system

Abstract

The system according to this embodiment aims to streamline the operations of real estate companies and enable prospective tenants to find their ideal property. [Solution] The system according to the embodiment comprises a collection unit, an organization unit, a proposal unit, and a contract unit. The collection unit automatically collects property information. The organization unit organizes the property information collected by the collection unit. The proposal unit proposes properties to customers based on the property information organized by the organization unit. The contract unit supports contracts based on the properties proposed by the proposal unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the load of the sales and contract operations of real estate companies is large, and it takes time for potential residents to find an ideal property.

[0005] The system according to the embodiment aims to streamline the operations of real estate companies and enable potential residents to find an ideal property.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an organization unit, a proposal unit, and a contract unit. The collection unit automatically collects property information. The organization unit organizes the property information collected by the collection unit. The proposal unit proposes properties to customers based on the property information organized by the organization unit. The contract unit supports contracts based on the properties proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment streamlines the operations of real estate companies and allows prospective tenants to find their ideal property. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls 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 reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[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 next-generation platform according to an embodiment of the present invention is "Ouchi Concierge," which solves challenges in the real estate industry using generative AI. This platform provides an agent AI that supports both real estate agent sales representatives and prospective tenants looking for properties, from property proposals to contract signing, using generative AI. The agent AI streamlines the operations of real estate companies 24 hours a day and helps prospective tenants find their ideal property. Through AI-driven dialogue and suggestions, it solves the challenges of both parties simultaneously. For example, this platform provides real estate companies with functions such as automatic collection and organization of property information, status management of customer response status, AI-driven business proposal notifications, automatic generation of explanation points during property viewings, 24 / 7 inquiry support, analysis and proposal of customers' potential needs, automatic scheduling of viewing appointments, prediction of the likelihood of closing a deal, analysis of market trends for similar properties, and determination of the optimal timing for proposals. Furthermore, for prospective tenants, the platform offers features such as personalized property search, automatic collection and organization of property information, property suggestions tailored to customer requests, automatic generation of explanation points for viewings, 24 / 7 inquiry support, analysis and suggestions for customers' potential needs, automatic scheduling of viewing appointments, detailed property information, decision support, prediction of closing probability, market trend analysis of similar properties, determination of optimal proposal timing, and interior layout through image generation. This platform will enable real estate companies to streamline their operations and make it easier for prospective tenants to find their ideal property. As a result, satisfaction for both parties will improve, and the entire real estate industry is expected to be revitalized. Thus, this next-generation platform can efficiently solve the challenges of the real estate industry and achieve operational efficiency and improved customer satisfaction.

[0029] The next-generation platform according to this embodiment comprises a collection unit, an organization unit, a proposal unit, and a contract unit. The collection unit automatically collects property information. The collection unit collects property information from the internet, for example, using web scraping technology. The collection unit can also obtain property information from real estate databases through API integration. Furthermore, the collection unit can receive data feeds from property information provision services. For example, the collection unit periodically collects property information from specific real estate portal sites using web scraping technology. It can also obtain the latest property information from real estate databases through API integration. By receiving data feeds from property information provision services, the collection unit can update property information in real time. The organization unit organizes the property information collected by the collection unit. The organization unit classifies property information into categories such as location, price, floor plan, and year built, for example. The organization unit can also store property information in a database and organize it to facilitate searching and filtering. Furthermore, the organization unit can perform data cleansing to eliminate duplication of property information and maintain consistency. For example, the organization department classifies property information by location and filters it by price range and floor plan. It stores the property information in a database and organizes it to facilitate searching and filtering. It performs data cleansing to eliminate duplicate property information and maintain consistency. The proposal department proposes properties to customers based on the property information organized by the organization department. For example, the proposal department proposes properties that meet customer needs. By collecting and analyzing customer needs, the proposal department can propose the most suitable properties. Furthermore, the proposal department can analyze customers' latent needs and propose the most suitable properties. For example, the proposal department collects and analyzes customer needs through surveys and interviews. Based on customer needs, it proposes the most suitable properties. It also analyzes customers' latent needs, considering their behavioral history and psychological factors, and proposes the most suitable properties. The contract department supports contracts based on the properties proposed by the proposal department. For example, the contract department supports the creation of contracts. The contract department can streamline contract creation by providing templates or using automated generation tools.Furthermore, the contracts department can manage the progress of contracts and provide appropriate support to customers. For example, the contracts department can provide contract templates, allowing customers to create contracts simply by entering the necessary information. Automatic generation tools can be used to streamline contract creation. By managing the progress of contracts and providing appropriate support to customers, the next-generation platform according to this embodiment can efficiently support everything from property information collection to contract signing.

