allow users to easily input their desired criteria through a user interface. For example, users can select a price range using dropdown menus or sliders, or specify a desired location on a map. Detailed conditions for floor plan and age of the building can also be set using checkboxes or text input fields. Furthermore, the reception desk has a function to save the user's entered criteria for later reuse, saving users the trouble of re-entering criteria. The reception desk can also provide real-time feedback based on the user's entered criteria. For example, it can instantly display the number of properties matching the entered criteria and a list of properties that meet the criteria. This allows users to adjust their criteria while checking how realistic their preferences are. Furthermore, the reception desk can provide relevant advice and suggestions based on the conditions entered by the user. For example, if the desired price range differs significantly from the average market price, it can explain the reasons and suggest alternatives. This allows users to set more realistic conditions and helps them find the best property.

[0031] The update department updates the property list based on information received by the reception department. The update department can, for example, update the property list in real time. It can also register and delete properties. Furthermore, the update department can periodically update property information. Specifically, the update department works with the database to retrieve the latest property information and provide it to users. For example, it immediately reflects new property information provided by real estate agents and property owners, ensuring users always have access to the most up-to-date information. It also quickly reflects information when a property is sold or contracted, removing it from the list to provide users with accurate information. Furthermore, the update department maintains the freshness of information by regularly updating property information. For example, it periodically checks the database and makes necessary updates to reflect the latest status, such as price changes or renovation information. The update department can also verify and cross-check data to ensure the reliability of property information. This allows users to use property information with confidence. Additionally, the update department can improve property information based on user feedback. For example, it can correct incorrect information or provide additional information based on user reports. This allows the update department to consistently provide accurate and reliable property information, thereby improving user satisfaction.

[0032] The matching unit matches the user with the most suitable property based on the property list updated by the update unit. For example, the matching unit can select properties based on how well they match the user's desired conditions. It can also select properties based on their evaluation score. Furthermore, the matching unit can select properties based on the user's past search history. Specifically, the matching unit uses an AI algorithm to compare the user's desired conditions with property information and lists properties with a high degree of match. For example, it comprehensively evaluates conditions such as price, location, floor plan, and age of the building to select the most suitable property. In addition, since the property evaluation score is calculated based on past user reviews and transaction history, it can prioritize recommending highly reliable properties. Furthermore, the matching unit analyzes the user's past search and browsing history to understand their preferences and tendencies. This allows it to predict properties that the user might be interested in and provide personalized recommendations. For example, for users who have frequently searched for properties in a specific area or price range in the past, it will prioritize displaying new properties that match that area or price range. The matching unit can also continuously improve its recommendation algorithm based on user feedback. For example, if a user gives a high rating to a recommended property, that rating is reflected in the algorithm to improve the accuracy of future recommendations. This allows the matching system to quickly and accurately provide users with the most suitable properties, thereby increasing their satisfaction.

[0033] The analytics department analyzes agents' past transaction data and user reviews. For example, the analytics department can assess reliability based on an agent's transaction history. It can also assess reliability based on user review scores. Furthermore, it can assess reliability based on an agent's past transaction history. Specifically, the analytics department uses data mining techniques to analyze agents' transaction data and user reviews in detail. For example, it evaluates the number of properties an agent has handled in the past, their closing rate, and the speed of transactions to identify highly reliable agents. User review scores reflect the quality of the agent's service and customer satisfaction, and can be used to assess an agent's reliability. Furthermore, the analytics department can evaluate an agent's expertise in specific areas or property types based on their past transaction history. This allows users to find the agent best suited to their needs. Based on this data, the analytics department quantitatively evaluates agent performance and provides foundational information for recommending highly reliable agents. The analytics department also continuously monitors agent performance and updates evaluations regularly. This ensures users always select agents based on the latest information. Furthermore, the analytics department can revise its evaluation criteria based on user feedback, enabling more accurate evaluations. This allows the analytics department to provide users with reliable agents and improve their satisfaction.

[0034] The recommendation department recommends highly reliable agents based on data analyzed by the analytics department. For example, the recommendation department can personalize recommendations to match the user's desired criteria. It can also recommend agents based on their evaluation scores. Furthermore, it can recommend agents based on their transaction history. Specifically, the recommendation department uses AI to analyze data provided by the analytics department and identify the agent best suited to the user's desired criteria. For example, it prioritizes recommending agents who are familiar with the user's desired area and property type. Additionally, since agent evaluation scores are calculated based on past transaction history and user reviews, it can recommend highly reliable agents. Furthermore, the recommendation department analyzes agents' transaction history in detail to evaluate their expertise in specific areas and property types. This allows users to find the agent best suited to their desired criteria. The recommendation department can also continuously improve its recommendation algorithm based on user feedback. For example, if a user gives a high rating to a recommended agent, that rating is reflected in the algorithm to improve future recommendation accuracy. Furthermore, the recommendation team can analyze users' search and browsing history to understand their preferences and trends, enabling them to provide more personalized recommendations. This allows the recommendation team to quickly and accurately provide users with the most suitable agents, thereby increasing user satisfaction.

[0035] The proposal department analyzes market trends and suggests the optimal timing for buying and selling. For example, it can analyze market trends based on price fluctuations. It can also analyze market trends based on the number of transactions. Furthermore, it can analyze market trends based on historical market data. Specifically, the proposal department uses AI to analyze historical market data and current market conditions in detail to predict future price fluctuations and transaction volume trends. For example, based on price fluctuation data from the past few years, it predicts future price increases or decreases and suggests the optimal timing for buying and selling to the user. By analyzing the number of transactions, it can grasp market activity and demand fluctuations and provide appropriate advice to the user. Furthermore, the proposal department can make more accurate market forecasts by considering regional market characteristics and economic indicators. This allows users to accurately grasp market trends and trade at the optimal time. The proposal department can also continuously improve its suggestions based on user feedback. For example, it analyzes the results of transactions made by users based on the suggestions and reflects these results in the algorithm to improve the accuracy of future suggestions. Furthermore, the proposal department can provide individually customized proposals, taking into account the user's desired conditions and transaction history. This allows the proposal department to offer users the optimal timing for buying and selling, thereby increasing their satisfaction.

[0036] The learning unit learns the user's desired conditions. The learning unit can learn the user's desired conditions using, for example, a machine learning algorithm. The learning unit can also learn desired conditions based on the user's past search history. Furthermore, the learning unit can learn desired conditions based on the user's input data. For example, the learning unit can analyze the desired conditions the user has entered in the past and learn the user's preferences. The learning unit can learn the characteristics of properties the user has searched for in the past and estimate the user's desired conditions. Furthermore, the learning unit can learn the characteristics of properties the user likes based on the user's input data. Furthermore, the learning unit can learn the characteristics of properties the user wants to avoid based on the user's past search history. As a result, by learning the user's desired conditions, the learning unit can achieve more accurate property matching. Some or all of the above processing in the learning unit may be performed using, for example, AI, or not using AI. For example, the learning unit can input the user's past search history into a generating AI and have the generating AI perform the learning of the user's desired conditions.

[0037] The update unit can update the property list in real time. For example, the update unit can register and delete properties in real time. The update unit can also update property information in real time. Furthermore, the update unit can update the status of properties in real time. For example, the update unit can register new properties in real time and reflect them in the property list. The update unit can also delete properties in real time and remove them from the property list. Furthermore, the update unit can update property information in real time to provide the latest information. As a result, by updating the property list in real time, the latest property information can be provided. Some or all of the above processes in the update unit may be performed using AI, for example, or not using AI. For example, the update unit can input information on new property registration and deletion into a generating AI and have the generating AI perform the property list update.

[0038] The analysis department can analyze agents' past transaction data and user reviews. For example, the analysis department can evaluate reliability based on an agent's trading performance. It can also evaluate reliability based on user review scores. Furthermore, it can evaluate reliability based on an agent's past transaction history. For example, the analysis department can analyze an agent's trading performance to identify highly reliable agents. It can also analyze user review scores to identify highly reliable agents. Furthermore, it can analyze an agent's past transaction history to identify highly reliable agents. This allows the analysis department to recommend highly reliable agents by analyzing their past transaction data and user reviews. Some or all of the above processing in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can input agent trading performance and user review data into a generating AI and have the generating AI perform the reliability evaluation.

[0039] The recommendation system can personalize and recommend highly reliable agents. For example, the recommendation system can personalize and recommend agents that match the user's desired conditions. It can also recommend agents based on their evaluation scores. Furthermore, it can recommend agents based on their transaction history. For example, the recommendation system personalizes and recommends agents based on the user's desired conditions. It can also recommend highly reliable agents based on their evaluation scores. Furthermore, it can recommend highly reliable agents based on their transaction history. This allows the system to provide the user with the most suitable agent by personalizing and recommending highly reliable agents. Some or all of the above processes in the recommendation system may be performed using AI, for example, or without AI. For example, the recommendation system can input the user's desired conditions and agent evaluation scores into a generating AI and have the generating AI perform agent recommendations.

[0040] The proposal unit can analyze market trends and propose the optimal timing for buying and selling. For example, the proposal unit can analyze market trends based on price fluctuations. It can also analyze market trends based on the trend in the number of transactions. Furthermore, the proposal unit can analyze market trends based on historical market data. For example, the proposal unit can analyze price fluctuations and propose the optimal timing for buying and selling. It can also analyze the trend in the number of transactions and propose the optimal timing for buying and selling. Furthermore, the proposal unit can analyze historical market data and propose the optimal timing for buying and selling. In this way, by analyzing market trends and proposing the optimal timing for buying and selling, it can support optimal transactions for users. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input price fluctuation and transaction volume data into a generating AI and have the generating AI perform market trend analysis.

[0041] The reception desk can analyze the user's past history of entering desired conditions and select the optimal input method. For example, the reception desk can automatically display desired conditions that the user has frequently entered in the past as candidates. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest desired conditions to be used during specific time periods based on the user's past input history. For example, the reception desk can analyze the user's past history of entering desired conditions and identify the input method the user prefers. The reception desk can also identify input methods that the user wants to avoid based on the user's past input history. Furthermore, the reception desk can predict and suggest desired conditions to be used during specific time periods based on the user's past input history. In this way, by analyzing the user's past history of entering desired conditions, the optimal input method can be provided. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past history of entering desired conditions into a generating AI and have the generating AI select the optimal input method.

