system

Abstract

The system according to this embodiment aims to quickly provide the most suitable rental property based on the user's desired conditions. [Solution] The system according to the embodiment comprises a reception unit, a selection unit, an analysis unit, a provision unit, and a support unit. The reception unit receives the user's desired conditions. The selection unit generates a list of optimal rental properties based on the information received by the reception unit. The analysis unit analyzes the user's lifestyle data. The provision unit provides information based on the data obtained by the analysis unit. The support unit supports contract procedures and moving arrangements.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of 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 prior art, there is a problem that it is difficult to quickly provide an optimal rental property based on the user's desired conditions.

[0005] The system according to the embodiment aims to quickly provide an optimal rental property based on the user's desired conditions.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a selection unit, an analysis unit, a provision unit, and a support unit. The reception unit receives the user's desired conditions. The selection unit generates a list of optimal rental properties based on the information received by the reception unit. The analysis unit analyzes the user's lifestyle data. The provision unit provides information based on the data obtained by the analysis unit. The support unit assists with contract procedures and moving arrangements. [Effects of the Invention]

[0007] The system according to this embodiment can quickly provide the most suitable rental property based on the user's desired conditions. [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) The rental apartment suggestion system according to an embodiment of the present invention is a system that suggests rental apartments using AI technology. This rental apartment suggestion system allows users to input their desired conditions, and the AI ​​generates a list of optimal rental properties based on the user's desired conditions, enabling the rapid provision of suitable properties to those planning to relocate or move. Furthermore, the rental apartment suggestion system can provide suggestions tailored to individual needs by analyzing the user's lifestyle data and suggesting the most suitable areas and properties. In addition, the rental apartment suggestion system supports the user's decision-making by having agents understand the user's needs and provide the latest market information and property details. It also supports contract procedures and moving arrangements, realizing the ideal housing search. This mechanism enables efficient property selection and improves user satisfaction. It also enables differentiation from other companies and strengthens market competitiveness. Furthermore, it provides economic benefits by reducing the time and effort required for property selection. For example, the rental apartment suggestion system allows users to input their desired conditions. For example, the rental apartment suggestion system allows users to input desired conditions such as rent, floor plan, and location. Next, the rental apartment suggestion system uses AI to generate a list of optimal rental properties based on the user's desired conditions. For example, a rental apartment suggestion system lists and presents properties that match the user's desired conditions. Furthermore, the rental apartment suggestion system analyzes the user's lifestyle data. For example, it analyzes the user's hobbies and daily activity patterns to suggest the most suitable area and property. In addition, the rental apartment suggestion system has agents who understand the user's needs and provide the latest market information and property details. For example, agents provide the user with detailed property information and information about the surrounding environment. Furthermore, the rental apartment suggestion system supports contract procedures and moving arrangements. For example, it assists with contract creation and arranges moving companies. As a result, the rental apartment suggestion system can achieve efficient property selection and improve user satisfaction.This allows the rental apartment suggestion system to quickly provide the most suitable rental properties based on the user's desired conditions, enabling efficient property selection.

[0029] The rental apartment proposal system according to this embodiment comprises a reception unit, a selection unit, an analysis unit, a provision unit, and a support unit. The reception unit receives input from the user regarding their desired conditions. These conditions include, but are not limited to, rent, floor plan, and location. For example, the reception unit receives input from the user regarding their desired rent. The reception unit can also receive input from the user regarding their desired floor plan. Furthermore, the reception unit can also receive input from the user regarding their desired location. For example, the reception unit provides an interface for the user to input their desired rent. The user can input their desired rent through this interface. The reception unit also provides an interface for the user to input their desired floor plan. The user can input their desired floor plan through this interface. Furthermore, the reception unit provides an interface for the user to input their desired location. The user can input their desired location through this interface. The selection unit generates a list of optimal rental properties based on the information received by the reception unit. The selection unit lists properties based on the user's desired conditions. For example, the selection unit lists properties that match the user's desired rent. The selection unit can also list properties that match the user's desired floor plan. Furthermore, the selection unit can also list properties that match the user's desired location. For example, the selection unit uses an algorithm to list properties that match the user's desired rent. The selection unit can use an algorithm to list properties that match the user's desired rent. Furthermore, the selection unit uses an algorithm to list properties that match the user's desired floor plan. The selection unit can use an algorithm to list properties that match the user's desired floor plan. Furthermore, the selection unit uses an algorithm to list properties that match the user's desired location. The selection unit can use an algorithm to list properties that match the user's desired location. The analysis unit analyzes the user's lifestyle data. For example, the analysis unit analyzes the user's hobbies and daily behavior patterns.The analysis department collects data to analyze users' hobbies, for example. The analysis department can collect data and analyze users' hobbies. The analysis department also collects data to analyze users' daily behavior patterns, for example. The analysis department can collect data and analyze users' daily behavior patterns. Furthermore, the analysis department can comprehensively analyze users' lifestyle data, for example. The analysis department can comprehensively analyze users' hobbies and daily behavior patterns and propose optimal areas and properties. The provision department provides information based on the data obtained by the analysis department. The provision department provides, for example, detailed property information to users, for example. The provision department provides, for example, an interface for providing detailed property information, for example. The provision department can provide detailed property information to users through this interface. Furthermore, the provision department can also provide users with information about the surrounding environment, for example. The provision department provides, for example, an interface for providing information about the surrounding environment, for example. The provision department can provide information about the surrounding environment to users through this interface. Furthermore, the provision department can also provide users with the latest market information, for example. The provision department provides, for example, an interface for providing the latest market information. The provisioning unit can provide users with the latest market information through an interface. The support unit supports contract procedures and moving arrangements. The support unit can, for example, assist in drafting contracts. The support unit can, for example, provide an interface for assisting in drafting contracts. The support unit can provide users with assistance in drafting contracts through this interface. The support unit can also arrange for moving companies. The support unit can, for example, provide an interface for arranging for moving companies. The support unit can provide users with arrangements for moving companies through this interface. As a result, the rental apartment proposal system according to this embodiment can quickly provide the most suitable rental property based on the user's desired conditions and achieve efficient property selection.

[0030] The reception desk inputs the user's desired conditions. These conditions may include, but are not limited to, rent, floor plan, and location. For example, the reception desk can input the user's desired rent. It can also input the user's desired floor plan. Furthermore, it can input the user's desired location. For instance, the reception desk provides an interface for the user to input their desired rent. The user can input their desired rent through this interface. The reception desk also provides an interface for the user to input their desired floor plan. The user can input their desired floor plan through this interface. Furthermore, the reception desk provides an interface for the user to input their desired location. The user can input their desired location through this interface. The reception desk stores the user's entered conditions in a database and uses them for subsequent processing. For example, the reception desk centrally manages the rent, floor plan, and location conditions entered by the user, making them accessible to the selection and analysis departments. Furthermore, the reception desk can update the user's entered conditions in real time. For example, if the user changes their desired conditions, the reception desk immediately updates the database to reflect the latest information. This allows the reception desk to accurately and quickly collect user preferences, improving the overall efficiency of the system. Furthermore, the reception desk can provide relevant information based on the user's input preferences. For example, it can display a list of properties matching the user's desired rent or offer advice on preferred floor plans. This enables the reception desk to respond flexibly to user needs, thereby improving user satisfaction.

[0031] The selection unit generates a list of optimal rental properties based on the information received by the reception unit. For example, the selection unit lists properties based on the user's desired conditions. For example, the selection unit lists properties that match the user's desired rent. The selection unit can also list properties that match the user's desired floor plan. Furthermore, the selection unit can list properties that match the user's desired location. For example, the selection unit uses an algorithm to list properties that match the user's desired rent. The selection unit can use an algorithm to list properties that match the user's desired rent. The selection unit also uses an algorithm to list properties that match the user's desired floor plan. Furthermore, the selection unit uses an algorithm to list properties that match the user's desired location. By using these algorithms, the selection unit can quickly list the properties that best suit the user's desired conditions. Furthermore, the selection unit can prioritize properties based on the user's desired conditions. For example, it can prioritize listing the most suitable properties by considering the importance of rent, floor plan, and location. This allows the selection unit to efficiently select properties that best suit the user's needs. The selection unit can also filter properties based on the user's desired conditions. For example, it can generate a more detailed property list by adding conditions such as a specific area, distance from the station, or age of the building. This allows the selection unit to respond to the user's specific needs and provide the most suitable properties.