[0030] The data collection unit automatically collects property information. For example, the data collection unit uses web scraping technology to collect property information from the internet. Web scraping technology is a technique that automatically extracts necessary information from specific websites, and the data collection unit uses this to periodically collect property information from real estate portal sites and real estate company websites. Specifically, the data collection unit analyzes the HTML structure of websites and extracts information such as the property's location, price, floor plan, and year of construction. The data collection unit can also obtain property information from real estate databases through API integration. API integration allows the data collection unit to obtain the latest property information in real time and reflect it in the database. Furthermore, the data collection unit can receive data feeds from property information provision services. A data feed is a flow of data provided by property information provision services, and by receiving this, the data collection unit can update property information in real time. For example, the data collection unit periodically collects property information from specific real estate portal sites and obtains the latest property information from real estate databases through API integration. By receiving data feeds from property information provision services, the data collection unit can update property information in real time. This allows the data collection unit to efficiently gather a wide range of property information from diverse sources and provide the data that will form the foundation of the next-generation platform.

[0031] The organization department organizes the property information collected by the collection department. For example, the organization department categorizes property information by location, price, floor plan, and year built. This allows users to quickly find the necessary information when searching for properties. The organization department can also store the property information in a database and organize it to facilitate searching and filtering. The property information stored in the database is indexed so that users can search using various criteria, enabling efficient searching. Furthermore, the organization department can perform data cleansing to eliminate duplicate property information and maintain consistency. Data cleansing is the process of detecting, correcting, or deleting duplicates and errors from the collected property information. For example, the organization department categorizes property information by location and filters it by price range and floor plan. It stores the property information in a database and organizes it to facilitate searching and filtering. It performs data cleansing to eliminate duplicate property information and maintain consistency. In this way, the organization department efficiently organizes the collected property information, allowing users to quickly and accurately obtain the information they need.

[0032] The proposal department proposes properties to customers based on property information organized by the organization department. For example, the proposal department proposes properties that meet the customer's requests. By collecting and analyzing customer requests, the proposal department can propose the most suitable properties. Customer requests are collected through surveys and interviews, and the proposal department uses this information to understand the customer's desired conditions. Furthermore, the proposal department can also analyze the customer's latent needs and propose the most suitable properties. Analyzing latent needs takes into account the customer's behavioral history and psychological factors. For example, the proposal department analyzes the customer's website browsing history and past inquiry history to identify properties that the customer might be interested in. It also analyzes the characteristics of properties that customers prefer, taking into account their psychological factors. As a result, the proposal department can not only propose the most suitable properties based on customer requests, but also propose properties that meet the customer's latent needs. For example, the proposal department collects and analyzes customer requests through surveys and interviews. Based on customer requests, it proposes the most suitable properties. By analyzing the customer's latent needs, taking into account their behavioral history and psychological factors, it proposes the most suitable properties. As a result, the proposal department can propose the most suitable properties to customers and improve customer satisfaction.

[0033] The Contracts Department supports contracts based on properties proposed by the Proposal Department. For example, the Contracts Department supports the creation of contract documents. The Contracts Department can streamline contract creation by providing templates and using automated generation tools. Templates provide common contract formats, allowing customers to create contracts simply by entering the necessary information. Automated generation tools automatically generate contracts based on customer input, streamlining the creation process. Furthermore, the Contracts Department can manage contract progress and provide appropriate support to customers. Contract progress management tracks each step from contract creation to signing and submission, supporting customers in smoothly completing the contract. For example, the Contracts Department provides contract templates, allowing customers to create contracts simply by entering the necessary information. Automated generation tools streamline contract creation. By managing contract progress and providing appropriate support to customers, the Contracts Department can support customers in smoothly completing the contract, improving the efficiency of the contract process and increasing customer satisfaction.

[0034] The proposal department can propose properties that meet customer needs. For example, the proposal department collects and analyzes customer needs through surveys and interviews. Based on customer needs, the proposal department proposes the most suitable properties. For example, the proposal department can collect the conditions of the property that the customer wants (location, price, floor plan, etc.) and propose properties based on that. In addition, the proposal department can make personalized property proposals based on the customer's lifestyle and hobbies and preferences. For example, if the customer has a pet, the proposal department will prioritize proposing pet-friendly properties. If the customer has children, they will propose properties within the school district. If the customer owns a car, they will propose properties with parking. In this way, the proposal department can propose properties that meet customer needs.

[0035] The proposal department can analyze customers' latent needs and suggest the most suitable properties. For example, it can analyze customers' browsing history and suggest properties that they might be interested in. It can also consider customers' psychological factors and suggest properties that they are likely to like. For example, it can analyze the characteristics of properties customers have viewed in the past and suggest similar properties. It can suggest properties with similar conditions based on the conditions of properties customers have inquired about in the past. It can prioritize suggesting properties that customers might be interested in based on their browsing history. This enables the proposal department to suggest the most suitable properties based on customers' latent needs.