[0042] The reception desk can filter the user's current living situation and areas of interest when they input their desired conditions. For example, if the user enters their family structure, the reception desk will prioritize displaying properties suitable for families. It can also prioritize displaying pet-friendly properties if the user owns pets. Furthermore, if the user enters their hobbies or areas of interest, the reception desk can prioritize displaying properties related to those interests. For example, the reception desk can filter properties suitable for families based on the user's family structure. It can also filter properties that allow pets if the user owns pets. Furthermore, it can filter properties related to the user's hobbies and areas of interest. This allows the reception desk to provide more appropriate properties by filtering based on the user's current living situation and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input data on the user's living situation and areas of interest into a generating AI and have the generating AI perform the filtering.

[0043] The reception desk can prioritize inputting highly relevant conditions when users enter their desired conditions, taking into account their geographical location. For example, if a user enters their current location, the reception desk will prioritize displaying properties in that area. Furthermore, if a user specifies a particular region, the reception desk can prioritize displaying properties in that region. Additionally, if a user is on the move, the reception desk can suggest the most suitable properties based on their current location. For example, the reception desk can filter properties in the vicinity based on the user's current location. Furthermore, if a user specifies a particular region, the reception desk can filter properties in that region. Additionally, if a user is on the move, the reception desk can suggest the most suitable properties based on their current location. This allows the reception desk to provide more appropriate properties by prioritizing the input of highly relevant conditions, taking into account the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's geographical location information into a generating AI and have the generating AI perform the filtering of highly relevant conditions.

[0044] The reception desk can analyze the user's social media activity when they input their desired conditions and input relevant conditions. For example, the reception desk can suggest relevant properties based on places the user has shared on social media. It can also analyze the content of the user's social media posts and suggest properties based on their interests. Furthermore, the reception desk can suggest relevant properties based on the areas where the user's social media friends live. For example, the reception desk can filter relevant properties based on places the user has shared on social media. It can also analyze the content of the user's social media posts and filter properties based on their interests. Furthermore, the reception desk can filter relevant properties based on the areas where the user's social media friends live. This allows the reception desk to provide more appropriate properties by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input data on the user's social media activity into a generating AI and have the generating AI perform filtering of relevant conditions.

[0045] The update unit can select the optimal update method by referring to past update history when updating the property list. For example, the update unit can analyze past update history to understand the types of properties users prefer and update accordingly. The update unit can also prioritize displaying properties that users have shown interest in based on past update history. Furthermore, the update unit can exclude properties that users want to avoid based on past update history. For example, the update unit can analyze past update history to understand the types of properties users prefer. The update unit can also prioritize displaying properties that users have shown interest in based on past update history. Furthermore, the update unit can exclude properties that users want to avoid based on past update history. This allows the update unit to provide the optimal method for updating the property list by referring to past update history. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input past update history data into a generating AI and have the generating AI select the optimal update method.

[0046] The update unit can apply different update algorithms depending on the property category when updating the property list. For example, for residential properties, the update unit applies an update algorithm that reflects the latest market trends. For commercial properties, the update unit can also apply an update algorithm that takes into account the surrounding business environment. Furthermore, for investment properties, the update unit can also apply an update algorithm that prioritizes profitability. By applying different update algorithms depending on the property category, more appropriate property information can be provided. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input data corresponding to the property category into a generating AI and have the generating AI execute the application of the update algorithm.

[0047] The update unit can update the property list while considering the geographical distribution of properties. For example, if the user specifies a particular region, the update unit will prioritize updating properties in that region. Furthermore, if the user specifies a wide area, the update unit can update properties in a balanced manner while considering geographical distribution. Additionally, if the user is on the move, the update unit can update the most suitable properties based on their current location. This allows for the provision of more appropriate property information by considering the geographical distribution of properties during updates. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input data on the geographical distribution of properties into a generating AI and have the generating AI perform the update.

[0048] The update unit can improve the accuracy of updates by referring to relevant literature for properties when updating the property list. For example, the update unit can refer to relevant literature for properties and perform updates that reflect the latest market trends. The update unit can also prioritize updating properties that users are likely to be interested in based on the relevant literature. Furthermore, the update unit can analyze the relevant literature for properties and exclude properties that users want to avoid when updating. For example, the update unit can refer to relevant literature for properties and perform updates that reflect the latest market trends. The update unit can also prioritize updating properties that users are likely to be interested in based on the relevant literature for properties. Furthermore, the update unit can analyze the relevant literature for properties and exclude properties that users want to avoid when updating. This improves the accuracy of updates by referring to relevant literature for properties. Some or all of the above processing in the update unit may be performed using AI, for example, or not using AI. For example, the update unit can input data on relevant literature for properties into a generating AI and have the generating AI perform the improvement of update accuracy.

[0049] The matching unit can improve the accuracy of matching by considering the interrelationships between properties during the matching process. For example, the matching unit can consider the surrounding environment of a property and prioritize matching properties that are related to it. It can also consider the price range of properties and prioritize matching properties in the same price range. Furthermore, the matching unit can consider the facilities and conditions of properties and prioritize matching properties that meet the user's preferences. For example, the matching unit can consider the surrounding environment of a property and prioritize matching properties that are related to it. It can also consider the price range of properties and prioritize matching properties in the same price range. Furthermore, the matching unit can consider the facilities and conditions of properties and prioritize matching properties that meet the user's preferences. This improves the accuracy of matching by considering the interrelationships between properties. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input data on the interrelationships between properties into a generating AI and have the generating AI perform the matching accuracy improvement.

[0050] The matching unit can perform matching while considering the attribute information of the property submitter. For example, if the property submitter is a highly reliable agent, the matching unit will prioritize matching with that property. The matching unit can also prioritize matching with properties submitted by agents who have received high ratings in the past. Furthermore, the matching unit can also prioritize matching with properties submitted by agents who have attributes that match the user's desired conditions. For example, if the property submitter is a highly reliable agent, the matching unit will prioritize matching with that property. The matching unit can also prioritize matching with properties submitted by agents who have received high ratings in the past. Furthermore, the matching unit can also prioritize matching with properties submitted by agents who have attributes that match the user's desired conditions. This allows for the provision of more reliable properties by considering the attribute information of the property submitter. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input the attribute information of the property submitter into a generating AI and have the generating AI perform the matching.

[0051] The matching unit can perform matching while considering the geographical distribution of properties. For example, if the user specifies a particular area, the matching unit will prioritize matching properties in that area. Furthermore, if the user specifies a wide area, the matching unit can also match properties in a balanced manner while considering geographical distribution. Additionally, if the user is on the move, the matching unit can match the most suitable property based on their current location. This allows for the provision of more appropriate properties by considering the geographical distribution of properties during the matching process. Some or all of the above-described processes in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input data on the geographical distribution of properties into a generating AI and have the generating AI perform the matching.

[0052] The matching unit can improve the accuracy of matching by referring to relevant literature on properties during the matching process. For example, the matching unit can refer to relevant literature on properties to perform matching that reflects the latest market trends. The matching unit can also prioritize matching properties that are likely to interest the user based on the relevant literature. Furthermore, the matching unit can analyze the relevant literature on properties and exclude properties that the user wants to avoid during the matching process. For example, the matching unit can refer to relevant literature on properties to perform matching that reflects the latest market trends. The matching unit can also prioritize matching properties that are likely to interest the user based on the relevant literature. Furthermore, the matching unit can analyze the relevant literature on properties and exclude properties that the user wants to avoid during the matching process. This improves the accuracy of matching by referring to relevant literature on properties. Some or all of the above processing in the matching unit may be performed using AI, for example, or not. For example, the matching unit can input data on relevant literature on properties into a generating AI and have the generating AI perform the matching accuracy improvement.

[0053] The analysis unit can optimize the current analysis by referring to past analysis data during the analysis process. For example, the analysis unit can prioritize displaying agents that are likely to interest the user based on past analysis data. The analysis unit can also exclude agents that the user wants to avoid from past analysis data. Furthermore, the analysis unit can prioritize displaying agents that match the user's desired conditions by referring to past analysis data. For example, the analysis unit can prioritize displaying agents that are likely to interest the user based on past analysis data. The analysis unit can also exclude agents that the user wants to avoid from past analysis data. Furthermore, the analysis unit can prioritize displaying agents that match the user's desired conditions by referring to past analysis data. In this way, the current analysis can be optimized by referring to past analysis data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past analysis data into a generating AI and have the generating AI perform the optimization of the current analysis.

[0054] The analysis unit can apply different analytical methods to each agent category during analysis. For example, in the case of residential agents, the analysis unit can apply an analytical method that emphasizes past transaction data. In the case of commercial agents, the analysis unit can also apply an analytical method that takes the business environment into consideration. Furthermore, in the case of investment agents, the analysis unit can also apply an analytical method that emphasizes profitability. By applying different analytical methods to each agent category, more appropriate information can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data for each agent category into a generating AI and have the generating AI execute the application of analytical methods.

[0055] The analysis unit can analyze changes in the analysis based on the agent's trading period during the analysis. For example, the analysis unit can analyze seasonal trading trends based on the agent's trading period. The analysis unit can also prioritize displaying agents that perform well during specific periods based on the agent's trading period. Furthermore, the analysis unit can analyze peak trading periods based on the agent's trading period. For example, the analysis unit can analyze seasonal trading trends based on the agent's trading period. The analysis unit can also prioritize displaying agents that perform well during specific periods based on the agent's trading period. Furthermore, the analysis unit can analyze peak trading periods based on the agent's trading period. This allows for the provision of more appropriate information by analyzing changes in the analysis based on the agent's trading period. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input agent trading period data into a generating AI and have the generating AI execute the changes in the analysis.

[0056] The analysis unit can perform analysis by referring to the relevant market data of agents. For example, the analysis unit can perform analysis that reflects the latest market trends based on the relevant market data of agents. The analysis unit can also prioritize displaying agents that are likely to interest the user based on the relevant market data of agents. Furthermore, the analysis unit can analyze the relevant market data of agents and exclude agents that the user wants to avoid. For example, the analysis unit can perform analysis that reflects the latest market trends based on the relevant market data of agents. The analysis unit can also prioritize displaying agents that are likely to interest the user based on the relevant market data of agents. Furthermore, the analysis unit can analyze the relevant market data of agents and exclude agents that the user wants to avoid. This allows for the provision of more appropriate information by referring to the relevant market data of agents. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the relevant market data of agents into a generating AI and have the generating AI perform the analysis.