[0032] The analytics department analyzes users' lifestyle data. For example, the analytics department analyzes users' hobbies and daily behavior patterns. For example, the analytics department collects data to analyze users' hobbies. The analytics department can collect data and analyze users' hobbies. The analytics department also collects data to analyze users' daily behavior patterns. The analytics department can collect data and analyze users' daily behavior patterns. Furthermore, the analytics department can comprehensively analyze users' lifestyle data. The analytics department can comprehensively analyze users' hobbies and daily behavior patterns and propose the most suitable areas and properties. The analytics department utilizes various data sources to collect users' lifestyle data. For example, it collects the content of users' social media posts and the usage history of applications used by users to understand users' hobbies and interests. It also collects data such as users' travel history and purchase history to analyze daily behavior patterns. As a result, the analytics department can understand users' lifestyles in detail and propose the most suitable properties. Furthermore, the analytics department uses AI to analyze the collected data and identify properties that are best suited to the user's lifestyle. For example, AI learns users' hobbies and behavioral patterns, identifying the characteristics of areas and properties that users prefer. This allows the analytics department to suggest properties that are best suited to the user's lifestyle. Furthermore, the analytics department can predict future needs based on users' lifestyle data. For instance, it can predict and suggest the conditions of properties that will be needed in the future in response to changes in the user's lifestyle. This allows the analytics department to address users' long-term needs and provide them with the most suitable properties.

[0033] The provisioning unit provides information based on data obtained by the analysis unit. For example, the provisioning unit provides users with detailed property information. For example, the provisioning unit provides an interface for providing detailed property information. The provisioning unit can provide users with detailed property information through this interface. The provisioning unit can also provide users with information about the surrounding environment. For example, the provisioning unit provides an interface for providing information about the surrounding environment. The provisioning unit can provide users with information about the surrounding environment through this interface. Furthermore, the provisioning unit can also provide users with the latest market information. For example, the provisioning unit provides an interface for providing the latest market information. The provisioning unit can provide users with the latest market information through this interface. The provisioning unit provides a visually easy-to-understand interface so that users can easily view detailed property information. For example, it displays property photos, floor plans, and detailed information about facilities so that users can intuitively understand the features of the property. The provisioning unit also provides information about the surrounding environment of the property. For example, it provides information about the nearest train station or bus stop, supermarkets, hospitals, and other facilities necessary for daily life so that users can understand the convenience of the property. Furthermore, the service provider will provide users with the latest market information to help them select properties. For example, they will provide information on rental market trends and property price fluctuations, enabling users to select properties at the optimal time. In this way, the service provider can provide users with comprehensive information and support them in their property selection.

[0034] The support department assists with contract procedures and moving arrangements. For example, the support department provides assistance in drafting contracts. For example, it provides an interface for drafting contracts. Through this interface, the support department can provide users with assistance in drafting contracts. The support department can also arrange for moving companies. For example, it provides an interface for arranging moving companies. Through this interface, the support department can provide users with assistance in arranging moving companies. The support department provides detailed guidelines regarding contract procedures to help users proceed smoothly. For example, it provides advice on how to fill out contracts and prepare necessary documents, ensuring users can complete the contract process without anxiety. In addition to arranging moving companies, the support department also assists with various procedures related to moving. For example, it provides support for changing contracts for utilities such as electricity, gas, and water, and various procedures related to changing addresses. This allows the support department to provide comprehensive support so that users can smoothly start their new lives. Furthermore, the support department responds quickly to user inquiries and provides necessary information. For example, by providing expert advice on contract details or moving-related inquiries, the support department can alleviate user concerns. This allows the support department to provide users with a sense of security and improve their satisfaction.

[0035] The reception desk can analyze the user's past input history of desired conditions and provide the optimal input interface. For example, the reception desk can automatically display as suggestions the user has frequently entered in the past. For example, the reception desk can automatically display the most frequently entered conditions based on the user's past input. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, if the reception desk has used voice input in the past, it will prioritize suggesting voice input. The reception desk can also predict and suggest desired conditions to be used during specific time periods based on the user's past input history. For example, based on the desired conditions the user has entered during a specific time period in the past, the reception desk will suggest similar conditions for the same time period. In this way, by analyzing past input history, the reception desk can provide the user with the optimal input interface. 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 past input history data into a generating AI and have the generating AI suggest the optimal input interface.

[0036] The reception desk can customize input fields based on the user's current living situation and areas of interest when they enter their desired conditions. For example, when the user enters their current living situation, the reception desk can automatically display relevant input fields based on their areas of interest. For example, when the user enters their family structure, the reception desk can automatically display input fields related to family structure. The reception desk can also prioritize property information related to specific areas of interest if the user has such areas. For example, if the user owns a pet, the reception desk can prioritize property information that allows pets. The reception desk can also customize necessary input fields based on the user's current living situation to reduce the effort required for input. For example, if the user is single, the reception desk can prioritize property information for single people. This reduces the effort required for input by customizing input fields according to the user's 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 the user's living situation data into a generating AI and have the generating AI perform the customization of input fields.

[0037] The reception desk can prioritize the input of highly relevant conditions when users enter their desired conditions, taking into account their geographical location. For example, if a user lives in a specific area, the reception desk can prioritize the input of desired conditions related to that area. For example, if a user lives in a specific area, the reception desk can prioritize the input of desired conditions related to the average rent and surrounding facilities in that area. The reception desk can also prioritize the input of desired conditions related to a specific area if the user plans to move to that area. For example, if a user plans to move to a specific area, the reception desk can prioritize the input of desired conditions related to the transportation access and school district in that area. Furthermore, the reception desk can automatically display highly relevant conditions based on the user's current geographical location. For example, the reception desk can automatically display information on nearby properties and desired conditions related to the surrounding environment based on the user's current geographical location. This allows for more appropriate property selection by prioritizing the input of highly relevant conditions based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's geographical location information into the generating AI and have the AI ​​prioritize inputting the most relevant conditions.

[0038] The reception desk can analyze the user's social media activity when they input their desired conditions and suggest relevant conditions. For example, the reception desk can suggest property types that the user is interested in based on their social media activity. The reception desk can also suggest desired conditions related to specific regions based on the user's social media activity. For example, the reception desk can suggest desired conditions related to regions that the user is interested in based on their social media activity. The reception desk can also analyze the user's social media activity and automatically display relevant desired conditions. For example, the reception desk can analyze the user's social media activity and automatically display conditions that the user is interested in. This allows the reception desk to suggest relevant conditions 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 using AI. For example, the reception desk can input the user's social media data into a generating AI and have the generating AI suggest relevant conditions.

[0039] The selection unit can adjust the level of detail in the property list based on the importance of the properties when generating the property list. For example, the selection unit can display high-importance properties in detail and low-importance properties in a simplified manner. For example, the selection unit can display detailed property information for high-importance properties and simplified property information for low-importance properties. The selection unit can also adjust the display order of the list according to the importance of the properties. For example, the selection unit can display high-importance properties at the top of the list and low-importance properties at the bottom. The selection unit can also prioritize the display of high-importance properties to attract the user's attention. For example, the selection unit can display high-importance properties at the beginning of the list to attract the user's attention. In this way, by adjusting the level of detail in the list according to the importance of the properties, it is possible to provide property information that will attract the user's attention. Some or all of the above processing in the selection unit may be performed using AI, for example, or not using AI. For example, the selection unit can input property importance data into a generation AI and have the generation AI perform the adjustment of the level of detail in the list.

[0040] The selection unit can apply different selection algorithms depending on the property category when generating the property list. For example, for family-oriented properties, the selection unit can apply an algorithm that takes into account family structure and proximity to schools. For example, for family-oriented properties, the selection unit can apply an algorithm that takes into account family structure and proximity to schools to select the most suitable property. The selection unit can also apply an algorithm that takes into account commuting convenience and surrounding facilities for single-person properties. For example, for single-person properties, the selection unit can apply an algorithm that takes into account commuting convenience and surrounding facilities to select the most suitable property. The selection unit can also apply an algorithm that takes into account barrier-free access and proximity to medical facilities for elderly-oriented properties. For example, for elderly-oriented properties, the selection unit can apply an algorithm that takes into account barrier-free access and proximity to medical facilities to select the most suitable property. In this way, by applying different selection algorithms depending on the property category, property information that meets the user's needs can be provided. Some or all of the above processing in the selection unit may be performed using AI, for example, or without using AI. For example, the selection unit can input property category data into the generation AI and have the generation AI execute the application of the selection algorithm.

[0041] The selection unit can determine the priority of a property list based on the property registration date when generating the property list. For example, the selection unit can prioritize displaying newly registered properties. For example, the selection unit can display properties with newer registration dates at the top of the list. The selection unit can also postpone displaying older properties. For example, the selection unit can display older properties at the bottom of the list. The selection unit can also adjust the display order of the property list according to the registration date. For example, the selection unit can adjust the display order of the list according to the property registration date, prioritizing the display of newer properties. This allows for the priority of providing new property information by determining the list priority based on the property registration date. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input property registration date data into a generation AI and have the generation AI determine the list priority.