[0036] The contracts department can support the creation of contracts. For example, the contracts department can provide contract templates, allowing customers to create contracts simply by entering the necessary information. The contracts department can also streamline contract creation by using automated generation tools. For example, the contracts department can provide contract templates, allowing customers to create contracts simply by entering the necessary information. The contracts department streamlines contract creation by using automated generation tools. The contracts department automates the contract creation process, allowing customers to create contracts quickly. In this way, the contracts department can efficiently support contract creation.

[0037] The contracts department can manage the progress of contracts. For example, the contracts department can provide tools to visualize contract progress, allowing customers to grasp the progress at a glance. The contracts department can also use notification functions to inform customers about contract progress. For example, the contracts department can provide tools to visualize contract progress, allowing customers to grasp the progress at a glance. The contracts department can use notification functions to inform customers about contract progress. The contracts department updates contract progress in real time, ensuring customers always have the latest information. This allows the contracts department to efficiently manage contract progress.

[0038] The data collection unit can analyze the user's past property viewing history and select the optimal collection method when collecting property information. For example, the data collection unit can analyze the characteristics of properties the user has previously viewed and prioritize collecting similar property information. The data collection unit can also analyze the price range of properties the user has previously viewed and collect property information in the same price range. The data collection unit can also analyze the location conditions of properties the user has previously viewed and collect property information in the same area. For example, the data collection unit can analyze the characteristics of properties the user has previously viewed and prioritize collecting similar property information. The data collection unit can analyze the price range of properties the user has previously viewed and collect property information in the same price range. The data collection unit can analyze the location conditions of properties the user has previously viewed and collect property information in the same area. In this way, the data collection unit can select the optimal collection method by analyzing the user's past property viewing history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's property viewing history data into a generating AI and have the generating AI select the optimal collection method.

[0039] The data collection unit can filter property information based on the user's current living situation and areas of interest. For example, if the user owns a pet, the data collection unit will prioritize collecting pet-friendly properties. If the user has children, the data collection unit can also prioritize collecting properties within a school district. If the user owns a car, the data collection unit can also prioritize collecting properties with parking spaces. For example, if the user owns a pet, the data collection unit will prioritize collecting pet-friendly properties. If the user has children, the data collection unit will prioritize collecting properties within a school district. If the user owns a car, the data collection unit will prioritize collecting properties with parking spaces. This allows the data collection unit to collect more appropriate property information by filtering property information based on the user's current living situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input data on the user's living situation and areas of interest into a generating AI and have the generating AI perform the filtering.

[0040] The data collection unit can prioritize collecting highly relevant property information by considering the user's geographical location. For example, the data collection unit can prioritize collecting property information close to the user's current location. The data collection unit can also prioritize collecting property information along the user's commute route. The data collection unit can also prioritize collecting property information in areas the user frequently visits. For example, the data collection unit can prioritize collecting property information close to the user's current location. The data collection unit can prioritize collecting property information along the user's commute route. The data collection unit can prioritize collecting property information in areas the user frequently visits. In this way, the data collection unit can prioritize collecting highly relevant property information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location data into a generating AI and have the generating AI perform the collection of highly relevant property information.

[0041] The data collection unit can collect relevant property information by analyzing the user's social media activity when collecting property information. For example, the data collection unit can collect property information for areas the user has shown interest in on social media. The data collection unit can also collect property information from real estate companies the user follows on social media. The data collection unit can also collect relevant property information based on property information the user has shared on social media. For example, the data collection unit can collect property information for areas the user has shown interest in on social media. The data collection unit can collect property information from real estate companies the user follows on social media. The data collection unit can collect relevant property information based on property information the user has shared on social media. In this way, the data collection unit can collect relevant property information by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI collect relevant property information.

[0042] The organization unit can adjust the level of detail in the organization of property information based on the importance of the property. For example, the organization unit can organize important property information in detail and display it preferentially. The organization unit can also simplify the organization of less important property information and postpone it. The organization unit can also adjust the display order of information according to the importance of the property. For example, the organization unit can organize important property information in detail and display it preferentially. The organization unit can simplify the organization of less important property information and postpone it. The organization unit adjusts the display order of information according to the importance of the property. In this way, the organization unit can prioritize the organization of important property information by adjusting the level of detail in the organization based on the importance of the property. Some or all of the above processing in the organization unit may be performed using AI, for example, or not using AI. For example, the organization unit can input property importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in the organization.

[0043] The sorting unit can apply different sorting algorithms depending on the property category when sorting property information. For example, the sorting unit can apply different sorting algorithms for rental properties and properties for sale. The sorting unit can also apply different sorting algorithms for commercial properties and residential properties. The sorting unit can also apply different sorting algorithms for new properties and used properties. For example, the sorting unit can apply different sorting algorithms for rental properties and properties for sale. The sorting unit can apply different sorting algorithms for commercial properties and residential properties. The sorting unit can apply different sorting algorithms for new properties and used properties. This allows the sorting unit to sort more appropriately by applying different sorting algorithms depending on the property category. Some or all of the above processing in the sorting unit may be performed using AI, for example, or without AI. For example, the sorting unit can input property category data into a generating AI and have the generating AI execute the application of sorting algorithms.