[0057] The recommendation system can improve the accuracy of recommendations by considering the interrelationships between agents. For example, the recommendation system can prioritize recommending interrelated agents based on their past transaction data. It can also prioritize recommending highly reliable agents based on user reviews of agents. Furthermore, it can prioritize recommending agents that match the user's desired conditions based on their transaction history. By considering the interrelationships between agents, the recommendation system improves the accuracy of recommendations. Some or all of the above processing in the recommendation system may be performed using AI, for example, or without AI. For example, the recommendation system can input data on the interrelationships between agents into a generating AI and have the generating AI perform the recommendation accuracy improvement.

[0058] The recommendation department can make recommendations by considering the attribute information of agents. For example, the recommendation department can recommend agents that match the user's desired conditions based on the agent's area of ​​expertise. The recommendation department can also recommend highly reliable agents based on the agent's past transaction record. Furthermore, the recommendation department can recommend highly rated agents based on the agent's user reviews. For example, the recommendation department can recommend agents that match the user's desired conditions based on the agent's area of ​​expertise. The recommendation department can also recommend highly reliable agents based on the agent's past transaction record. Furthermore, the recommendation department can recommend highly rated agents based on the agent's user reviews. This allows for the provision of more appropriate agents by considering the attribute information of agents. Some or all of the above processing in the recommendation department may be performed using AI, for example, or not using AI. For example, the recommendation department can input agent attribute information data into a generating AI and have the generating AI perform the recommendations.

[0059] The recommendation system can make recommendations while considering the geographical distribution of agents. For example, if a user specifies a particular region, the recommendation system will prioritize recommending agents in that region. Furthermore, if a user specifies a wide area, the recommendation system can recommend agents in a balanced manner while considering geographical distribution. Additionally, if a user is on the move, the recommendation system can recommend the most suitable agent based on their current location. This allows for the provision of more appropriate agents by considering the geographical distribution of agents. Some or all of the above processing in the recommendation system may be performed using AI, for example, or without AI. For example, the recommendation system can input data on the geographical distribution of agents into a generating AI and have the generating AI perform the recommendations.

[0060] The recommendation system can improve the accuracy of recommendations by referring to the agent's relevant literature during the recommendation process. For example, the recommendation system can refer to the agent's relevant literature to make recommendations that reflect the latest market trends. Furthermore, based on the agent's relevant literature, the recommendation system can prioritize recommending agents that the user is likely to be interested in. In addition, the recommendation system can analyze the agent's relevant literature and exclude agents that the user wants to avoid. This improves the accuracy of recommendations by referring to the agent's relevant literature. Some or all of the above processing in the recommendation system may be performed using AI, for example, or without AI. For example, the recommendation system can input data on the agent's relevant literature into a generating AI and have the generating AI perform the recommendation accuracy improvement.

[0061] The proposal unit can optimize its proposal algorithm by referring to past proposal data when making a proposal. For example, the proposal unit can prioritize displaying proposals that are likely to interest the user based on past proposal data. It can also exclude proposals that the user wants to avoid from past proposal data. Furthermore, the proposal unit can prioritize displaying proposals that match the user's desired conditions by referring to past proposal data. For example, the proposal unit can prioritize displaying proposals that are likely to interest the user based on past proposal data. It can also exclude proposals that the user wants to avoid from past proposal data. Furthermore, the proposal unit can prioritize displaying proposals that match the user's desired conditions by referring to past proposal data. In this way, the proposal algorithm can be optimized by referring to past proposal data. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input past proposal data into a generation AI and have the generation AI perform the optimization of the proposal algorithm.

[0062] The proposal unit can apply different proposal methods to each property category when making proposals. For example, in the case of residential properties, the proposal unit can apply a proposal method that reflects the latest market trends. In the case of commercial properties, the proposal unit can also apply a proposal method that takes into account the surrounding business environment. Furthermore, in the case of investment properties, the proposal unit can also apply a proposal method that emphasizes profitability. By applying different proposal methods to each property category, the proposal unit can provide more appropriate proposals. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not. For example, the proposal unit can input data for each property category into a generating AI and have the generating AI execute the application of proposal methods.

[0063] The proposal department can analyze changes in proposals based on the property submission timing. For example, the proposal department can analyze seasonal proposal trends based on the property submission timing. The proposal department can also prioritize displaying proposals that are strong during specific periods based on the property submission timing. Furthermore, the proposal department can analyze peak periods for proposals based on the property submission timing. By analyzing changes in proposals based on the property submission timing, more appropriate proposals can be provided. Some or all of the above processing in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input property submission timing data into a generation AI and have the generation AI execute the changes in proposals.

[0064] The proposal unit can make proposals by referring to relevant market data for the property. For example, the proposal unit can make proposals that reflect the latest market trends based on the relevant market data for the property. The proposal unit can also prioritize displaying proposals that are likely to interest the user based on the relevant market data for the property. Furthermore, the proposal unit can analyze the relevant market data for the property and exclude proposals that the user would like to avoid. For example, the proposal unit can make proposals that reflect the latest market trends based on the relevant market data for the property. Furthermore, the proposal unit can prioritize displaying proposals that are likely to interest the user based on the relevant market data for the property. Furthermore, the proposal unit can analyze the relevant market data for the property and exclude proposals that the user would like to avoid. This allows for the provision of more appropriate proposals by referring to relevant market data for the property. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input relevant market data for the property into a generating AI and have the generating AI execute the proposals.

[0065] The learning unit can optimize its learning algorithm by referring to past learning data during the learning process. For example, the learning unit can prioritize learning properties that users are likely to be interested in based on past learning data. The learning unit can also exclude properties that users want to avoid from past learning data. Furthermore, the learning unit can refer to past learning data and prioritize learning properties that match the user's desired conditions. For example, the learning unit can prioritize learning properties that users are likely to be interested in based on past learning data. The learning unit can also exclude properties that users want to avoid from past learning data. Furthermore, the learning unit can refer to past learning data and prioritize learning properties that match the user's desired conditions. This allows the learning algorithm to be optimized by referring to past learning data. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input past learning data into a generating AI and have the generating AI perform the optimization of the learning algorithm.

[0066] The learning unit can weight the training data based on the property submission date during training. For example, the learning unit can prioritize learning the most recent data based on the property submission date. The learning unit can also prioritize learning data that is strong during specific periods based on the property submission date. Furthermore, the learning unit can analyze and weight the peak periods of training based on the property submission date. For example, the learning unit can prioritize learning the most recent data based on the property submission date. Furthermore, the learning unit can also prioritize learning data that is strong during specific periods based on the property submission date. Furthermore, the learning unit can analyze and weight the peak periods of training based on the property submission date. This allows for more appropriate training by weighting the training data based on the property submission date. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input property submission date data into a generating AI and have the generating AI perform the weighting of the training data.

[0067] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0068] The reception desk can analyze the user's past request input history and select the optimal input method. For example, it can automatically display requests that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). Furthermore, it can predict and suggest requests that the user will use during specific time periods based on their past input history. In this way, by analyzing the user's past request input history, the system can provide the most suitable input method.

[0069] The update unit can select the optimal update method when updating the property list by referring to past update history. For example, it can analyze past update history to understand the types of properties users prefer and update accordingly. It can also prioritize displaying properties that users have shown interest in based on past update history. Furthermore, it can exclude properties that users want to avoid based on past update history. In this way, by referring to past update history, the system can provide the most optimal method for updating the property list.

[0070] The matching function can improve the accuracy of matching by considering the interrelationships between properties during the matching process. For example, it can prioritize matching properties that are related to each other by considering the surrounding environment of the properties. It can also prioritize matching properties in the same price range by considering the price range of the properties. Furthermore, it can prioritize matching properties that match the user's preferences by considering the facilities and conditions of the properties. In this way, the accuracy of matching is improved by considering the interrelationships between properties.

[0071] The recommendation system can improve the accuracy of recommendations by considering the interrelationships between agents. For example, it can prioritize recommending related agents based on their past transaction data. It can also prioritize recommending highly reliable agents based on user reviews. Furthermore, it can prioritize recommending agents that match the user's desired conditions based on their transaction history. In this way, considering the interrelationships between agents improves the accuracy of recommendations.

[0072] The proposal department can apply different proposal methods depending on the property category. For example, for residential properties, a proposal method that reflects the latest market trends can be applied. For commercial properties, a proposal method that takes into account the surrounding business environment can be applied. Furthermore, for investment properties, a proposal method that emphasizes profitability can be applied. By applying different proposal methods to each property category, more appropriate proposals can be provided.

[0073] The following briefly describes the processing flow for example form 1.

[0074] Step 1: The reception desk enters the user's desired conditions. These conditions include price, location, floor plan, and age of the building. For example, the user can enter their desired price range, location, floor plan, and age of the building. Step 2: The update department updates the property list based on the information received by the reception department. The update department can update the property list in real time, register and delete properties, and perform periodic information updates. Step 3: The matching unit matches the user with the most suitable property based on the property list updated by the update unit. The matching unit can select properties based on the degree of match with the user's desired conditions, the property's evaluation score, and the user's past search history. Step 4: The analysis department analyzes the agent's past transaction data and user reviews. The analysis department can assess the agent's reliability based on their transaction performance, user review rating scores, and past transaction history. Step 5: The recommendation department recommends highly reliable agents based on the data analyzed by the analysis department. The recommendation department can personalize and recommend agents that match the user's preferences, and can make recommendations based on agent evaluation scores and transaction history. Step 6: The proposal department analyzes market trends and proposes the optimal timing for buying and selling. The proposal department can analyze market trends based on price fluctuations, transaction volume trends, and historical market data.