[0042] The selection unit can adjust the order of properties in a list based on their relevance when generating the property list. For example, the selection unit can prioritize displaying properties that are most relevant to the user's desired conditions. For example, the selection unit can display properties most relevant to the user's desired conditions at the top of the list. The selection unit can also postpone displaying less relevant properties. For example, the selection unit can display less relevant properties at the bottom of the list. The selection unit can also adjust the display order of the list according to the relevance of the properties. For example, the selection unit can adjust the display order of the list according to the relevance of the properties, prioritizing the display of highly relevant properties. In this way, by adjusting the order of the list based on the relevance of the properties, the selection unit can prioritize providing properties that are most relevant to the user's desired conditions. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input property relevance data into a generation AI and have the generation AI perform the adjustment of the list order.

[0043] The analysis unit can optimize its analysis algorithm by referring to past data when analyzing lifestyle data. For example, the analysis unit can select the optimal analysis algorithm based on past lifestyle data. For example, the analysis unit can select the optimal analysis algorithm by referring to past lifestyle data. The analysis unit can also improve the accuracy of the analysis results by referring to past data. For example, the analysis unit can improve the accuracy of the analysis results by referring to past data. The analysis unit can also perform analysis that is optimal for the user's lifestyle by utilizing past data. For example, the analysis unit can perform analysis that is optimal for the user's lifestyle by utilizing past data. In this way, the accuracy of the analysis results can be improved by optimizing the analysis algorithm by referring to past data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input past data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0044] The analysis unit can perform lifestyle data analysis while considering user attribute information. For example, the analysis unit can analyze lifestyle data while considering the user's age and gender. The analysis unit can also analyze lifestyle data while considering the user's occupation and income. The analysis unit can also analyze lifestyle data while considering the user's family structure and hobbies. By considering user attribute information during analysis, more appropriate analysis results 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 user attribute information into a generating AI and have the generating AI perform the analysis.

[0045] The analysis unit can perform lifestyle data analysis while considering the geographical distribution of users. For example, the analysis unit can analyze lifestyle data while considering the user's residential area. The analysis unit can also analyze lifestyle data while considering the user's commuting route. For example, the analysis unit can analyze lifestyle data while considering the user's commuting route. The analysis unit can also perform optimal analysis based on the geographical distribution of users. For example, the analysis unit can perform optimal analysis based on the geographical distribution of users. By considering the geographical distribution of users, more appropriate analysis results 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 user geographical distribution data into a generating AI and have the generating AI perform the analysis.

[0046] The analysis unit can improve the accuracy of its analysis by referring to relevant literature when analyzing lifestyle data. For example, the analysis unit can optimize its lifestyle data analysis method by referring to relevant literature. The analysis unit can also improve the accuracy of its analysis results based on relevant literature. The analysis unit can also utilize relevant literature to perform an analysis that is best suited to the user's lifestyle. This improves the accuracy of lifestyle data analysis by referring to relevant literature. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevant literature data into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0047] The information provider can provide optimal information by referring to the user's past behavior history when providing information. For example, the information provider can provide optimal property information based on the user's past behavior history. For example, the information provider can refer to the user's past behavior history and provide optimal property information. The information provider can also prioritize providing relevant information from the user's past behavior history. For example, the information provider can refer to the user's past behavior history and prioritize providing relevant information. The information provider can also analyze the user's past behavior history and provide the most suitable information. For example, the information provider can analyze the user's past behavior history and provide the most suitable information. In this way, by referring to past behavior history, the information provider can provide the user with optimal information. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the user's past behavior history data into a generating AI and have the generating AI perform the task of providing optimal information.

[0048] The information provider can provide optimal information by considering the user's geographical location when providing information. For example, the information provider can provide optimal property information based on the user's current location. The information provider can also prioritize providing relevant information by considering the user's geographical location. The information provider can also customize and provide information according to the user's current location. By doing so, more appropriate property information can be provided by providing optimal information based on the user's geographical location. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the user's geographical location data into a generating AI and have the generating AI perform the task of providing optimal information.

[0049] The information provider can provide relevant information by analyzing the user's social media activity when providing information. For example, the information provider can provide property information that the user is interested in based on the user's social media activity. The information provider can also prioritize providing relevant information based on the user's social media activity. The information provider can also analyze the user's social media activity and provide the most appropriate information. In this way, relevant information can be provided by analyzing the user's social media activity. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the user's social media data into a generating AI and have the generating AI perform the provision of relevant information.

[0050] The support unit can provide optimal support by referring to the user's past support history during support. For example, the support unit can provide the optimal support method based on the user's past support history. For example, the support unit can refer to the user's past support history and provide the optimal support method. The support unit can also prioritize providing relevant support based on the user's past support history. For example, the support unit can refer to the user's past support history and prioritize providing relevant support. The support unit can also analyze the user's past support history and provide the most appropriate support. For example, the support unit can analyze the user's past support history and provide the most appropriate support. In this way, by referring to past support history, the support unit can provide the user with the most suitable support. Some or all of the above processes in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input the user's past support history data into a generating AI and have the generating AI perform the task of providing optimal support.

[0051] The support unit can customize the support provided based on the user's current living situation. For example, the support unit can provide the most appropriate support based on the user's current living situation. For example, the support unit can refer to the user's current living situation and provide the most appropriate support. The support unit can also prioritize providing relevant support according to the user's current living situation. For example, the support unit can refer to the user's current living situation and prioritize providing relevant support. The support unit can also customize and provide support considering the user's current living situation. For example, the support unit can customize and provide support considering the user's current living situation. This allows for the provision of more appropriate support by customizing support based on the user's current living situation. Some or all of the above processes in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's current living situation data into a generating AI and have the generating AI perform the customization of the support content.

[0052] The support unit can provide optimal support by considering the user's geographical location information during support. For example, the support unit can provide optimal support based on the user's current location. The support unit can also prioritize providing relevant support by considering the user's geographical location information. The support unit can also customize and provide support content according to the user's current location. This allows for more appropriate support to be provided by providing optimal support based on the user's geographical location information. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's geographical location information data into a generating AI and have the generating AI perform the provision of optimal support.

[0053] The support unit can analyze the user's social media activity and provide relevant support during support sessions. For example, the support unit can provide support that the user is interested in based on their social media activity. The support unit can also prioritize providing relevant support based on the user's social media activity. The support unit can also analyze the user's social media activity and provide the most appropriate support. In this way, relevant support can be provided by analyzing the user's social media activity. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's social media data into a generating AI and have the generating AI perform the provision of relevant support.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The rental apartment suggestion system can analyze a user's past property selection history and make optimal property suggestions. For example, it can prioritize suggesting properties with similar characteristics based on the features of properties the user has previously selected. It can also consider the area and rent range of properties the user has previously selected and suggest properties with similar conditions. Furthermore, it can analyze the trends of properties selected at specific time periods from the user's past selection history and suggest similar properties at the same time period. In this way, by analyzing past selection history, the system can make optimal property suggestions to the user. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input past selection history data into a generating AI and have the generating AI execute optimal property suggestions.

[0056] The rental apartment suggestion system can customize property suggestions based on the user's current living situation. For example, when a user enters their family structure, the system prioritizes suggesting properties related to that family structure. Furthermore, if a user has a specific area of ​​interest, the system can prioritize suggesting properties related to that area. In addition, it can customize the necessary property information based on the user's current living situation, reducing the effort required for suggestions. This reduces the effort involved in suggesting properties by customizing them according to the user's living situation and areas of interest. Some or all of the above-described processes in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the user's living situation data into a generating AI and have the generating AI perform the customization of property suggestions.

[0057] The rental apartment suggestion system can suggest properties while considering the user's geographical location. For example, if the user lives in a specific area, it can prioritize suggesting properties related to that area. Similarly, if the user plans to move to a specific area, it can prioritize suggesting properties related to that area. Furthermore, it can automatically display highly relevant property information based on the user's current geographical location. This allows for more appropriate property selection by prioritizing the suggestion of highly relevant properties based on the user's geographical location. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For instance, the selection unit can input the user's geographical location information into a generating AI, causing the AI ​​to prioritize suggesting highly relevant property information.

[0058] The rental apartment suggestion system can analyze a user's social media activity and suggest relevant property information. For example, it can suggest property types of interest based on the user's social media activity. It can also suggest property information related to a specific area based on the user's social media activity. Furthermore, it can analyze the user's social media activity and automatically display relevant property information. In this way, relevant property information can be suggested by analyzing the user's social media activity. Some or all of the above processing in the selection unit may be performed using AI, for example, or not using AI. For example, the selection unit can input the user's social media data into a generating AI and have the generating AI perform the task of suggesting relevant property information.