[0044] The sorting unit can determine the sorting priority based on the property submission date when sorting property information. For example, the sorting unit can prioritize sorting newly submitted property information. The sorting unit can also postpone sorting older property information. The sorting unit can also adjust the display order of information according to the submission date. For example, the sorting unit prioritizes sorting newly submitted property information. The sorting unit postpones sorting older property information. The sorting unit adjusts the display order of information according to the submission date. In this way, the sorting unit can prioritize sorting the latest property information by determining the sorting priority based on the property submission date. Some or all of the above processing in the sorting unit may be performed using AI, for example, or not using AI. For example, the sorting unit can input property submission date data into a generating AI and have the generating AI perform the determination of sorting priority.

[0045] The sorting unit can adjust the sorting order based on the relevance of properties when sorting property information. For example, the sorting unit can group similar property information together and display it in order of relevance. The sorting unit can also postpone sorting less relevant property information. The sorting unit can also adjust the display order of information according to the relevance of properties. For example, the sorting unit can group similar property information together and display it in order of relevance. The sorting unit postpones sorting less relevant property information. The sorting unit adjusts the display order of information according to the relevance of properties. In this way, the sorting unit can prioritize sorting highly relevant property information by adjusting the sorting order based on the relevance of properties. Some or all of the above processing in the sorting unit may be performed using AI, for example, or not using AI. For example, the sorting unit can input property relevance data into a generating AI and have the generating AI perform the sorting order adjustment.

[0046] The proposal unit can adjust the level of detail in a property proposal based on the importance of the property. For example, the proposal unit can propose important properties in detail and display them preferentially. The proposal unit can also propose less important properties in a simplified manner and postpone them. The proposal unit can also adjust the display order of information according to the importance of the property. For example, the proposal unit can propose important properties in detail and display them preferentially. The proposal unit can propose less important properties in a simplified manner and postpone them. The proposal unit can adjust the display order of information according to the importance of the property. In this way, the proposal unit can prioritize proposing important properties by adjusting the level of detail in the proposal based on the importance of the property. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input property importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in the proposal.

[0047] The proposal unit can apply different proposal algorithms depending on the property category when proposing properties. For example, the proposal unit can apply different proposal algorithms for rental properties and properties for sale. The proposal unit can also apply different proposal algorithms for commercial properties and residential properties. The proposal unit can also apply different proposal algorithms for new properties and used properties. For example, the proposal unit can apply different proposal algorithms for rental properties and properties for sale. The proposal unit can apply different proposal algorithms for commercial properties and residential properties. The proposal unit can apply different proposal algorithms for new properties and used properties. This allows the proposal unit to make more appropriate proposals by applying different proposal algorithms depending on the property category. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input property category data into a generating AI and have the generating AI execute the application of the proposal algorithm.

[0048] The proposal department can determine the priority of property proposals based on the submission date. For example, the proposal department can prioritize newly submitted properties. The proposal department can also postpone older properties. The proposal department can also adjust the display order of information according to the submission date. For example, the proposal department can prioritize newly submitted properties. The proposal department can postpone older properties. The proposal department can adjust the display order of information according to the submission date. This allows the proposal department to prioritize the most recent properties by determining the priority of proposals based on the submission date. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input property submission date data into a generating AI and have the generating AI perform the determination of proposal priority.

[0049] The proposal unit can adjust the order of proposals based on the relevance of the properties when proposing properties. For example, the proposal unit can group similar properties together and display them in order of relevance. The proposal unit can also postpone less relevant properties. The proposal unit can also adjust the display order of information according to the relevance of the properties. For example, the proposal unit can group similar properties together and display them in order of relevance. The proposal unit can postpone less relevant properties. The proposal unit adjusts the display order of information according to the relevance of the properties. In this way, the proposal unit can prioritize proposing highly relevant properties by adjusting the order of proposals based on the relevance of the properties. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input property relevance data into a generating AI and have the generating AI perform the adjustment of the proposal order.

[0050] The contract department can customize the means of contract support based on the user's current living situation. For example, if the user is busy, the contract department will prioritize providing online contract procedures. If the user is elderly, the contract department can also provide in-person contract support. If the user is a foreigner, the contract department can also provide multilingual contract support. For example, if the user is busy, the contract department will prioritize providing online contract procedures. If the user is elderly, the contract department will provide in-person contract support. If the user is a foreigner, the contract department will provide multilingual contract support. This allows the contract department to provide more appropriate support by customizing the means of contract support based on the user's current living situation. Some or all of the above processing in the contract department may be performed using AI, for example, or not using AI. For example, the contract department can input user living situation data into a generating AI and have the generating AI perform the customization of the means of contract support.