[0075] (Example of form 2) An embodiment of the present invention provides a real estate matching system that automatically matches the user with the most suitable property based on their desired conditions and recommends a highly reliable real estate agent. In this system, the user inputs their desired conditions, and the AI ​​matches the user with the most suitable property based on those conditions. The AI ​​updates the property list in real time and recommends properties to the user. Furthermore, the AI ​​analyzes the agent's past transaction data and user reviews to recommend a highly reliable agent. The AI ​​also analyzes market trends and proposes the optimal timing for buying or selling. For example, the real estate matching system includes a reception unit where the user inputs their desired conditions. The reception unit allows the user to input conditions such as price, location, floor plan, and year of construction. Next, the update unit updates the property list based on the information received by the reception unit. The update unit updates the property list in real time, providing the latest property information. Furthermore, the matching unit matches the user with the most suitable property based on the property list updated by the update unit. The matching unit selects the most suitable property based on factors such as the degree of match with the user's desired conditions and the property's evaluation score. Next, the analysis unit analyzes the agent's past transaction data and user reviews. The analysis unit evaluates the reliability of the agents and identifies highly reliable agents. Next, the Recommendation Department recommends highly reliable agents based on data analyzed by the Analysis Department. The Recommendation Department personalizes and recommends agents that match the user's desired conditions. Finally, the Proposal Department analyzes market trends and proposes the optimal timing for buying and selling. The Proposal Department analyzes market trends based on price fluctuations and transaction volume trends and proposes the optimal timing for the user. As a result, the real estate matching system can efficiently find properties that meet the user's desired conditions and easily find a reliable real estate agent. It can also significantly reduce the time and effort required for property search and agent selection. In this way, the real estate matching system can automatically match the best properties based on the user's desired conditions and recommend highly reliable real estate agents.

[0076] The real estate matching system according to this embodiment comprises a reception unit, an update unit, a matching unit, an analysis unit, a recommendation unit, and a proposal unit. The reception unit receives input from the user regarding their desired conditions. These conditions include, but are not limited to, price, location, floor plan, and age of the building. For example, the reception unit can receive input from the user regarding their desired price range. The reception unit can also receive input from the user regarding their desired location. Furthermore, the reception unit can receive input from the user regarding their desired floor plan and age of the building. The update unit updates the property list based on the information received by the reception unit. For example, the update unit can update the property list in real time. The update unit can also register and delete properties. Furthermore, the update unit can periodically update property information. The matching unit matches the user with the most suitable property based on the property list updated by the update unit. For example, the matching unit can select a property based on its degree of match with the user's desired conditions. The matching unit can also select a property based on its evaluation score. Furthermore, the matching unit can also select a property based on the user's past search history. The Analysis Department analyzes agents' past transaction data and user reviews. For example, the Analysis Department can evaluate reliability based on an agent's transaction history. It can also evaluate reliability based on user review scores. Furthermore, the Analysis Department can evaluate reliability based on an agent's past transaction history. The Recommendation Department recommends highly reliable agents based on the data analyzed by the Analysis Department. For example, the Recommendation Department can personalize and recommend agents that match the user's desired conditions. It can also recommend agents based on their evaluation scores. Furthermore, the Recommendation Department can recommend agents based on their transaction history. The Proposal Department analyzes market trends and proposes the optimal timing for buying and selling. For example, the Proposal Department can analyze market trends based on price fluctuations. It can also analyze market trends based on the trend in the number of transactions. Furthermore, the Proposal Department can analyze market trends based on historical market data.As a result, the real estate matching system according to this embodiment can automatically match the user with the most suitable property based on their desired conditions and recommend a highly reliable real estate agent.

[0077] The reception desk allows users to input their desired criteria. These criteria may include, but are not limited to, price, location, floor plan, and age of the building. For example, the reception desk can allow users to input their desired price range, location, floor plan, and age of the building. Specifically, the reception desk is designed to allow users to easily input their desired criteria through a user interface. For example, users can select a price range using dropdown menus or sliders, or specify a desired location on a map. Detailed conditions for floor plan and age of the building can also be set using checkboxes or text input fields. Furthermore, the reception desk has a function to save the user's entered criteria for later reuse, saving users the trouble of re-entering criteria. The reception desk can also provide real-time feedback based on the user's entered criteria. For example, it can instantly display the number of properties matching the entered criteria and a list of properties that meet the criteria. This allows users to adjust their criteria while checking how realistic their preferences are. Furthermore, the reception desk can provide relevant advice and suggestions based on the conditions entered by the user. For example, if the desired price range differs significantly from the average market price, it can explain the reasons and suggest alternatives. This allows users to set more realistic conditions and helps them find the best property.

[0078] The update department updates the property list based on information received by the reception department. The update department can, for example, update the property list in real time. It can also register and delete properties. Furthermore, the update department can periodically update property information. Specifically, the update department works with the database to retrieve the latest property information and provide it to users. For example, it immediately reflects new property information provided by real estate agents and property owners, ensuring users always have access to the most up-to-date information. It also quickly reflects information when a property is sold or contracted, removing it from the list to provide users with accurate information. Furthermore, the update department maintains the freshness of information by regularly updating property information. For example, it periodically checks the database and makes necessary updates to reflect the latest status, such as price changes or renovation information. The update department can also verify and cross-check data to ensure the reliability of property information. This allows users to use property information with confidence. Additionally, the update department can improve property information based on user feedback. For example, it can correct incorrect information or provide additional information based on user reports. This allows the update department to consistently provide accurate and reliable property information, thereby improving user satisfaction.

[0079] The matching unit matches the user with the most suitable property based on the property list updated by the update unit. For example, the matching unit can select properties based on how well they match the user's desired conditions. It can also select properties based on their evaluation score. Furthermore, the matching unit can select properties based on the user's past search history. Specifically, the matching unit uses an AI algorithm to compare the user's desired conditions with property information and lists properties with a high degree of match. For example, it comprehensively evaluates conditions such as price, location, floor plan, and age of the building to select the most suitable property. In addition, since the property evaluation score is calculated based on past user reviews and transaction history, it can prioritize recommending highly reliable properties. Furthermore, the matching unit analyzes the user's past search and browsing history to understand their preferences and tendencies. This allows it to predict properties that the user might be interested in and provide personalized recommendations. For example, for users who have frequently searched for properties in a specific area or price range in the past, it will prioritize displaying new properties that match that area or price range. The matching unit can also continuously improve its recommendation algorithm based on user feedback. For example, if a user gives a high rating to a recommended property, that rating is reflected in the algorithm to improve the accuracy of future recommendations. This allows the matching system to quickly and accurately provide users with the most suitable properties, thereby increasing their satisfaction.

[0080] The analytics department analyzes agents' past transaction data and user reviews. For example, the analytics department can assess reliability based on an agent's transaction history. It can also assess reliability based on user review scores. Furthermore, it can assess reliability based on an agent's past transaction history. Specifically, the analytics department uses data mining techniques to analyze agents' transaction data and user reviews in detail. For example, it evaluates the number of properties an agent has handled in the past, their closing rate, and the speed of transactions to identify highly reliable agents. User review scores reflect the quality of the agent's service and customer satisfaction, and can be used to assess an agent's reliability. Furthermore, the analytics department can evaluate an agent's expertise in specific areas or property types based on their past transaction history. This allows users to find the agent best suited to their needs. Based on this data, the analytics department quantitatively evaluates agent performance and provides foundational information for recommending highly reliable agents. The analytics department also continuously monitors agent performance and updates evaluations regularly. This ensures users always select agents based on the latest information. Furthermore, the analytics department can revise its evaluation criteria based on user feedback, enabling more accurate evaluations. This allows the analytics department to provide users with reliable agents and improve their satisfaction.

[0081] The recommendation department recommends highly reliable agents based on data analyzed by the analytics department. For example, the recommendation department can personalize recommendations to match the user's desired criteria. It can also recommend agents based on their evaluation scores. Furthermore, it can recommend agents based on their transaction history. Specifically, the recommendation department uses AI to analyze data provided by the analytics department and identify the agent best suited to the user's desired criteria. For example, it prioritizes recommending agents who are familiar with the user's desired area and property type. Additionally, since agent evaluation scores are calculated based on past transaction history and user reviews, it can recommend highly reliable agents. Furthermore, the recommendation department analyzes agents' transaction history in detail to evaluate their expertise in specific areas and property types. This allows users to find the agent best suited to their desired criteria. The recommendation department can also continuously improve its recommendation algorithm based on user feedback. For example, if a user gives a high rating to a recommended agent, that rating is reflected in the algorithm to improve future recommendation accuracy. Furthermore, the recommendation team can analyze users' search and browsing history to understand their preferences and trends, enabling them to provide more personalized recommendations. This allows the recommendation team to quickly and accurately provide users with the most suitable agents, thereby increasing user satisfaction.

[0082] The proposal department analyzes market trends and suggests the optimal timing for buying and selling. For example, it can analyze market trends based on price fluctuations. It can also analyze market trends based on the number of transactions. Furthermore, it can analyze market trends based on historical market data. Specifically, the proposal department uses AI to analyze historical market data and current market conditions in detail to predict future price fluctuations and transaction volume trends. For example, based on price fluctuation data from the past few years, it predicts future price increases or decreases and suggests the optimal timing for buying and selling to the user. By analyzing the number of transactions, it can grasp market activity and demand fluctuations and provide appropriate advice to the user. Furthermore, the proposal department can make more accurate market forecasts by considering regional market characteristics and economic indicators. This allows users to accurately grasp market trends and trade at the optimal time. The proposal department can also continuously improve its suggestions based on user feedback. For example, it analyzes the results of transactions made by users based on the suggestions and reflects these results in the algorithm to improve the accuracy of future suggestions. Furthermore, the proposal department can provide individually customized proposals, taking into account the user's desired conditions and transaction history. This allows the proposal department to offer users the optimal timing for buying and selling, thereby increasing their satisfaction.

[0083] The learning unit learns the user's desired conditions. The learning unit can learn the user's desired conditions using, for example, a machine learning algorithm. The learning unit can also learn desired conditions based on the user's past search history. Furthermore, the learning unit can learn desired conditions based on the user's input data. For example, the learning unit can analyze the desired conditions the user has entered in the past and learn the user's preferences. The learning unit can learn the characteristics of properties the user has searched for in the past and estimate the user's desired conditions. Furthermore, the learning unit can learn the characteristics of properties the user likes based on the user's input data. Furthermore, the learning unit can learn the characteristics of properties the user wants to avoid based on the user's past search history. As a result, by learning the user's desired conditions, the learning unit can achieve more accurate property matching. Some or all of the above processing in the learning unit may be performed using, for example, AI, or not using AI. For example, the learning unit can input the user's past search history into a generating AI and have the generating AI perform the learning of the user's desired conditions.