[0059] The rental apartment suggestion system can adjust the level of detail in the property list based on the importance of each property when generating the property list. For example, it can display highly important properties in detail and less important properties in a simplified manner. It can also adjust the display order of the list according to the importance of each property. Furthermore, it can prioritize the display of highly important properties to attract the user's attention. In this way, by adjusting the level of detail in the list according to the importance of each property, it is possible to provide property information that will attract the user's attention. Some or all of the above processing in the selection unit may be performed using AI, for example, or not using AI. For example, the selection unit can input property importance data into a generation AI and have the generation AI perform the adjustment of the level of detail in the list.

[0060] The rental apartment suggestion system can apply different selection algorithms depending on the property category when generating a property list. For example, for family-friendly properties, an algorithm that considers family structure and proximity to schools can be applied. For single-person properties, an algorithm that considers commuting convenience and surrounding facilities can be applied. Furthermore, for properties for the elderly, an algorithm that considers barrier-free access and proximity to medical facilities can be applied. By applying different selection algorithms according to the property category, the system can provide property information that meets the user's needs. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input property category data into a generation AI and have the generation AI execute the application of the selection algorithm.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The reception desk inputs the user's desired conditions. These conditions include rent, floor plan, and location. The reception desk provides an interface for the user to input their desired rent, floor plan, and location conditions, and the user can input these conditions through this interface. Step 2: The selection unit generates a list of optimal rental properties based on the information received by the reception unit. The selection unit uses an algorithm to list properties that match the user's desired conditions, such as rent, floor plan, and location. Step 3: The analytics department analyzes user lifestyle data. The analytics department analyzes users' hobbies and daily behavior patterns, collects data to suggest the most suitable areas and properties, and performs a comprehensive analysis. Step 4: The provision department provides information based on the data obtained by the analysis department. The provision department provides users with an interface to provide them with detailed property information, information on the surrounding environment, and the latest market information, and users can obtain this information through the interface. Step 5: The support department assists with contract procedures and moving arrangements. The support department provides an interface for assisting with contract creation and arranging moving companies, and users can receive this support through the interface.

[0063] (Example of form 2) The rental apartment suggestion system according to an embodiment of the present invention is a system that suggests rental apartments using AI technology. This rental apartment suggestion system allows users to input their desired conditions, and the AI ​​generates a list of optimal rental properties based on the user's desired conditions, enabling the rapid provision of suitable properties to those planning to relocate or move. Furthermore, the rental apartment suggestion system can provide suggestions tailored to individual needs by analyzing the user's lifestyle data and suggesting the most suitable areas and properties. In addition, the rental apartment suggestion system supports the user's decision-making by having agents understand the user's needs and provide the latest market information and property details. It also supports contract procedures and moving arrangements, realizing the ideal housing search. This mechanism enables efficient property selection and improves user satisfaction. It also enables differentiation from other companies and strengthens market competitiveness. Furthermore, it provides economic benefits by reducing the time and effort required for property selection. For example, the rental apartment suggestion system allows users to input their desired conditions. For example, the rental apartment suggestion system allows users to input desired conditions such as rent, floor plan, and location. Next, the rental apartment suggestion system uses AI to generate a list of optimal rental properties based on the user's desired conditions. For example, a rental apartment suggestion system lists and presents properties that match the user's desired conditions. Furthermore, the rental apartment suggestion system analyzes the user's lifestyle data. For example, it analyzes the user's hobbies and daily activity patterns to suggest the most suitable area and property. In addition, the rental apartment suggestion system has agents who understand the user's needs and provide the latest market information and property details. For example, agents provide the user with detailed property information and information about the surrounding environment. Furthermore, the rental apartment suggestion system supports contract procedures and moving arrangements. For example, it assists with contract creation and arranges moving companies. As a result, the rental apartment suggestion system can achieve efficient property selection and improve user satisfaction.This allows the rental apartment suggestion system to quickly provide the most suitable rental properties based on the user's desired conditions, enabling efficient property selection.

[0064] The rental apartment proposal system according to this embodiment comprises a reception unit, a selection unit, an analysis unit, a provision unit, and a support unit. The reception unit receives input from the user regarding their desired conditions. These conditions include, but are not limited to, rent, floor plan, and location. For example, the reception unit receives input from the user regarding their desired rent. The reception unit can also receive input from the user regarding their desired floor plan. Furthermore, the reception unit can also receive input from the user regarding their desired location. For example, the reception unit provides an interface for the user to input their desired rent. The user can input their desired rent through this interface. The reception unit also provides an interface for the user to input their desired floor plan. The user can input their desired floor plan through this interface. Furthermore, the reception unit provides an interface for the user to input their desired location. The user can input their desired location through this interface. The selection unit generates a list of optimal rental properties based on the information received by the reception unit. The selection unit lists properties based on the user's desired conditions. For example, the selection unit lists properties that match the user's desired rent. The selection unit can also list properties that match the user's desired floor plan. Furthermore, the selection unit can also list properties that match the user's desired location. For example, the selection unit uses an algorithm to list properties that match the user's desired rent. The selection unit can use an algorithm to list properties that match the user's desired rent. Furthermore, the selection unit uses an algorithm to list properties that match the user's desired floor plan. The selection unit can use an algorithm to list properties that match the user's desired floor plan. Furthermore, the selection unit uses an algorithm to list properties that match the user's desired location. The selection unit can use an algorithm to list properties that match the user's desired location. The analysis unit analyzes the user's lifestyle data. For example, the analysis unit analyzes the user's hobbies and daily behavior patterns.The analysis department collects data to analyze users' hobbies, for example. The analysis department can collect data and analyze users' hobbies. The analysis department also collects data to analyze users' daily behavior patterns, for example. The analysis department can collect data and analyze users' daily behavior patterns. Furthermore, the analysis department can comprehensively analyze users' lifestyle data, for example. The analysis department can comprehensively analyze users' hobbies and daily behavior patterns and propose optimal areas and properties. The provision department provides information based on the data obtained by the analysis department. The provision department provides, for example, detailed property information to users, for example. The provision department provides, for example, an interface for providing detailed property information, for example. The provision department can provide detailed property information to users through this interface. Furthermore, the provision department can also provide users with information about the surrounding environment, for example. The provision department provides, for example, an interface for providing information about the surrounding environment, for example. The provision department can provide information about the surrounding environment to users through this interface. Furthermore, the provision department can also provide users with the latest market information, for example. The provision department provides, for example, an interface for providing the latest market information. The provisioning unit can provide users with the latest market information through an interface. The support unit supports contract procedures and moving arrangements. The support unit can, for example, assist in drafting contracts. The support unit can, for example, provide an interface for assisting in drafting contracts. The support unit can provide users with assistance in drafting contracts through this interface. The support unit can also arrange for moving companies. The support unit can, for example, provide an interface for arranging for moving companies. The support unit can provide users with arrangements for moving companies through this interface. As a result, the rental apartment proposal system according to this embodiment can quickly provide the most suitable rental property based on the user's desired conditions and achieve efficient property selection.

[0065] The reception desk inputs the user's desired conditions. These conditions may include, but are not limited to, rent, floor plan, and location. For example, the reception desk can input the user's desired rent. It can also input the user's desired floor plan. Furthermore, it can input the user's desired location. For instance, the reception desk provides an interface for the user to input their desired rent. The user can input their desired rent through this interface. The reception desk also provides an interface for the user to input their desired floor plan. The user can input their desired floor plan through this interface. Furthermore, the reception desk provides an interface for the user to input their desired location. The user can input their desired location through this interface. The reception desk stores the user's entered conditions in a database and uses them for subsequent processing. For example, the reception desk centrally manages the rent, floor plan, and location conditions entered by the user, making them accessible to the selection and analysis departments. Furthermore, the reception desk can update the user's entered conditions in real time. For example, if the user changes their desired conditions, the reception desk immediately updates the database to reflect the latest information. This allows the reception desk to accurately and quickly collect user preferences, improving the overall efficiency of the system. Furthermore, the reception desk can provide relevant information based on the user's input preferences. For example, it can display a list of properties matching the user's desired rent or offer advice on preferred floor plans. This enables the reception desk to respond flexibly to user needs, thereby improving user satisfaction.