[0051] The contract department can select the optimal contract support method by considering the user's geographical location information when providing contract support. For example, the contract department can provide contract support at a location close to the user's current location. The contract department can also provide contract support at a location along the user's commute route. The contract department can also provide contract support in an area the user frequently visits. For example, the contract department can provide contract support at a location close to the user's current location. The contract department can provide contract support at a location along the user's commute route. The contract department can provide contract support in an area the user frequently visits. This allows the contract department to select the optimal contract support method by considering the user's geographical location information. Some or all of the above processing in the contract department may be performed using AI, for example, or without AI. For example, the contract department can input the user's geographical location information data into a generating AI and have the generating AI select the optimal contract support method.

[0052] The contracts department can analyze a user's social media activity and propose contract support methods when providing contract support. For example, the contracts department can propose contract procedures that the user has shown interest in on social media. The contracts department can also propose contract procedures of real estate companies that the user follows on social media. The contracts department can also propose relevant contract support based on contract procedures that the user has shared on social media. For example, the contracts department can propose contract procedures that the user has shown interest in on social media. The contracts department can propose contract procedures of real estate companies that the user follows on social media. The contracts department can propose relevant contract support based on contract procedures that the user has shared on social media. In this way, the contracts department can propose more appropriate contract support methods by analyzing the user's social media activity. Some or all of the above processing in the contracts department may be performed using AI, for example, or not using AI. For example, the contracts department can input the user's social media activity data into a generating AI and have the generating AI propose contract support methods.

[0053] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0054] The next-generation platform can further analyze a user's past property viewing history to provide optimal property recommendations. For example, the recommendation unit can analyze the characteristics of properties the user has previously viewed and prioritize suggesting similar properties. It can also analyze the price range of properties the user has previously viewed and suggest properties in the same price range. It can also analyze the location conditions of properties the user has previously viewed and suggest properties in the same area. In this way, the recommendation unit can provide optimal property recommendations by analyzing the user's past property viewing history. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or not. For example, the recommendation unit can input the user's property viewing history data into a generating AI and have the generating AI execute optimal property recommendations.

[0055] The next-generation platform can further suggest properties based on the user's current living situation and areas of interest. For example, if the user owns a pet, the suggestion unit will prioritize suggesting pet-friendly properties. If the user has children, it can also suggest properties within a school district. If the user owns a car, it can also suggest properties with parking. In this way, the suggestion unit can suggest more appropriate properties by suggesting properties based on the user's current living situation and areas of interest. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input data on the user's living situation and areas of interest into a generating AI and have the generating AI execute property suggestions.

[0056] The next-generation platform can further consider the user's geographical location when suggesting properties. For example, the suggestion unit can prioritize suggesting properties close to the user's current location. It can also suggest properties along the user's commute route. It can also suggest properties in areas the user frequently visits. In this way, the suggestion unit can prioritize suggesting highly relevant properties by considering the user's geographical location. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the user's geographical location data into a generating AI and have the generating AI execute property suggestions.

[0057] The next-generation platform can further analyze users' social media activity and provide relevant property suggestions. For example, the suggestion unit can suggest properties in areas where the user has shown interest on social media. It can also suggest properties from real estate companies that the user follows on social media. It can also suggest relevant properties based on property information shared by the user on social media. This allows the suggestion unit to provide relevant property suggestions by analyzing the user's social media activity. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can input the user's social media activity data into a generating AI and have the generating AI generate property suggestions.

[0058] The next-generation platform can further adjust the level of detail in property suggestions based on the importance of the property. For example, the suggestion section can suggest important properties in detail and display them preferentially. Less important properties can be suggested in a simplified manner and postponed. The display order of information can also be adjusted according to the importance of the property. This allows the suggestion section to prioritize suggesting important properties by adjusting the level of detail in suggestions based on the importance of the property. Some or all of the above processing in the suggestion section may be performed using AI, for example, or not using AI. For example, the suggestion section can input property importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in suggestions.

[0059] The following briefly describes the processing flow for example form 1.

[0060] Step 1: The collection unit automatically collects property information. The collection unit collects property information from the internet, for example, using web scraping technology. The collection unit can also obtain property information from real estate databases through API integration. Furthermore, the collection unit can receive data feeds from property information provision services. Step 2: The organization department organizes the property information collected by the collection department. For example, the organization department categorizes the property information by location, price, floor plan, and year built. The organization department can also store the property information in a database and organize it to facilitate searching and filtering. Furthermore, the organization department can perform data cleansing to eliminate duplication of property information and maintain consistency. Step 3: The proposal department proposes properties to customers based on the property information organized by the organization department. For example, the proposal department proposes properties that meet the customer's needs. By collecting and analyzing customer requests, the proposal department can propose the most suitable properties. Furthermore, the proposal department can also analyze the customer's potential needs and propose the most suitable properties. Step 4: The contracts department supports the contract based on the property proposed by the proposal department. For example, the contracts department supports the creation of the contract. The contracts department can streamline the creation of the contract by providing templates or using automated generation tools. In addition, the contracts department can manage the progress of the contract and provide appropriate support to the customer.