[0084] The update unit can update the property list in real time. For example, the update unit can register and delete properties in real time. The update unit can also update property information in real time. Furthermore, the update unit can update the status of properties in real time. For example, the update unit can register new properties in real time and reflect them in the property list. The update unit can also delete properties in real time and remove them from the property list. Furthermore, the update unit can update property information in real time to provide the latest information. As a result, by updating the property list in real time, the latest property information can be provided. Some or all of the above processes in the update unit may be performed using AI, for example, or not using AI. For example, the update unit can input information on new property registration and deletion into a generating AI and have the generating AI perform the property list update.

[0085] The analysis department can analyze agents' past transaction data and user reviews. For example, the analysis department can evaluate reliability based on an agent's trading performance. It can also evaluate reliability based on user review scores. Furthermore, it can evaluate reliability based on an agent's past transaction history. For example, the analysis department can analyze an agent's trading performance to identify highly reliable agents. It can also analyze user review scores to identify highly reliable agents. Furthermore, it can analyze an agent's past transaction history to identify highly reliable agents. This allows the analysis department to recommend highly reliable agents by analyzing their past transaction data and user reviews. Some or all of the above processing in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can input agent trading performance and user review data into a generating AI and have the generating AI perform the reliability evaluation.

[0086] The recommendation system can personalize and recommend highly reliable agents. For example, the recommendation system can personalize and recommend agents that match the user's desired conditions. It can also recommend agents based on their evaluation scores. Furthermore, it can recommend agents based on their transaction history. For example, the recommendation system personalizes and recommends agents based on the user's desired conditions. It can also recommend highly reliable agents based on their evaluation scores. Furthermore, it can recommend highly reliable agents based on their transaction history. This allows the system to provide the user with the most suitable agent by personalizing and recommending highly reliable agents. Some or all of the above processes in the recommendation system may be performed using AI, for example, or without AI. For example, the recommendation system can input the user's desired conditions and agent evaluation scores into a generating AI and have the generating AI perform agent recommendations.

[0087] The proposal unit can analyze market trends and propose the optimal timing for buying and selling. For example, the proposal unit can analyze market trends based on price fluctuations. It can also analyze market trends based on the trend in the number of transactions. Furthermore, the proposal unit can analyze market trends based on historical market data. For example, the proposal unit can analyze price fluctuations and propose the optimal timing for buying and selling. It can also analyze the trend in the number of transactions and propose the optimal timing for buying and selling. Furthermore, the proposal unit can analyze historical market data and propose the optimal timing for buying and selling. In this way, by analyzing market trends and proposing the optimal timing for buying and selling, it can support optimal transactions for users. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input price fluctuation and transaction volume data into a generating AI and have the generating AI perform market trend analysis.

[0088] The reception system can estimate the user's emotions and adjust the input method for desired conditions based on the estimated emotions. For example, if the user is stressed, the reception system can provide a simple interface and minimize the input steps. If the user is relaxed, the reception system can also provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception system can prioritize voice input to allow for quick input of desired conditions. For example, the reception system can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. It can also record the user's voice and estimate their emotions using voice analysis technology. In addition, the reception system can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows for a more appropriate input method by adjusting the input method for desired conditions according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes at the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input user image data captured by a camera into a generating AI and have the generating AI perform an estimation of the user's emotions.

[0089] The reception desk can analyze the user's past history of entering desired conditions and select the optimal input method. For example, the reception desk can automatically display desired conditions that the user has frequently entered in the past as candidates. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest desired conditions to be used during specific time periods based on the user's past input history. For example, the reception desk can analyze the user's past history of entering desired conditions and identify the input method the user prefers. The reception desk can also identify input methods that the user wants to avoid based on the user's past input history. Furthermore, the reception desk can predict and suggest desired conditions to be used during specific time periods based on the user's past input history. In this way, by analyzing the user's past history of entering desired conditions, the optimal input method can be provided. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past history of entering desired conditions into a generating AI and have the generating AI select the optimal input method.

[0090] The reception desk can filter the user's current living situation and areas of interest when they input their desired conditions. For example, if the user enters their family structure, the reception desk will prioritize displaying properties suitable for families. It can also prioritize displaying pet-friendly properties if the user owns pets. Furthermore, if the user enters their hobbies or areas of interest, the reception desk can prioritize displaying properties related to those interests. For example, the reception desk can filter properties suitable for families based on the user's family structure. It can also filter properties that allow pets if the user owns pets. Furthermore, it can filter properties related to the user's hobbies and areas of interest. This allows the reception desk to provide more appropriate properties by filtering based on the user's current living situation and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input data on the user's living situation and areas of interest into a generating AI and have the generating AI perform the filtering.

[0091] The reception desk can estimate the user's emotions and prioritize the desired conditions to be entered based on those emotions. For example, if the user is stressed, the reception desk can prioritize the input of important desired conditions. If the user is relaxed, the reception desk can also allow the user to enter detailed desired conditions. Furthermore, if the user is in a hurry, the reception desk can allow the user to enter only the most important desired conditions. For example, the reception desk can capture the user's facial expression with a camera and estimate their emotions using an emotion estimation algorithm. The reception desk can also record the user's voice and estimate their emotions using voice analysis technology. In addition, the reception desk can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the system to provide more suitable properties by prioritizing desired conditions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes at the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input user image data captured by a camera into a generating AI and have the generating AI perform an estimation of the user's emotions.

[0092] The reception desk can prioritize inputting highly relevant conditions when users enter their desired conditions, taking into account their geographical location. For example, if a user enters their current location, the reception desk will prioritize displaying properties in that area. Furthermore, if a user specifies a particular region, the reception desk can prioritize displaying properties in that region. Additionally, if a user is on the move, the reception desk can suggest the most suitable properties based on their current location. For example, the reception desk can filter properties in the vicinity based on the user's current location. Furthermore, if a user specifies a particular region, the reception desk can filter properties in that region. Additionally, if a user is on the move, the reception desk can suggest the most suitable properties based on their current location. This allows the reception desk to provide more appropriate properties by prioritizing the input of highly relevant conditions, taking into account the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's geographical location information into a generating AI and have the generating AI perform the filtering of highly relevant conditions.

[0093] The reception desk can analyze the user's social media activity when they input their desired conditions and input relevant conditions. For example, the reception desk can suggest relevant properties based on places the user has shared on social media. It can also analyze the content of the user's social media posts and suggest properties based on their interests. Furthermore, the reception desk can suggest relevant properties based on the areas where the user's social media friends live. For example, the reception desk can filter relevant properties based on places the user has shared on social media. It can also analyze the content of the user's social media posts and filter properties based on their interests. Furthermore, the reception desk can filter relevant properties based on the areas where the user's social media friends live. This allows the reception desk to provide more appropriate properties by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input data on the user's social media activity into a generating AI and have the generating AI perform filtering of relevant conditions.

[0094] The update unit can estimate the user's emotions and adjust the frequency of property listing updates based on the estimated emotions. For example, if the user is stressed, the update unit can reduce the update frequency and display only important properties. Conversely, if the user is relaxed, the update unit can increase the update frequency and display more properties. Furthermore, if the user is in a hurry, the update unit can update the property listing in real time to provide information quickly. For example, the update unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. It can also record the user's voice and estimate their emotions using voice analysis technology. In addition, the update unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows for the provision of more appropriate property information by adjusting the property listing update frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, 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 update unit may be performed using AI, for example, or without AI. For example, the update unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0095] The update unit can select the optimal update method by referring to past update history when updating the property list. For example, the update unit can analyze past update history to understand the types of properties users prefer and update accordingly. The update unit can also prioritize displaying properties that users have shown interest in based on past update history. Furthermore, the update unit can exclude properties that users want to avoid based on past update history. For example, the update unit can analyze past update history to understand the types of properties users prefer. The update unit can also prioritize displaying properties that users have shown interest in based on past update history. Furthermore, the update unit can exclude properties that users want to avoid based on past update history. This allows the update unit to provide the optimal method for updating the property list by referring to past update history. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input past update history data into a generating AI and have the generating AI select the optimal update method.

[0096] The update unit can apply different update algorithms depending on the property category when updating the property list. For example, for residential properties, the update unit applies an update algorithm that reflects the latest market trends. For commercial properties, the update unit can also apply an update algorithm that takes into account the surrounding business environment. Furthermore, for investment properties, the update unit can also apply an update algorithm that prioritizes profitability. By applying different update algorithms depending on the property category, more appropriate property information can be provided. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input data corresponding to the property category into a generating AI and have the generating AI execute the application of the update algorithm.

[0097] The update unit can estimate the user's emotions and adjust the display method of the property list based on the estimated emotions. For example, if the user is stressed, the update unit can provide a simple and highly visible display method. If the user is relaxed, the update unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the update unit can provide a concise display method. For example, the update unit can capture the user's facial expression with a camera and estimate their emotions using an emotion estimation algorithm. The update unit can also record the user's voice and estimate their emotions using voice analysis technology. Furthermore, the update unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the system to provide more appropriate property information by adjusting the display method of the property list according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, 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 update unit may be performed using AI, for example, or without AI. For example, the update unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0098] The update unit can update the property list while considering the geographical distribution of properties. For example, if the user specifies a particular region, the update unit will prioritize updating properties in that region. Furthermore, if the user specifies a wide area, the update unit can update properties in a balanced manner while considering geographical distribution. Additionally, if the user is on the move, the update unit can update the most suitable properties based on their current location. This allows for the provision of more appropriate property information by considering the geographical distribution of properties during updates. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input data on the geographical distribution of properties into a generating AI and have the generating AI perform the update.