[0066] The selection unit generates a list of optimal rental properties based on the information received by the reception unit. For example, the selection unit lists properties based on the user's desired conditions. For example, the selection unit lists properties that match the user's desired rent. The selection unit can also list properties that match the user's desired floor plan. Furthermore, the selection unit can list properties that match the user's desired location. For example, the selection unit uses an algorithm to list properties that match the user's desired rent. The selection unit can use an algorithm to list properties that match the user's desired rent. The selection unit also uses an algorithm to list properties that match the user's desired floor plan. Furthermore, the selection unit uses an algorithm to list properties that match the user's desired location. By using these algorithms, the selection unit can quickly list the properties that best suit the user's desired conditions. Furthermore, the selection unit can prioritize properties based on the user's desired conditions. For example, it can prioritize listing the most suitable properties by considering the importance of rent, floor plan, and location. This allows the selection unit to efficiently select properties that best suit the user's needs. The selection unit can also filter properties based on the user's desired conditions. For example, it can generate a more detailed property list by adding conditions such as a specific area, distance from the station, or age of the building. This allows the selection unit to respond to the user's specific needs and provide the most suitable properties.

[0067] The analytics department analyzes users' lifestyle data. For example, the analytics department analyzes users' hobbies and daily behavior patterns. For example, the analytics department collects data to analyze users' hobbies. The analytics department can collect data and analyze users' hobbies. The analytics department also collects data to analyze users' daily behavior patterns. The analytics department can collect data and analyze users' daily behavior patterns. Furthermore, the analytics department can comprehensively analyze users' lifestyle data. The analytics department can comprehensively analyze users' hobbies and daily behavior patterns and propose the most suitable areas and properties. The analytics department utilizes various data sources to collect users' lifestyle data. For example, it collects the content of users' social media posts and the usage history of applications used by users to understand users' hobbies and interests. It also collects data such as users' travel history and purchase history to analyze daily behavior patterns. As a result, the analytics department can understand users' lifestyles in detail and propose the most suitable properties. Furthermore, the analytics department uses AI to analyze the collected data and identify properties that are best suited to the user's lifestyle. For example, AI learns users' hobbies and behavioral patterns, identifying the characteristics of areas and properties that users prefer. This allows the analytics department to suggest properties that are best suited to the user's lifestyle. Furthermore, the analytics department can predict future needs based on users' lifestyle data. For instance, it can predict and suggest the conditions of properties that will be needed in the future in response to changes in the user's lifestyle. This allows the analytics department to address users' long-term needs and provide them with the most suitable properties.

[0068] The provisioning unit provides information based on data obtained by the analysis unit. For example, the provisioning unit provides users with detailed property information. For example, the provisioning unit provides an interface for providing detailed property information. The provisioning unit can provide users with detailed property information through this interface. The provisioning unit can also provide users with information about the surrounding environment. For example, the provisioning unit provides an interface for providing information about the surrounding environment. The provisioning unit can provide users with information about the surrounding environment through this interface. Furthermore, the provisioning unit can also provide users with the latest market information. For example, the provisioning unit provides an interface for providing the latest market information. The provisioning unit can provide users with the latest market information through this interface. The provisioning unit provides a visually easy-to-understand interface so that users can easily view detailed property information. For example, it displays property photos, floor plans, and detailed information about facilities so that users can intuitively understand the features of the property. The provisioning unit also provides information about the surrounding environment of the property. For example, it provides information about the nearest train station or bus stop, supermarkets, hospitals, and other facilities necessary for daily life so that users can understand the convenience of the property. Furthermore, the service provider will provide users with the latest market information to help them select properties. For example, they will provide information on rental market trends and property price fluctuations, enabling users to select properties at the optimal time. In this way, the service provider can provide users with comprehensive information and support them in their property selection.

[0069] The support department assists with contract procedures and moving arrangements. For example, the support department provides assistance in drafting contracts. For example, it provides an interface for drafting contracts. Through this interface, the support department can provide users with assistance in drafting contracts. The support department can also arrange for moving companies. For example, it provides an interface for arranging moving companies. Through this interface, the support department can provide users with assistance in arranging moving companies. The support department provides detailed guidelines regarding contract procedures to help users proceed smoothly. For example, it provides advice on how to fill out contracts and prepare necessary documents, ensuring users can complete the contract process without anxiety. In addition to arranging moving companies, the support department also assists with various procedures related to moving. For example, it provides support for changing contracts for utilities such as electricity, gas, and water, and various procedures related to changing addresses. This allows the support department to provide comprehensive support so that users can smoothly start their new lives. Furthermore, the support department responds quickly to user inquiries and provides necessary information. For example, by providing expert advice on contract details or moving-related inquiries, the support department can alleviate user concerns. This allows the support department to provide users with a sense of security and improve their satisfaction.

[0070] The reception desk 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 desk can provide a simple interface and minimize the input steps. For example, if the user is stressed, the reception desk can display only simple input fields to allow for quick input of desired conditions. Also, if the user is relaxed, the reception desk can provide detailed input options and suggest a customizable input method. For example, if the user is relaxed, the reception desk can display detailed input fields and allow the user to customize them as they see fit. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of desired conditions. For example, if the user is in a hurry, the reception desk can prioritize voice input and convert what the user dictates into text for input. This allows for more appropriate input 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 facial expression data into a generating AI and have the generating AI perform emotion estimation.

[0071] The reception desk can analyze the user's past input history of desired conditions and provide the optimal input interface. For example, the reception desk can automatically display as suggestions the user has frequently entered in the past. For example, the reception desk can automatically display the most frequently entered conditions based on the user's past input. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, if the reception desk has used voice input in the past, it will prioritize suggesting voice input. The reception desk can also predict and suggest desired conditions to be used during specific time periods based on the user's past input history. For example, based on the desired conditions the user has entered during a specific time period in the past, the reception desk will suggest similar conditions for the same time period. In this way, by analyzing past input history, the reception desk can provide the user with the optimal input interface. 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 past input history data into a generating AI and have the generating AI suggest the optimal input interface.

[0072] The reception desk can customize input fields based on the user's current living situation and areas of interest when they enter their desired conditions. For example, when the user enters their current living situation, the reception desk can automatically display relevant input fields based on their areas of interest. For example, when the user enters their family structure, the reception desk can automatically display input fields related to family structure. The reception desk can also prioritize property information related to specific areas of interest if the user has such areas. For example, if the user owns a pet, the reception desk can prioritize property information that allows pets. The reception desk can also customize necessary input fields based on the user's current living situation to reduce the effort required for input. For example, if the user is single, the reception desk can prioritize property information for single people. This reduces the effort required for input by customizing input fields according to the user's 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 the user's living situation data into a generating AI and have the generating AI perform the customization of input fields.

[0073] The reception desk can estimate the user's emotions and prioritize the entered preferences based on those emotions. For example, if the user is stressed, the reception desk may prompt them to prioritize important preferences such as rent and floor plan. If the user is relaxed, the reception desk may prompt them to prioritize detailed preferences such as surrounding environment and facilities. If the user is in a hurry, the reception desk may prompt them to prioritize the most important preferences such as rent and location. This allows the system to prioritize important preferences based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI 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 facial expression data into a generating AI and have the generating AI perform emotion estimation.

[0074] The reception desk can prioritize the input of highly relevant conditions when users enter their desired conditions, taking into account their geographical location. For example, if a user lives in a specific area, the reception desk can prioritize the input of desired conditions related to that area. For example, if a user lives in a specific area, the reception desk can prioritize the input of desired conditions related to the average rent and surrounding facilities in that area. The reception desk can also prioritize the input of desired conditions related to a specific area if the user plans to move to that area. For example, if a user plans to move to a specific area, the reception desk can prioritize the input of desired conditions related to the transportation access and school district in that area. Furthermore, the reception desk can automatically display highly relevant conditions based on the user's current geographical location. For example, the reception desk can automatically display information on nearby properties and desired conditions related to the surrounding environment based on the user's current geographical location. This allows for more appropriate property selection by prioritizing the input of highly relevant conditions based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's geographical location information into the generating AI and have the AI ​​prioritize inputting the most relevant conditions.

[0075] The reception desk can analyze the user's social media activity when they input their desired conditions and suggest relevant conditions. For example, the reception desk can suggest property types that the user is interested in based on their social media activity. The reception desk can also suggest desired conditions related to specific regions based on the user's social media activity. For example, the reception desk can suggest desired conditions related to regions that the user is interested in based on their social media activity. The reception desk can also analyze the user's social media activity and automatically display relevant desired conditions. For example, the reception desk can analyze the user's social media activity and automatically display conditions that the user is interested in. This allows the reception desk to suggest relevant conditions 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 using AI. For example, the reception desk can input the user's social media data into a generating AI and have the generating AI suggest relevant conditions.