[0061] (Example of form 2) The next-generation platform according to an embodiment of the present invention is "Ouchi Concierge," which solves challenges in the real estate industry using generative AI. This platform provides an agent AI that supports both real estate agent sales representatives and prospective tenants looking for properties, from property proposals to contract signing, using generative AI. The agent AI streamlines the operations of real estate companies 24 hours a day and helps prospective tenants find their ideal property. Through AI-driven dialogue and suggestions, it solves the challenges of both parties simultaneously. For example, this platform provides real estate companies with functions such as automatic collection and organization of property information, status management of customer response status, AI-driven business proposal notifications, automatic generation of explanation points during property viewings, 24 / 7 inquiry support, analysis and proposal of customers' potential needs, automatic scheduling of viewing appointments, prediction of the likelihood of closing a deal, analysis of market trends for similar properties, and determination of the optimal timing for proposals. Furthermore, for prospective tenants, the platform offers features such as personalized property search, automatic collection and organization of property information, property suggestions tailored to customer requests, automatic generation of explanation points for viewings, 24 / 7 inquiry support, analysis and suggestions for customers' potential needs, automatic scheduling of viewing appointments, detailed property information, decision support, prediction of closing probability, market trend analysis of similar properties, determination of optimal proposal timing, and interior layout through image generation. This platform will enable real estate companies to streamline their operations and make it easier for prospective tenants to find their ideal property. As a result, satisfaction for both parties will improve, and the entire real estate industry is expected to be revitalized. Thus, this next-generation platform can efficiently solve the challenges of the real estate industry and achieve operational efficiency and improved customer satisfaction.

[0062] The next-generation platform according to this embodiment comprises a collection unit, an organization unit, a proposal unit, and a contract unit. The collection unit automatically collects property information. The collection unit collects property information from the internet, for example, using web scraping technology. The collection unit can also obtain property information from real estate databases through API integration. Furthermore, the collection unit can receive data feeds from property information provision services. For example, the collection unit periodically collects property information from specific real estate portal sites using web scraping technology. It can also obtain the latest property information from real estate databases through API integration. By receiving data feeds from property information provision services, the collection unit can update property information in real time. The organization unit organizes the property information collected by the collection unit. The organization unit classifies property information into categories such as location, price, floor plan, and year built, for example. The organization unit can also store property information in a database and organize it to facilitate searching and filtering. Furthermore, the organization unit can perform data cleansing to eliminate duplication of property information and maintain consistency. For example, the organization department classifies property information by location and filters it by price range and floor plan. It stores the property information in a database and organizes it to facilitate searching and filtering. It performs data cleansing to eliminate duplicate property information and maintain consistency. The proposal department proposes properties to customers based on the property information organized by the organization department. For example, the proposal department proposes properties that meet customer needs. By collecting and analyzing customer needs, the proposal department can propose the most suitable properties. Furthermore, the proposal department can analyze customers' latent needs and propose the most suitable properties. For example, the proposal department collects and analyzes customer needs through surveys and interviews. Based on customer needs, it proposes the most suitable properties. It also analyzes customers' latent needs, considering their behavioral history and psychological factors, and proposes the most suitable properties. The contract department supports contracts based on the properties proposed by the proposal department. For example, the contract department supports the creation of contracts. The contract department can streamline contract creation by providing templates or using automated generation tools.Furthermore, the contracts department can manage the progress of contracts and provide appropriate support to customers. For example, the contracts department can provide contract templates, allowing customers to create contracts simply by entering the necessary information. Automatic generation tools can be used to streamline contract creation. By managing the progress of contracts and providing appropriate support to customers, the next-generation platform according to this embodiment can efficiently support everything from property information collection to contract signing.

[0063] The data collection unit automatically collects property information. For example, the data collection unit uses web scraping technology to collect property information from the internet. Web scraping technology is a technique that automatically extracts necessary information from specific websites, and the data collection unit uses this to periodically collect property information from real estate portal sites and real estate company websites. Specifically, the data collection unit analyzes the HTML structure of websites and extracts information such as the property's location, price, floor plan, and year of construction. The data collection unit can also obtain property information from real estate databases through API integration. API integration allows the data collection unit to obtain the latest property information in real time and reflect it in the database. Furthermore, the data collection unit can receive data feeds from property information provision services. A data feed is a flow of data provided by property information provision services, and by receiving this, the data collection unit can update property information in real time. For example, the data collection unit periodically collects property information from specific real estate portal sites and obtains the latest property information from real estate databases through API integration. By receiving data feeds from property information provision services, the data collection unit can update property information in real time. This allows the data collection unit to efficiently gather a wide range of property information from diverse sources and provide the data that will form the foundation of the next-generation platform.