[0099] The update unit can improve the accuracy of updates by referring to relevant literature for properties when updating the property list. For example, the update unit can refer to relevant literature for properties and perform updates that reflect the latest market trends. The update unit can also prioritize updating properties that users are likely to be interested in based on the relevant literature. Furthermore, the update unit can analyze the relevant literature for properties and exclude properties that users want to avoid when updating. For example, the update unit can refer to relevant literature for properties and perform updates that reflect the latest market trends. The update unit can also prioritize updating properties that users are likely to be interested in based on the relevant literature for properties. Furthermore, the update unit can analyze the relevant literature for properties and exclude properties that users want to avoid when updating. This improves the accuracy of updates by referring to relevant literature for properties. Some or all of the above processing in the update unit may be performed using AI, for example, or not using AI. For example, the update unit can input data on relevant literature for properties into a generating AI and have the generating AI perform the improvement of update accuracy.

[0100] The matching unit can estimate the user's emotions and adjust the matching criteria based on the estimated emotions. For example, if the user is stressed, the matching unit can apply simple matching criteria. If the user is relaxed, the matching unit can also apply more detailed matching criteria. Furthermore, if the user is in a hurry, the matching unit can apply criteria for rapid matching. For example, the matching unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The matching unit can also record the user's voice and estimate their emotions using voice analysis technology. In addition, the matching unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the system to provide more appropriate properties by adjusting the matching criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, 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 matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0101] The matching unit can improve the accuracy of matching by considering the interrelationships between properties during the matching process. For example, the matching unit can consider the surrounding environment of a property and prioritize matching properties that are related to it. It can also consider the price range of properties and prioritize matching properties in the same price range. Furthermore, the matching unit can consider the facilities and conditions of properties and prioritize matching properties that meet the user's preferences. For example, the matching unit can consider the surrounding environment of a property and prioritize matching properties that are related to it. It can also consider the price range of properties and prioritize matching properties in the same price range. Furthermore, the matching unit can consider the facilities and conditions of properties and prioritize matching properties that meet the user's preferences. This improves the accuracy of matching by considering the interrelationships between properties. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input data on the interrelationships between properties into a generating AI and have the generating AI perform the matching accuracy improvement.

[0102] The matching unit can perform matching while considering the attribute information of the property submitter. For example, if the property submitter is a highly reliable agent, the matching unit will prioritize matching with that property. The matching unit can also prioritize matching with properties submitted by agents who have received high ratings in the past. Furthermore, the matching unit can also prioritize matching with properties submitted by agents who have attributes that match the user's desired conditions. For example, if the property submitter is a highly reliable agent, the matching unit will prioritize matching with that property. The matching unit can also prioritize matching with properties submitted by agents who have received high ratings in the past. Furthermore, the matching unit can also prioritize matching with properties submitted by agents who have attributes that match the user's desired conditions. This allows for the provision of more reliable properties by considering the attribute information of the property submitter. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input the attribute information of the property submitter into a generating AI and have the generating AI perform the matching.

[0103] The matching unit can estimate the user's emotions and adjust the order in which matching results are displayed based on the estimated emotions. For example, if the user is feeling stressed, the matching unit may prioritize displaying important properties. If the user is relaxed, the matching unit may also prioritize displaying properties with detailed information. Furthermore, if the user is in a hurry, the matching unit may prioritize displaying properties that can be quickly reviewed. For example, the matching unit may capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. It may also record the user's voice and estimate their emotions using voice analysis technology. Additionally, the matching unit may collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the system to provide more appropriate properties by adjusting the order in which matching results are displayed 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 include, 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 matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0104] The matching unit can perform matching while considering the geographical distribution of properties. For example, if the user specifies a particular area, the matching unit will prioritize matching properties in that area. Furthermore, if the user specifies a wide area, the matching unit can also match properties in a balanced manner while considering geographical distribution. Additionally, if the user is on the move, the matching unit can match the most suitable property based on their current location. This allows for the provision of more appropriate properties by considering the geographical distribution of properties during the matching process. Some or all of the above-described processes in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input data on the geographical distribution of properties into a generating AI and have the generating AI perform the matching.

[0105] The matching unit can improve the accuracy of matching by referring to relevant literature on properties during the matching process. For example, the matching unit can refer to relevant literature on properties to perform matching that reflects the latest market trends. The matching unit can also prioritize matching properties that are likely to interest the user based on the relevant literature. Furthermore, the matching unit can analyze the relevant literature on properties and exclude properties that the user wants to avoid during the matching process. For example, the matching unit can refer to relevant literature on properties to perform matching that reflects the latest market trends. The matching unit can also prioritize matching properties that are likely to interest the user based on the relevant literature. Furthermore, the matching unit can analyze the relevant literature on properties and exclude properties that the user wants to avoid during the matching process. This improves the accuracy of matching by referring to relevant literature on properties. Some or all of the above processing in the matching unit may be performed using AI, for example, or not. For example, the matching unit can input data on relevant literature on properties into a generating AI and have the generating AI perform the matching accuracy improvement.

[0106] The analysis unit can estimate the user's emotions and adjust the display method of the analysis 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. For example, the analysis unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The analysis unit can also record the user's voice and estimate their emotions using voice analysis technology. Furthermore, the analysis unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the system to provide more appropriate information by adjusting the display method of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, 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, for example, or without AI. For example, the analysis unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0107] The analysis unit can optimize the current analysis by referring to past analysis data during the analysis process. For example, the analysis unit can prioritize displaying agents that are likely to interest the user based on past analysis data. The analysis unit can also exclude agents that the user wants to avoid from past analysis data. Furthermore, the analysis unit can prioritize displaying agents that match the user's desired conditions by referring to past analysis data. For example, the analysis unit can prioritize displaying agents that are likely to interest the user based on past analysis data. The analysis unit can also exclude agents that the user wants to avoid from past analysis data. Furthermore, the analysis unit can prioritize displaying agents that match the user's desired conditions by referring to past analysis data. In this way, the current analysis can be optimized by referring to past analysis data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past analysis data into a generating AI and have the generating AI perform the optimization of the current analysis.

[0108] The analysis unit can apply different analytical methods to each agent category during analysis. For example, in the case of residential agents, the analysis unit can apply an analytical method that emphasizes past transaction data. In the case of commercial agents, the analysis unit can also apply an analytical method that takes the business environment into consideration. Furthermore, in the case of investment agents, the analysis unit can also apply an analytical method that emphasizes profitability. By applying different analytical methods to each agent category, more appropriate information can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data for each agent category into a generating AI and have the generating AI execute the application of analytical methods.

[0109] The analysis unit can estimate the user's emotions and adjust the importance of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit will prioritize displaying important analysis results. If the user is relaxed, the analysis unit can also display detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can display concise analysis results. For example, the analysis unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. It can also record the user's voice and estimate their emotions using voice analysis technology. Additionally, the analysis unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows for the provision of more appropriate information by adjusting the importance of the analysis 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 includes, 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, for example, or without AI. For example, the analysis unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0110] The analysis unit can analyze changes in the analysis based on the agent's trading period during the analysis. For example, the analysis unit can analyze seasonal trading trends based on the agent's trading period. The analysis unit can also prioritize displaying agents that perform well during specific periods based on the agent's trading period. Furthermore, the analysis unit can analyze peak trading periods based on the agent's trading period. For example, the analysis unit can analyze seasonal trading trends based on the agent's trading period. The analysis unit can also prioritize displaying agents that perform well during specific periods based on the agent's trading period. Furthermore, the analysis unit can analyze peak trading periods based on the agent's trading period. This allows for the provision of more appropriate information by analyzing changes in the analysis based on the agent's trading period. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input agent trading period data into a generating AI and have the generating AI execute the changes in the analysis.

[0111] The analysis unit can perform analysis by referring to the relevant market data of agents. For example, the analysis unit can perform analysis that reflects the latest market trends based on the relevant market data of agents. The analysis unit can also prioritize displaying agents that are likely to interest the user based on the relevant market data of agents. Furthermore, the analysis unit can analyze the relevant market data of agents and exclude agents that the user wants to avoid. For example, the analysis unit can perform analysis that reflects the latest market trends based on the relevant market data of agents. The analysis unit can also prioritize displaying agents that are likely to interest the user based on the relevant market data of agents. Furthermore, the analysis unit can analyze the relevant market data of agents and exclude agents that the user wants to avoid. This allows for the provision of more appropriate information by referring to the relevant market data of agents. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the relevant market data of agents into a generating AI and have the generating AI perform the analysis.

[0112] The recommendation system can estimate the user's emotions and prioritize which agents to recommend based on those emotions. For example, if the user is stressed, the recommendation system will prioritize recommending a highly reliable agent. If the user is relaxed, the recommendation system can also recommend an agent with detailed information. Furthermore, if the user is in a hurry, the recommendation system can prioritize recommending an agent that can respond quickly. For example, the recommendation system can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. It can also record the user's voice and estimate their emotions using voice analysis technology. In addition, the recommendation system can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the system to provide more appropriate agents by prioritizing which agents to recommend according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, 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 recommendation department may be performed using AI, for example, or without AI. For example, the recommendation department can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0113] The recommendation system can improve the accuracy of recommendations by considering the interrelationships between agents. For example, the recommendation system can prioritize recommending interrelated agents based on their past transaction data. It can also prioritize recommending highly reliable agents based on user reviews of agents. Furthermore, it can prioritize recommending agents that match the user's desired conditions based on their transaction history. By considering the interrelationships between agents, the recommendation system improves the accuracy of recommendations. Some or all of the above processing in the recommendation system may be performed using AI, for example, or without AI. For example, the recommendation system can input data on the interrelationships between agents into a generating AI and have the generating AI perform the recommendation accuracy improvement.

[0114] The recommendation department can make recommendations by considering the attribute information of agents. For example, the recommendation department can recommend agents that match the user's desired conditions based on the agent's area of ​​expertise. The recommendation department can also recommend highly reliable agents based on the agent's past transaction record. Furthermore, the recommendation department can recommend highly rated agents based on the agent's user reviews. For example, the recommendation department can recommend agents that match the user's desired conditions based on the agent's area of ​​expertise. The recommendation department can also recommend highly reliable agents based on the agent's past transaction record. Furthermore, the recommendation department can recommend highly rated agents based on the agent's user reviews. This allows for the provision of more appropriate agents by considering the attribute information of agents. Some or all of the above processing in the recommendation department may be performed using AI, for example, or not using AI. For example, the recommendation department can input agent attribute information data into a generating AI and have the generating AI perform the recommendations.