[0076] The selection unit can estimate the user's emotions and adjust how the property list is displayed based on the estimated emotions. For example, if the user is stressed, the selection unit can display a simple property list. For example, if the user is stressed, the selection unit can display only basic property information to allow the user to quickly select a property. The selection unit can also display a list with detailed property information if the user is relaxed. For example, if the user is relaxed, the selection unit can display detailed property information to allow the user to freely select a property. The selection unit can also prioritize displaying important property information if the user is in a hurry. For example, if the selection unit prioritizes displaying important property information such as rent and location if the user is in a hurry. In this way, by adjusting how the property list is displayed according to the user's emotions, more appropriate property information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input user facial expression data into a generating AI and have the generating AI perform emotion estimation.

[0077] The selection unit can adjust the level of detail in the property list based on the importance of the properties when generating the property list. For example, the selection unit can display high-importance properties in detail and low-importance properties in a simplified manner. For example, the selection unit can display detailed property information for high-importance properties and simplified property information for low-importance properties. The selection unit can also adjust the display order of the list according to the importance of the properties. For example, the selection unit can display high-importance properties at the top of the list and low-importance properties at the bottom. The selection unit can also prioritize the display of high-importance properties to attract the user's attention. For example, the selection unit can display high-importance properties at the beginning of the list to attract the user's attention. In this way, by adjusting the level of detail in the list according to the importance of the properties, it is possible to provide property information that will attract the user's attention. Some or all of the above processing in the selection unit may be performed using AI, for example, or not using AI. For example, the selection unit can input property importance data into a generation AI and have the generation AI perform the adjustment of the level of detail in the list.

[0078] The selection unit can apply different selection algorithms depending on the property category when generating the property list. For example, for family-oriented properties, the selection unit can apply an algorithm that takes into account family structure and proximity to schools. For example, for family-oriented properties, the selection unit can apply an algorithm that takes into account family structure and proximity to schools to select the most suitable property. The selection unit can also apply an algorithm that takes into account commuting convenience and surrounding facilities for single-person properties. For example, for single-person properties, the selection unit can apply an algorithm that takes into account commuting convenience and surrounding facilities to select the most suitable property. The selection unit can also apply an algorithm that takes into account barrier-free access and proximity to medical facilities for elderly-oriented properties. For example, for elderly-oriented properties, the selection unit can apply an algorithm that takes into account barrier-free access and proximity to medical facilities to select the most suitable property. In this way, by applying different selection algorithms depending on the property category, property information that meets the user's needs can be provided. Some or all of the above processing in the selection unit may be performed using AI, for example, or without using AI. For example, the selection unit can input property category data into the generation AI and have the generation AI execute the application of the selection algorithm.

[0079] The selection unit can estimate the user's emotions and adjust the length of the property list based on the estimated emotions. For example, if the user is stressed, the selection unit can display a shorter property list. For example, if the user is stressed, the selection unit can display only important properties to allow for quick property selection. The selection unit can also display a longer property list if the user is relaxed. For example, if the user is relaxed, the selection unit can display a longer property list that includes detailed property information. The selection unit can also display only important properties if the user is in a hurry. For example, if the user is in a hurry, the selection unit can prioritize displaying important property information such as rent and location. By adjusting the length of the property list according to the user's emotions, more appropriate property information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input user facial expression data into a generating AI and have the generating AI perform emotion estimation.

[0080] The selection unit can determine the priority of a property list based on the property registration date when generating the property list. For example, the selection unit can prioritize displaying newly registered properties. For example, the selection unit can display properties with newer registration dates at the top of the list. The selection unit can also postpone displaying older properties. For example, the selection unit can display older properties at the bottom of the list. The selection unit can also adjust the display order of the property list according to the registration date. For example, the selection unit can adjust the display order of the list according to the property registration date, prioritizing the display of newer properties. This allows for the priority of providing new property information by determining the list priority based on the property registration date. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input property registration date data into a generation AI and have the generation AI determine the list priority.

[0081] The selection unit can adjust the order of properties in a list based on their relevance when generating the property list. For example, the selection unit can prioritize displaying properties that are most relevant to the user's desired conditions. For example, the selection unit can display properties most relevant to the user's desired conditions at the top of the list. The selection unit can also postpone displaying less relevant properties. For example, the selection unit can display less relevant properties at the bottom of the list. The selection unit can also adjust the display order of the list according to the relevance of the properties. For example, the selection unit can adjust the display order of the list according to the relevance of the properties, prioritizing the display of highly relevant properties. In this way, by adjusting the order of the list based on the relevance of the properties, the selection unit can prioritize providing properties that are most relevant to the user's desired conditions. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input property relevance data into a generation AI and have the generation AI perform the adjustment of the list order.

[0082] The analysis unit can estimate the user's emotions and adjust the lifestyle data analysis method based on the estimated user emotions. For example, if the user is stressed, the analysis unit can apply a simple analysis method. For example, if the user is stressed, the analysis unit can perform a simple data analysis and provide results quickly. The analysis unit can also apply a detailed analysis method if the user is relaxed. For example, if the user is relaxed, the analysis unit can perform a detailed data analysis and provide the user with detailed analysis results. The analysis unit can also provide analysis results quickly if the user is in a hurry. For example, if the user is in a hurry, the analysis unit can perform a simple data analysis and provide results quickly. In this way, by adjusting the lifestyle data analysis method according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user facial expression data into a generating AI and have the generating AI perform emotion estimation.

[0083] The analysis unit can optimize its analysis algorithm by referring to past data when analyzing lifestyle data. For example, the analysis unit can select the optimal analysis algorithm based on past lifestyle data. For example, the analysis unit can select the optimal analysis algorithm by referring to past lifestyle data. The analysis unit can also improve the accuracy of the analysis results by referring to past data. For example, the analysis unit can improve the accuracy of the analysis results by referring to past data. The analysis unit can also perform analysis that is optimal for the user's lifestyle by utilizing past data. For example, the analysis unit can perform analysis that is optimal for the user's lifestyle by utilizing past data. In this way, the accuracy of the analysis results can be improved by optimizing the analysis algorithm by referring to past data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input past data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0084] The analysis unit can perform lifestyle data analysis while considering user attribute information. For example, the analysis unit can analyze lifestyle data while considering the user's age and gender. The analysis unit can also analyze lifestyle data while considering the user's occupation and income. The analysis unit can also analyze lifestyle data while considering the user's family structure and hobbies. By considering user attribute information during analysis, more appropriate analysis results 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 user attribute information into a generating AI and have the generating AI perform the analysis.

[0085] The analysis unit can estimate the user's emotions and adjust how the analysis results are displayed based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide a simple display method. For example, if the user is stressed, the analysis unit can display only a brief analysis result to provide information quickly. The analysis unit can also provide a detailed display method if the user is relaxed. For example, if the user is relaxed, the analysis unit can display a detailed analysis result, allowing the user to freely review the information. The analysis unit can also provide a concise display method if the user is in a hurry. For example, if the user is in a hurry, the analysis unit can display only the important information to provide information quickly. In this way, by adjusting how the analysis results are displayed according to the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user facial expression data into a generating AI and have the generating AI perform emotion estimation.

[0086] The analysis unit can perform lifestyle data analysis while considering the geographical distribution of users. For example, the analysis unit can analyze lifestyle data while considering the user's residential area. The analysis unit can also analyze lifestyle data while considering the user's commuting route. For example, the analysis unit can analyze lifestyle data while considering the user's commuting route. The analysis unit can also perform optimal analysis based on the geographical distribution of users. For example, the analysis unit can perform optimal analysis based on the geographical distribution of users. By considering the geographical distribution of users, more appropriate analysis results 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 user geographical distribution data into a generating AI and have the generating AI perform the analysis.

[0087] The analysis unit can improve the accuracy of its analysis by referring to relevant literature when analyzing lifestyle data. For example, the analysis unit can optimize its lifestyle data analysis method by referring to relevant literature. The analysis unit can also improve the accuracy of its analysis results based on relevant literature. The analysis unit can also utilize relevant literature to perform an analysis that is best suited to the user's lifestyle. This improves the accuracy of lifestyle data analysis by referring to relevant literature. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevant literature data into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0088] The information provider can estimate the user's emotions and adjust the method of information delivery based on the estimated emotions. For example, if the user is stressed, the information provider can apply a simple method of information delivery. For example, if the user is stressed, the information provider can provide only simple information and deliver it quickly. The information provider can also apply a detailed method of information delivery if the user is relaxed. For example, if the user is relaxed, the information provider can provide detailed information and allow the user to review the information at their own pace. The information provider can also deliver information quickly if the user is in a hurry. For example, if the user is in a hurry, the information provider can provide only important information and deliver it quickly. In this way, by adjusting the method of information delivery according to the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the service provider can input user facial expression data into a generating AI and have the AI ​​perform emotion estimation.