[0064] The organization department organizes the property information collected by the collection department. For example, the organization department categorizes property information by location, price, floor plan, and year built. This allows users to quickly find the necessary information when searching for properties. The organization department can also store the property information in a database and organize it to facilitate searching and filtering. The property information stored in the database is indexed so that users can search using various criteria, enabling efficient searching. Furthermore, the organization department can perform data cleansing to eliminate duplicate property information and maintain consistency. Data cleansing is the process of detecting, correcting, or deleting duplicates and errors from the collected property information. For example, the organization department categorizes property information by location and filters it by price range and floor plan. It stores the property information in a database and organizes it to facilitate searching and filtering. It performs data cleansing to eliminate duplicate property information and maintain consistency. In this way, the organization department efficiently organizes the collected property information, allowing users to quickly and accurately obtain the information they need.

[0065] The proposal department proposes properties to customers based on property information organized by the organization department. For example, the proposal department proposes properties that meet the customer's requests. By collecting and analyzing customer requests, the proposal department can propose the most suitable properties. Customer requests are collected through surveys and interviews, and the proposal department uses this information to understand the customer's desired conditions. Furthermore, the proposal department can also analyze the customer's latent needs and propose the most suitable properties. Analyzing latent needs takes into account the customer's behavioral history and psychological factors. For example, the proposal department analyzes the customer's website browsing history and past inquiry history to identify properties that the customer might be interested in. It also analyzes the characteristics of properties that customers prefer, taking into account their psychological factors. As a result, the proposal department can not only propose the most suitable properties based on customer requests, but also propose properties that meet the customer's latent needs. For example, the proposal department collects and analyzes customer requests through surveys and interviews. Based on customer requests, it proposes the most suitable properties. By analyzing the customer's latent needs, taking into account their behavioral history and psychological factors, it proposes the most suitable properties. As a result, the proposal department can propose the most suitable properties to customers and improve customer satisfaction.

[0066] The Contracts Department supports contracts based on properties proposed by the Proposal Department. For example, the Contracts Department supports the creation of contract documents. The Contracts Department can streamline contract creation by providing templates and using automated generation tools. Templates provide common contract formats, allowing customers to create contracts simply by entering the necessary information. Automated generation tools automatically generate contracts based on customer input, streamlining the creation process. Furthermore, the Contracts Department can manage contract progress and provide appropriate support to customers. Contract progress management tracks each step from contract creation to signing and submission, supporting customers in smoothly completing the contract. For example, the Contracts Department provides contract templates, allowing customers to create contracts simply by entering the necessary information. Automated generation tools streamline contract creation. By managing contract progress and providing appropriate support to customers, the Contracts Department can support customers in smoothly completing the contract, improving the efficiency of the contract process and increasing customer satisfaction.

[0067] The proposal department can propose properties that meet customer needs. For example, the proposal department collects and analyzes customer needs through surveys and interviews. Based on customer needs, the proposal department proposes the most suitable properties. For example, the proposal department can collect the conditions of the property that the customer wants (location, price, floor plan, etc.) and propose properties based on that. In addition, the proposal department can make personalized property proposals based on the customer's lifestyle and hobbies and preferences. For example, if the customer has a pet, the proposal department will prioritize proposing pet-friendly properties. If the customer has children, they will propose properties within the school district. If the customer owns a car, they will propose properties with parking. In this way, the proposal department can propose properties that meet customer needs.

[0068] The proposal department can analyze customers' latent needs and suggest the most suitable properties. For example, it can analyze customers' browsing history and suggest properties that they might be interested in. It can also consider customers' psychological factors and suggest properties that they are likely to like. For example, it can analyze the characteristics of properties customers have viewed in the past and suggest similar properties. It can suggest properties with similar conditions based on the conditions of properties customers have inquired about in the past. It can prioritize suggesting properties that customers might be interested in based on their browsing history. This enables the proposal department to suggest the most suitable properties based on customers' latent needs.

[0069] The contracts department can support the creation of contracts. For example, the contracts department can provide contract templates, allowing customers to create contracts simply by entering the necessary information. The contracts department can also streamline contract creation by using automated generation tools. For example, the contracts department can provide contract templates, allowing customers to create contracts simply by entering the necessary information. The contracts department streamlines contract creation by using automated generation tools. The contracts department automates the contract creation process, allowing customers to create contracts quickly. In this way, the contracts department can efficiently support contract creation.

[0070] The contracts department can manage the progress of contracts. For example, the contracts department can provide tools to visualize contract progress, allowing customers to grasp the progress at a glance. The contracts department can also use notification functions to inform customers about contract progress. For example, the contracts department can provide tools to visualize contract progress, allowing customers to grasp the progress at a glance. The contracts department can use notification functions to inform customers about contract progress. The contracts department updates contract progress in real time, ensuring customers always have the latest information. This allows the contracts department to efficiently manage contract progress.