[0115] The recommendation system can estimate the user's emotions and adjust how recommended agents are displayed based on those emotions. For example, if the user is stressed, the recommendation system can provide a simple and highly visible display. If the user is relaxed, it can provide a display that includes detailed information. Furthermore, if the user is in a hurry, it can provide a concise display. For example, the recommendation system can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. It can also record the user's voice and estimate their emotions using voice analysis technology. In addition, the recommendation system can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the system to provide more appropriate agents by adjusting how recommended agents are displayed according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, 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 recommendation department may be performed using AI, for example, or without AI. For example, the recommendation department can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0116] The recommendation system can make recommendations while considering the geographical distribution of agents. For example, if a user specifies a particular region, the recommendation system will prioritize recommending agents in that region. Furthermore, if a user specifies a wide area, the recommendation system can recommend agents in a balanced manner while considering geographical distribution. Additionally, if a user is on the move, the recommendation system can recommend the most suitable agent based on their current location. This allows for the provision of more appropriate agents by considering the geographical distribution of agents. Some or all of the above processing in the recommendation system may be performed using AI, for example, or without AI. For example, the recommendation system can input data on the geographical distribution of agents into a generating AI and have the generating AI perform the recommendations.

[0117] The recommendation system can improve the accuracy of recommendations by referring to the agent's relevant literature during the recommendation process. For example, the recommendation system can refer to the agent's relevant literature to make recommendations that reflect the latest market trends. Furthermore, based on the agent's relevant literature, the recommendation system can prioritize recommending agents that the user is likely to be interested in. In addition, the recommendation system can analyze the agent's relevant literature and exclude agents that the user wants to avoid. This improves the accuracy of recommendations by referring to the agent's relevant literature. Some or all of the above processing in the recommendation system may be performed using AI, for example, or without AI. For example, the recommendation system can input data on the agent's relevant literature into a generating AI and have the generating AI perform the recommendation accuracy improvement.

[0118] The suggestion unit can estimate the user's emotions and adjust its suggestion methods based on those emotions. For example, if the user is stressed, the suggestion unit can provide simple and highly visible suggestions. If the user is relaxed, it can provide suggestions that include more detailed information. Furthermore, if the user is in a hurry, it can provide concise suggestions. For example, the suggestion unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. It can also record the user's voice and estimate their emotions using voice analysis technology. In addition, the suggestion unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the suggestion unit to provide more appropriate suggestions by adjusting its methods according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, 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 proposed unit may be performed using AI, for example, or without AI. For example, the proposed unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0119] The proposal unit can optimize its proposal algorithm by referring to past proposal data when making a proposal. For example, the proposal unit can prioritize displaying proposals that are likely to interest the user based on past proposal data. It can also exclude proposals that the user wants to avoid from past proposal data. Furthermore, the proposal unit can prioritize displaying proposals that match the user's desired conditions by referring to past proposal data. For example, the proposal unit can prioritize displaying proposals that are likely to interest the user based on past proposal data. It can also exclude proposals that the user wants to avoid from past proposal data. Furthermore, the proposal unit can prioritize displaying proposals that match the user's desired conditions by referring to past proposal data. In this way, the proposal algorithm can be optimized by referring to past proposal data. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input past proposal data into a generation AI and have the generation AI perform the optimization of the proposal algorithm.

[0120] The proposal unit can apply different proposal methods to each property category when making proposals. For example, in the case of residential properties, the proposal unit can apply a proposal method that reflects the latest market trends. In the case of commercial properties, the proposal unit can also apply a proposal method that takes into account the surrounding business environment. Furthermore, in the case of investment properties, the proposal unit can also apply a proposal method that emphasizes profitability. By applying different proposal methods to each property category, the proposal unit can provide more appropriate proposals. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not. For example, the proposal unit can input data for each property category into a generating AI and have the generating AI execute the application of proposal methods.

[0121] The suggestion unit can estimate the user's emotions and prioritize suggestions based on those emotions. For example, if the user is stressed, the suggestion unit will prioritize displaying important suggestions. If the user is relaxed, the suggestion unit can also display more detailed suggestions. Furthermore, if the user is in a hurry, the suggestion unit can prioritize suggestions that can be quickly reviewed. For example, the suggestion unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. It can also record the user's voice and estimate their emotions using voice analysis technology. Additionally, the suggestion unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the system to provide more appropriate suggestions by prioritizing them 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 includes, 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 proposed unit may be performed using AI, for example, or without AI. For example, the proposed unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0122] The proposal department can analyze changes in proposals based on the property submission timing. For example, the proposal department can analyze seasonal proposal trends based on the property submission timing. The proposal department can also prioritize displaying proposals that are strong during specific periods based on the property submission timing. Furthermore, the proposal department can analyze peak periods for proposals based on the property submission timing. By analyzing changes in proposals based on the property submission timing, more appropriate proposals can be provided. Some or all of the above processing in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input property submission timing data into a generation AI and have the generation AI execute the changes in proposals.

[0123] The proposal unit can make proposals by referring to relevant market data for the property. For example, the proposal unit can make proposals that reflect the latest market trends based on the relevant market data for the property. The proposal unit can also prioritize displaying proposals that are likely to interest the user based on the relevant market data for the property. Furthermore, the proposal unit can analyze the relevant market data for the property and exclude proposals that the user would like to avoid. For example, the proposal unit can make proposals that reflect the latest market trends based on the relevant market data for the property. Furthermore, the proposal unit can prioritize displaying proposals that are likely to interest the user based on the relevant market data for the property. Furthermore, the proposal unit can analyze the relevant market data for the property and exclude proposals that the user would like to avoid. This allows for the provision of more appropriate proposals by referring to relevant market data for the property. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input relevant market data for the property into a generating AI and have the generating AI execute the proposals.

[0124] The learning unit can estimate the user's emotions and select training data based on the estimated emotions. For example, if the user is stressed, the learning unit will select simple training data. If the user is relaxed, the learning unit can also select detailed training data. Furthermore, if the user is in a hurry, the learning unit can select data that allows for rapid learning. For example, the learning unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The learning unit can also record the user's voice and estimate their emotions using voice analysis technology. In addition, the learning unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows for more appropriate learning by selecting training data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0125] The learning unit can optimize its learning algorithm by referring to past learning data during the learning process. For example, the learning unit can prioritize learning properties that users are likely to be interested in based on past learning data. The learning unit can also exclude properties that users want to avoid from past learning data. Furthermore, the learning unit can refer to past learning data and prioritize learning properties that match the user's desired conditions. For example, the learning unit can prioritize learning properties that users are likely to be interested in based on past learning data. The learning unit can also exclude properties that users want to avoid from past learning data. Furthermore, the learning unit can refer to past learning data and prioritize learning properties that match the user's desired conditions. This allows the learning algorithm to be optimized by referring to past learning data. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input past learning data into a generating AI and have the generating AI perform the optimization of the learning algorithm.

[0126] The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated emotions. For example, if the user is stressed, the learning unit can reduce the learning frequency and learn only important data. Conversely, if the user is relaxed, the learning unit can increase the learning frequency and learn more data. Furthermore, if the user is in a hurry, the learning unit can prioritize learning data that can be learned quickly. For example, the learning unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. It can also record the user's voice and estimate their emotions using voice analysis technology. In addition, the learning unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows for more appropriate learning by adjusting the learning frequency according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0127] The learning unit can weight the training data based on the property submission date during training. For example, the learning unit can prioritize learning the most recent data based on the property submission date. The learning unit can also prioritize learning data that is strong during specific periods based on the property submission date. Furthermore, the learning unit can analyze and weight the peak periods of training based on the property submission date. For example, the learning unit can prioritize learning the most recent data based on the property submission date. Furthermore, the learning unit can also prioritize learning data that is strong during specific periods based on the property submission date. Furthermore, the learning unit can analyze and weight the peak periods of training based on the property submission date. This allows for more appropriate training by weighting the training data based on the property submission date. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input property submission date data into a generating AI and have the generating AI perform the weighting of the training data.

[0128] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0129] The reception desk can estimate the user's emotions and adjust the input method for their preferences based on those emotions. For example, if the user is stressed, it can provide a simple interface and minimize the input steps. If the user is relaxed, it can offer detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, it can prioritize voice input to allow for quick input of their preferences. In this way, by adjusting the input method for preferences according to the user's emotions, a more appropriate input method can be provided.

[0130] The learning unit can estimate the user's emotions and select training data based on those emotions. For example, if the user is stressed, it can select simple training data. If the user is relaxed, it can select more detailed training data. Furthermore, if the user is in a hurry, it can select data that allows for rapid learning. By selecting training data according to the user's emotions, more appropriate learning becomes possible.

[0131] The update function can estimate the user's emotions and adjust the frequency of property listing updates based on those emotions. For example, if the user is stressed, the update frequency can be reduced and only important properties are displayed. Conversely, if the user is relaxed, the update frequency can be increased and more properties can be displayed. Furthermore, if the user is in a hurry, the property listing can be updated in real time to provide information quickly. In this way, by adjusting the update frequency of the property listing according to the user's emotions, more appropriate property information can be provided.

[0132] The matching unit can estimate the user's emotions and adjust the matching criteria based on those estimates. For example, if the user is stressed, a simple matching criterion can be applied. Conversely, if the user is relaxed, a more detailed matching criterion can be applied. Furthermore, if the user is in a hurry, criteria for quick matching can be applied. This allows the system to provide more suitable properties by adjusting the matching criteria according to the user's emotions.

[0133] The suggestion function can estimate the user's emotions and adjust the suggestion method based on those emotions. For example, if the user is stressed, it can provide a simple and highly visible suggestion method. If the user is relaxed, it can provide a suggestion method that includes detailed information. Furthermore, if the user is in a hurry, it can provide a suggestion method that gets straight to the point. In this way, by adjusting the suggestion method according to the user's emotions, more appropriate suggestions can be provided.

[0134] The reception desk can analyze the user's past request input history and select the optimal input method. For example, it can automatically display requests that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). Furthermore, it can predict and suggest requests that the user will use during specific time periods based on their past input history. In this way, by analyzing the user's past request input history, the system can provide the most suitable input method.