[0089] The information provider can provide optimal information by referring to the user's past behavior history when providing information. For example, the information provider can provide optimal property information based on the user's past behavior history. For example, the information provider can refer to the user's past behavior history and provide optimal property information. The information provider can also prioritize providing relevant information from the user's past behavior history. For example, the information provider can refer to the user's past behavior history and prioritize providing relevant information. The information provider can also analyze the user's past behavior history and provide the most suitable information. For example, the information provider can analyze the user's past behavior history and provide the most suitable information. In this way, by referring to past behavior history, the information provider can provide the user with optimal information. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the user's past behavior history data into a generating AI and have the generating AI perform the task of providing optimal information.

[0090] The information provider can estimate the user's emotions and determine the priority of information provision based on the estimated emotions. For example, if the user is stressed, the information provider will prioritize providing important information. For example, if the user is stressed, the information provider will prioritize providing important property information. The information provider can also provide detailed information if the user is relaxed. For example, if the user is relaxed, the information provider will provide detailed property information. The information provider can also quickly provide important information if the user is in a hurry. For example, if the user is in a hurry, the information provider will quickly provide important property information. In this way, by determining the priority of information provision according to the user's emotions, important information can be provided preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the service provider can input user facial expression data into a generating AI and have the AI ​​perform emotion estimation.

[0091] The information provider can provide optimal information by considering the user's geographical location when providing information. For example, the information provider can provide optimal property information based on the user's current location. The information provider can also prioritize providing relevant information by considering the user's geographical location. The information provider can also customize and provide information according to the user's current location. By doing so, more appropriate property information can be provided by providing optimal information based on the user's geographical location. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the user's geographical location data into a generating AI and have the generating AI perform the task of providing optimal information.

[0092] The information provider can provide relevant information by analyzing the user's social media activity when providing information. For example, the information provider can provide property information that the user is interested in based on the user's social media activity. The information provider can also prioritize providing relevant information based on the user's social media activity. The information provider can also analyze the user's social media activity and provide the most appropriate information. In this way, relevant information can be provided by analyzing the user's social media activity. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the user's social media data into a generating AI and have the generating AI perform the provision of relevant information.

[0093] The support unit can estimate the user's emotions and adjust its support methods based on the estimated emotions. For example, if the user is stressed, the support unit can provide simple support. For example, if the user is stressed, the support unit can provide only simple support and provide it quickly. The support unit can also provide detailed support if the user is relaxed. For example, if the user is relaxed, the support unit can provide detailed support and allow the user to receive support freely. The support unit can also provide quick support if the user is in a hurry. For example, if the user is in a hurry, the support unit can provide only essential support and provide it quickly. In this way, by adjusting the support methods according to the user's emotions, more appropriate support can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input user facial expression data into a generating AI and have the AI ​​perform emotion estimation.

[0094] The support unit can provide optimal support by referring to the user's past support history during support. For example, the support unit can provide the optimal support method based on the user's past support history. For example, the support unit can refer to the user's past support history and provide the optimal support method. The support unit can also prioritize providing relevant support based on the user's past support history. For example, the support unit can refer to the user's past support history and prioritize providing relevant support. The support unit can also analyze the user's past support history and provide the most appropriate support. For example, the support unit can analyze the user's past support history and provide the most appropriate support. In this way, by referring to past support history, the support unit can provide the user with the most suitable support. Some or all of the above processes in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input the user's past support history data into a generating AI and have the generating AI perform the task of providing optimal support.

[0095] The support unit can customize the support provided based on the user's current living situation. For example, the support unit can provide the most appropriate support based on the user's current living situation. For example, the support unit can refer to the user's current living situation and provide the most appropriate support. The support unit can also prioritize providing relevant support according to the user's current living situation. For example, the support unit can refer to the user's current living situation and prioritize providing relevant support. The support unit can also customize and provide support considering the user's current living situation. For example, the support unit can customize and provide support considering the user's current living situation. This allows for the provision of more appropriate support by customizing support based on the user's current living situation. Some or all of the above processes in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's current living situation data into a generating AI and have the generating AI perform the customization of the support content.

[0096] The support unit can estimate the user's emotions and determine the priority of support based on the estimated emotions. For example, if the user is stressed, the support unit will prioritize providing important support. The support unit can also provide detailed support if the user is relaxed. The support unit can also provide quick and important support if the user is in a hurry. This allows for the priority of important support by determining the priority of support according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support unit may be performed using AI or not using AI. For example, the support unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0097] The support unit can provide optimal support by considering the user's geographical location information during support. For example, the support unit can provide optimal support based on the user's current location. The support unit can also prioritize providing relevant support by considering the user's geographical location information. The support unit can also customize and provide support content according to the user's current location. This allows for more appropriate support to be provided by providing optimal support based on the user's geographical location information. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's geographical location information data into a generating AI and have the generating AI perform the provision of optimal support.

[0098] The support unit can analyze the user's social media activity and provide relevant support during support sessions. For example, the support unit can provide support that the user is interested in based on their social media activity. The support unit can also prioritize providing relevant support based on the user's social media activity. The support unit can also analyze the user's social media activity and provide the most appropriate support. In this way, relevant support can be provided by analyzing the user's social media activity. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's social media data into a generating AI and have the generating AI perform the provision of relevant support.

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

[0100] The rental apartment suggestion system can estimate the user's emotions and adjust the property suggestion method based on the estimated emotions. For example, if the user is stressed, a simple property list can be displayed to allow for quick selection. If the user is relaxed, a list with detailed property information can be displayed to allow the user to freely select a property. Furthermore, if the user is in a hurry, important property information can be prioritized. In this way, by adjusting the property suggestion method according to the user's emotions, more appropriate property information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the selection unit may be performed using AI, or not using AI. For example, the selection unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0101] The rental apartment suggestion system can analyze a user's past property selection history and make optimal property suggestions. For example, it can prioritize suggesting properties with similar characteristics based on the features of properties the user has previously selected. It can also consider the area and rent range of properties the user has previously selected and suggest properties with similar conditions. Furthermore, it can analyze the trends of properties selected at specific time periods from the user's past selection history and suggest similar properties at the same time period. In this way, by analyzing past selection history, the system can make optimal property suggestions to the user. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input past selection history data into a generating AI and have the generating AI execute optimal property suggestions.

[0102] The rental apartment suggestion system can customize property suggestions based on the user's current living situation. For example, when a user enters their family structure, the system prioritizes suggesting properties related to that family structure. Furthermore, if a user has a specific area of ​​interest, the system can prioritize suggesting properties related to that area. In addition, it can customize the necessary property information based on the user's current living situation, reducing the effort required for suggestions. This reduces the effort involved in suggesting properties by customizing them according to the user's living situation and areas of interest. Some or all of the above-described processes in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the user's living situation data into a generating AI and have the generating AI perform the customization of property suggestions.

[0103] The rental apartment suggestion system can estimate the user's emotions and prioritize properties based on those emotions. For example, if the user is stressed, it can prioritize suggesting important properties. If the user is relaxed, it can also suggest properties with detailed information. Furthermore, if the user is in a hurry, it can prioritize suggesting the most important property information. In this way, by prioritizing properties according to the user's emotions, it can prioritize suggesting important properties. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the selection unit may be performed using AI, for example, or not using AI. For example, the selection unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0104] The rental apartment suggestion system can suggest properties while considering the user's geographical location. For example, if the user lives in a specific area, it can prioritize suggesting properties related to that area. Similarly, if the user plans to move to a specific area, it can prioritize suggesting properties related to that area. Furthermore, it can automatically display highly relevant property information based on the user's current geographical location. This allows for more appropriate property selection by prioritizing the suggestion of highly relevant properties based on the user's geographical location. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For instance, the selection unit can input the user's geographical location information into a generating AI, causing the AI ​​to prioritize suggesting highly relevant property information.

[0105] The rental apartment suggestion system can analyze a user's social media activity and suggest relevant property information. For example, it can suggest property types of interest based on the user's social media activity. It can also suggest property information related to a specific area based on the user's social media activity. Furthermore, it can analyze the user's social media activity and automatically display relevant property information. In this way, relevant property information can be suggested by analyzing the user's social media activity. Some or all of the above processing in the selection unit may be performed using AI, for example, or not using AI. For example, the selection unit can input the user's social media data into a generating AI and have the generating AI perform the task of suggesting relevant property information.

[0106] The rental apartment suggestion system can estimate the user's emotions and adjust how the property list is displayed based on the estimated emotions. For example, if the user is stressed, a simple property list can be displayed. If the user is relaxed, a list with detailed property information can be displayed. Furthermore, if the user is in a hurry, important property information can be prioritized. In this way, by adjusting how the property list is displayed according to the user's emotions, more appropriate property information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the selection unit may be performed using AI, for example, or not using AI. For example, the selection unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0107] The rental apartment suggestion system can adjust the level of detail in the property list based on the importance of each property when generating the property list. For example, it can display highly important properties in detail and less important properties in a simplified manner. It can also adjust the display order of the list according to the importance of each property. Furthermore, it can prioritize the display of highly important properties to attract the user's attention. In this way, by adjusting the level of detail in the list according to the importance of each property, it is possible to provide property information that will attract the user's attention. Some or all of the above processing in the selection unit may be performed using AI, for example, or not using AI. For example, the selection unit can input property importance data into a generation AI and have the generation AI perform the adjustment of the level of detail in the list.

[0108] The rental apartment suggestion system can apply different selection algorithms depending on the property category when generating a property list. For example, for family-friendly properties, an algorithm that considers family structure and proximity to schools can be applied. For single-person properties, an algorithm that considers commuting convenience and surrounding facilities can be applied. Furthermore, for properties for the elderly, an algorithm that considers barrier-free access and proximity to medical facilities can be applied. By applying different selection algorithms according to the property category, the system can provide property information that meets the user's needs. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input property category data into a generation AI and have the generation AI execute the application of the selection algorithm.

[0109] The rental apartment suggestion system can estimate the user's emotions and adjust the length of the property list based on the estimated emotions. For example, if the user is stressed, a shorter property list can be displayed. Conversely, if the user is relaxed, a longer property list can be displayed. Furthermore, if the user is in a hurry, only important properties can be displayed. This allows for the provision of more appropriate property information by adjusting the length of the property list according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the selection unit may be performed using AI, or not using AI. For example, the selection unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

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

[0111] Step 1: The reception desk inputs the user's desired conditions. These conditions include rent, floor plan, and location. The reception desk provides an interface for the user to input their desired rent, floor plan, and location conditions, and the user can input these conditions through this interface. Step 2: The selection unit generates a list of optimal rental properties based on the information received by the reception unit. The selection unit uses an algorithm to list properties that match the user's desired conditions, such as rent, floor plan, and location. Step 3: The analytics department analyzes user lifestyle data. The analytics department analyzes users' hobbies and daily behavior patterns, collects data to suggest the most suitable areas and properties, and performs a comprehensive analysis. Step 4: The provision department provides information based on the data obtained by the analysis department. The provision department provides users with an interface to provide them with detailed property information, information on the surrounding environment, and the latest market information, and users can obtain this information through the interface. Step 5: The support department assists with contract procedures and moving arrangements. The support department provides an interface for assisting with contract creation and arranging moving companies, and users can receive this support through the interface.

[0112] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

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

[0115] Each of the multiple elements described above, including the reception unit, selection unit, analysis unit, provision unit, and support unit, is implemented by, for example, 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 and provides an interface for the user to input desired rent, floor plan, and location conditions. The selection unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates a list of optimal rental properties based on the user's desired conditions. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the user's lifestyle data. The provision unit is implemented by, for example, the control unit 46A of the smart device 14 and provides detailed property information and information on the surrounding environment. The support unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and supports contract procedures and moving arrangements. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

[0117] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0118] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0119] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0120] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0122] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0123] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0124] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0125] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

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

[0128] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

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

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

[0131] Each of the multiple elements described above, including the reception unit, selection unit, analysis unit, provision unit, and support unit, is implemented, for example, in 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 and provides an interface for the user to input desired rent, floor plan, and location conditions. The selection unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates a list of optimal rental properties based on the user's desired conditions. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the user's lifestyle data. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214 and provides detailed property information and information on the surrounding environment. The support unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and supports contract procedures and moving arrangements. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

[0133] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0134] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0135] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0136] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0138] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0139] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0140] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0141] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

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

[0144] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

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

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

[0147] Each of the multiple elements described above, including the reception unit, selection unit, analysis unit, provision unit, and support unit, is implemented by, for example, 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 and provides an interface for the user to input desired rent, floor plan, and location conditions. The selection unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates a list of optimal rental properties based on the user's desired conditions. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the user's lifestyle data. The provision unit is implemented by, for example, the control unit 46A of the headset terminal 314 and provides detailed property information and information on the surrounding environment. The support unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and supports contract procedures and moving arrangements. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

[0149] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0150] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0151] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0152] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0153] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0154] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0155] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0156] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0157] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0158] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

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

[0161] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

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

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

[0164] Each of the multiple elements described above, including the reception unit, selection unit, analysis unit, provision unit, and support 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 provides an interface for the user to input desired rent, floor plan, and location conditions. The selection unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates a list of optimal rental properties based on the user's desired conditions. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the user's lifestyle data. The provision unit is implemented by, for example, the control unit 46A of the robot 414 and provides detailed property information and information on the surrounding environment. The support unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and supports contract procedures and moving arrangements. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0165] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0166] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0167] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0168] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0169] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0170] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0171] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0172] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0173] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0174] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0175] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0176] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0177] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0178] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0179] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

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

[0181] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0182] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0183] (Note 1) A reception desk where users enter their desired conditions, A selection unit generates a list of optimal rental properties based on the information received by the reception unit, The analytics department analyzes user lifestyle data, A providing unit that provides information based on the data obtained by the analysis unit, It includes a support department that assists with contract procedures and moving arrangements. A system characterized by the following features. (Note 2) 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 3) The aforementioned reception unit is By analyzing past user preference input history, we provide the optimal input interface. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is When users enter their desired conditions, the input fields are customized based on their current living situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is It estimates the user's emotions and determines the priority of the entered preferences based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) 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 7) The aforementioned reception unit is When you enter your desired criteria, the system analyzes your social media activity and suggests relevant criteria. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned selection 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 9) The aforementioned selection unit is When generating a property list, adjust the level of detail in the list based on the importance of each property. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned selection unit is When generating a property list, different selection algorithms are applied depending on the property category. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned selection unit is The system estimates the user's emotions and adjusts the length of the property listing based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned selection unit is When generating a property list, the priority of the list is determined based on when the properties were registered. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned selection unit is When generating a property list, adjust the order of the list based on the relevance of the properties. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is We estimate the user's emotions and adjust the lifestyle data analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is When analyzing lifestyle data, we optimize the analysis algorithm by referring to past data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is When analyzing lifestyle data, the analysis should take into account user attribute information. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is When analyzing lifestyle data, the analysis should take into account the geographical distribution of users. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit is When analyzing lifestyle data, referencing relevant literature improves the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, It estimates the user's emotions and adjusts the way information is provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing information, we refer to the user's past behavioral history to provide the most relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, The system estimates the user's emotions and prioritizes information provision based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing information, we will consider the user's geographical location to provide the most relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing information, we analyze the user's social media activity and provide relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned support unit is It estimates the user's emotions and adjusts the support method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned support unit is When providing support, we refer to the user's past support history to provide the most appropriate support. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned support unit is During support sessions, customize the support content based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned support unit is The system estimates the user's emotions and determines support priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned support unit is When providing support, we take the user's geographical location into consideration to provide the most appropriate support. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned support unit is During support, we analyze the user's social media activity to provide relevant support. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

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

Claims

1. A reception desk where users enter their desired conditions, A selection unit generates a list of optimal rental properties based on the information received by the reception unit, The analytics department analyzes user lifestyle data, A providing unit that provides information based on the data obtained by the analysis unit, It includes a support department that assists with contract procedures and moving arrangements. A system characterized by the following features.

2. 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.

3. The aforementioned reception unit is By analyzing past user preference input history, we provide the optimal input interface. The system according to feature 1.

4. The aforementioned reception unit is When users enter their desired conditions, the input fields are customized based on their current living situation and areas of interest. The system according to feature 1.

5. The aforementioned reception unit is It estimates the user's emotions and determines the priority of the entered preferences based on the estimated user emotions. The system according to feature 1.

6. The aforementioned reception unit is When users enter their desired conditions, the system prioritizes highly relevant conditions by considering their geographical location. The system according to feature 1.

7. The aforementioned reception unit is When you enter your desired criteria, the system analyzes your social media activity and suggests relevant criteria. The system according to feature 1.

8. The aforementioned selection unit is The system estimates the user's emotions and adjusts how the property listings are displayed based on those estimated emotions. The system according to feature 1.

9. The aforementioned selection unit is When generating a property list, adjust the level of detail in the list based on the importance of each property. The system according to feature 1.

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