[0135] The update unit can select the optimal update method when updating the property list by referring to past update history. For example, it can analyze past update history to understand the types of properties users prefer and update accordingly. It can also prioritize displaying properties that users have shown interest in based on past update history. Furthermore, it can exclude properties that users want to avoid based on past update history. In this way, by referring to past update history, the system can provide the most optimal method for updating the property list.

[0136] The matching function can improve the accuracy of matching by considering the interrelationships between properties during the matching process. For example, it can prioritize matching properties that are related to each other by considering the surrounding environment of the properties. It can also prioritize matching properties in the same price range by considering the price range of the properties. Furthermore, it can prioritize matching properties that match the user's preferences by considering the facilities and conditions of the properties. In this way, the accuracy of matching is improved by considering the interrelationships between properties.

[0137] The recommendation system can improve the accuracy of recommendations by considering the interrelationships between agents. For example, it can prioritize recommending related agents based on their past transaction data. It can also prioritize recommending highly reliable agents based on user reviews. Furthermore, it can prioritize recommending agents that match the user's desired conditions based on their transaction history. In this way, considering the interrelationships between agents improves the accuracy of recommendations.

[0138] The proposal department can apply different proposal methods depending on the property category. For example, for residential properties, a proposal method that reflects the latest market trends can be applied. For commercial properties, a proposal method that takes into account the surrounding business environment can be applied. Furthermore, for investment properties, a proposal method that emphasizes profitability can be applied. By applying different proposal methods to each property category, more appropriate proposals can be provided.

[0139] The following briefly describes the processing flow for example form 2.

[0140] Step 1: The reception desk enters the user's desired conditions. These conditions include price, location, floor plan, and age of the building. For example, the user can enter their desired price range, location, floor plan, and age of the building. Step 2: The update department updates the property list based on the information received by the reception department. The update department can update the property list in real time, register and delete properties, and perform periodic information updates. Step 3: The matching unit matches the user with the most suitable property based on the property list updated by the update unit. The matching unit can select properties based on the degree of match with the user's desired conditions, the property's evaluation score, and the user's past search history. Step 4: The analysis department analyzes the agent's past transaction data and user reviews. The analysis department can assess the agent's reliability based on their transaction performance, user review rating scores, and past transaction history. Step 5: The recommendation department recommends highly reliable agents based on the data analyzed by the analysis department. The recommendation department can personalize and recommend agents that match the user's preferences, and can make recommendations based on agent evaluation scores and transaction history. Step 6: The proposal department analyzes market trends and proposes the optimal timing for buying and selling. The proposal department can analyze market trends based on price fluctuations, transaction volume trends, and historical market data.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the reception unit, update unit, matching unit, analysis unit, recommendation unit, proposal unit, and learning unit, is implemented by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14, which inputs the user's desired conditions. The update unit is implemented by the specific processing unit 290 of the data processing unit 12, which updates the property list in real time. The matching unit is implemented by the specific processing unit 290 of the data processing unit 12, which matches the user with the most suitable property. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the agent's past transaction data and user reviews. The recommendation unit is implemented by the specific processing unit 290 of the data processing unit 12, which recommends highly reliable agents. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes market trends and proposes the optimal timing for buying and selling. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12, which learns the user's desired conditions. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0145] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] Each of the multiple elements described above, including the reception unit, update unit, matching unit, analysis unit, recommendation unit, proposal unit, and learning unit, is implemented by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214, which inputs the user's desired conditions. The update unit is implemented by the specific processing unit 290 of the data processing unit 12, which updates the property list in real time. The matching unit is implemented by the specific processing unit 290 of the data processing unit 12, which matches the user with the most suitable property. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the agent's past transaction data and user reviews. The recommendation unit is implemented by the specific processing unit 290 of the data processing unit 12, which recommends highly reliable agents. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes market trends and proposes the optimal timing for buying and selling. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12, which learns the user's desired conditions. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0161] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.).

[0173] 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.

[0174] 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.

[0175] 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.

[0176] Each of the multiple elements described above, including the reception unit, update unit, matching unit, analysis unit, recommendation unit, proposal unit, and learning unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314, which inputs the user's desired conditions. The update unit is implemented by the specific processing unit 290 of the data processing unit 12, which updates the property list in real time. The matching unit is implemented by the specific processing unit 290 of the data processing unit 12, which matches the user with the most suitable property. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the agent's past transaction data and user reviews. The recommendation unit is implemented by the specific processing unit 290 of the data processing unit 12, which recommends highly reliable agents. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes market trends and proposes the optimal timing for buying and selling. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12, which learns the user's desired conditions. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0177] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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).

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.).

[0190] 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.

[0191] 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.

[0192] 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.

[0193] Each of the multiple elements described above, including the reception unit, update unit, matching unit, analysis unit, recommendation unit, proposal unit, and learning unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and takes the user's desired conditions as input. The update unit is implemented by the specific processing unit 290 of the data processing unit 12 and updates the property list in real time. The matching unit is implemented by the specific processing unit 290 of the data processing unit 12 and matches the user with the most suitable property. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the agent's past transaction data and user reviews. The recommendation unit is implemented by the specific processing unit 290 of the data processing unit 12 and recommends highly reliable agents. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes market trends and proposes the optimal timing for buying and selling. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns the user's desired conditions. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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."

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] (Note 1) A reception desk where users enter their desired conditions, An update unit updates the list of properties based on the information received by the reception unit, A matching unit that matches the most suitable property based on the property list updated by the aforementioned update unit, The analytics department analyzes agents' past transaction data and user reviews, Based on the data analyzed by the aforementioned analysis unit, the recommendation unit recommends highly reliable agents, It includes a proposal department that analyzes market trends and suggests the optimal timing for buying and selling. A system characterized by the following features. (Note 2) It includes a learning unit that learns the user's desired conditions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned update unit is, The property list is updated in real time. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit is Analyze the agent's past transaction data and user reviews. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned recommendation department, Personalize and recommend highly reliable agents. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, We analyze market trends and propose the optimal timing for buying and selling. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and adjusts the input method for desired conditions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the user's past input history of desired conditions and select the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When users enter their desired criteria, the system filters the results based on their current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and determines the priority of the input preferences based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When users enter their desired conditions, the system prioritizes highly relevant conditions by considering their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When you enter your desired criteria, the system analyzes your social media activity and inputs relevant criteria. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned update unit is, The system estimates user sentiment and adjusts the frequency of property listing updates based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned update unit is, When updating the property list, the system will refer to past update history to select the most suitable update method. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned update unit is, When updating the property list, different update algorithms are applied depending on the property category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned update unit is, The system estimates the user's emotions and adjusts how the property listings are displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned update unit is, When updating the property list, 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 update unit is, When updating the property list, we refer to relevant literature for each property to improve the accuracy of the update. The system described in Appendix 1, characterized by the features described herein. (Note 19) The matching unit is It estimates the user's emotions and adjusts the matching criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The matching unit is When matching properties, we improve the accuracy of the matching process by considering the interrelationships between them. The system described in Appendix 1, characterized by the features described herein. (Note 21) The matching unit is During the matching process, the attribute information of the property submitter is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 22) The matching unit is It estimates the user's sentiment and adjusts the order in which matching results are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The matching unit is During the matching process, the geographical distribution of properties is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The matching unit is During the matching process, we refer to relevant literature related to the property to improve the accuracy of the matching. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned analysis unit is During analysis, refer to past analysis data to optimize the current analysis. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned analysis unit is During analysis, different analytical methods are applied to each agent category. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned analysis unit is We estimate the user's emotions and adjust the importance of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned analysis unit is During the analysis, we analyze how the analysis changes based on the agent's transaction timing. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned analysis unit is During the analysis, we will refer to relevant market data for the agents. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned recommendation department, It estimates the user's emotions and determines the priority of recommended agents based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned recommendation department, When making recommendations, we improve the accuracy of recommendations by considering the relationships between agents. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned recommendation department, When making recommendations, the agent's attribute information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned recommendation department, We estimate the user's emotions and adjust how recommended agents are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned recommendation department, When making recommendations, the geographical distribution of agents should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned recommendation department, When making recommendations, we improve the accuracy of recommendations by referring to the agent's relevant literature. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned proposal section is, It estimates the user's emotions and adjusts the suggestion method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned proposal section is, When making a proposal, the proposal algorithm is optimized by referring to past proposal data. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned proposal section is, When making a proposal, different proposal methods will be applied depending on the property category. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned proposal section is, When making a proposal, analyze how the proposal changes based on when the property was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned proposal section is, When making a proposal, refer to relevant market data for the property. The system described in Appendix 1, characterized by the features described herein. (Note 43) The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 44) The aforementioned learning unit, During training, the learning algorithm is optimized by referring to past training data. The system described in Appendix 2, characterized by the features described herein. (Note 45) The aforementioned learning unit, It estimates the user's emotions and adjusts the learning frequency based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 46) The aforementioned learning unit, During training, the training data is weighted based on when the property was submitted. The system described in Appendix 2, characterized by the features described herein. [Explanation of Symbols]

[0213] 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. A reception desk where users enter their desired conditions, An update unit updates the list of properties based on the information received by the reception unit, A matching unit that matches the most suitable property based on the property list updated by the aforementioned update unit, The analytics department analyzes agents' past transaction data and user reviews, Based on the data analyzed by the aforementioned analysis unit, the recommendation unit recommends highly reliable agents, It includes a proposal department that analyzes market trends and suggests the optimal timing for buying and selling. A system characterized by the following features.

2. It includes a learning unit that learns the user's desired conditions. The system according to feature 1.

3. The aforementioned update unit is The property list is updated in real time. The system according to feature 1.

4. The aforementioned analysis unit is Analyze the agent's past transaction data and user reviews. The system according to feature 1.

5. The aforementioned recommendation department, Personalize and recommend highly reliable agents. The system according to feature 1.

6. The aforementioned proposal section is, We analyze market trends and propose the optimal timing for buying and selling. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and adjusts the input method for desired conditions based on the estimated user emotions. The system according to feature 1.

8. The aforementioned reception unit is Analyze the user's past input history of desired conditions and select the optimal input method. The system according to feature 1.

9. The aforementioned reception unit is When users enter their desired criteria, the system filters the results based on their current lifestyle and areas of interest. The system according to feature 1.

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