[0071] The data collection unit can estimate the user's emotions and adjust the timing of property information collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the collection timing and collect the information when the user is relaxed. If the user is excited, the data collection unit can also speed up the collection timing and provide property information immediately. If the user is tired, the data collection unit can also adjust the collection timing and collect the information after the user has rested. For example, if the user is stressed, the data collection unit can delay the collection timing and collect the information when the user is relaxed. If the user is excited, the data collection unit can speed up the collection timing and provide property information immediately. If the user is tired, the data collection unit can adjust the collection timing and collect the information after the user has rested. In this way, the data collection unit can collect property information at a more appropriate time by adjusting the timing of property information collection 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 processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generating AI and have the generating AI adjust the timing of data collection.

[0072] The data collection unit can analyze the user's past property viewing history and select the optimal collection method when collecting property information. For example, the data collection unit can analyze the characteristics of properties the user has previously viewed and prioritize collecting similar property information. The data collection unit can also analyze the price range of properties the user has previously viewed and collect property information in the same price range. The data collection unit can also analyze the location conditions of properties the user has previously viewed and collect property information in the same area. For example, the data collection unit can analyze the characteristics of properties the user has previously viewed and prioritize collecting similar property information. The data collection unit can analyze the price range of properties the user has previously viewed and collect property information in the same price range. The data collection unit can analyze the location conditions of properties the user has previously viewed and collect property information in the same area. In this way, the data collection unit can select the optimal collection method by analyzing the user's past property viewing history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's property viewing history data into a generating AI and have the generating AI select the optimal collection method.

[0073] The data collection unit can filter property information based on the user's current living situation and areas of interest. For example, if the user owns a pet, the data collection unit will prioritize collecting pet-friendly properties. If the user has children, the data collection unit can also prioritize collecting properties within a school district. If the user owns a car, the data collection unit can also prioritize collecting properties with parking spaces. For example, if the user owns a pet, the data collection unit will prioritize collecting pet-friendly properties. If the user has children, the data collection unit will prioritize collecting properties within a school district. If the user owns a car, the data collection unit will prioritize collecting properties with parking spaces. This allows the data collection unit to collect more appropriate property information by filtering property information based on the user's current living situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input data on the user's living situation and areas of interest into a generating AI and have the generating AI perform the filtering.

[0074] The data collection unit can estimate the user's emotions and determine the priority of property information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting property information in a relaxing environment. If the user is excited, the data collection unit may also prioritize collecting property information in an active lifestyle. If the user is tired, the data collection unit may also prioritize collecting property information in a quiet environment. For example, if the user is stressed, the data collection unit will prioritize collecting property information in a relaxing environment. If the user is excited, the data collection unit will prioritize collecting property information in an active lifestyle. If the user is tired, the data collection unit will prioritize collecting property information in a quiet environment. In this way, the data collection unit can collect more appropriate property information by prioritizing property information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generating AI, which can then perform the task of determining the priority of property information.

[0075] The data collection unit can prioritize collecting highly relevant property information by considering the user's geographical location. For example, the data collection unit can prioritize collecting property information close to the user's current location. The data collection unit can also prioritize collecting property information along the user's commute route. The data collection unit can also prioritize collecting property information in areas the user frequently visits. For example, the data collection unit can prioritize collecting property information close to the user's current location. The data collection unit can prioritize collecting property information along the user's commute route. The data collection unit can prioritize collecting property information in areas the user frequently visits. In this way, the data collection unit can prioritize collecting highly relevant property information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location data into a generating AI and have the generating AI perform the collection of highly relevant property information.

[0076] The data collection unit can collect relevant property information by analyzing the user's social media activity when collecting property information. For example, the data collection unit can collect property information for areas the user has shown interest in on social media. The data collection unit can also collect property information from real estate companies the user follows on social media. The data collection unit can also collect relevant property information based on property information the user has shared on social media. For example, the data collection unit can collect property information for areas the user has shown interest in on social media. The data collection unit can collect property information from real estate companies the user follows on social media. The data collection unit can collect relevant property information based on property information the user has shared on social media. In this way, the data collection unit can collect relevant property information by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI collect relevant property information.

[0077] The organization unit can estimate the user's emotions and adjust how property information is organized based on the estimated emotions. For example, if the user is stressed, the organization unit can provide a simple organization method and reduce the amount of information. If the user is relaxed, the organization unit can also provide a detailed organization method and enrich the information. If the user is in a hurry, the organization unit can prioritize and quickly provide important information. For example, if the user is stressed, the organization unit can provide a simple organization method and reduce the amount of information. If the user is relaxed, the organization unit can provide a detailed organization method and enrich the information. If the user is in a hurry, the organization unit can prioritize and quickly provide important information. This allows the organization unit to organize property information more appropriately by adjusting how it is organized according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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 organization unit may be performed using AI, for example, or without AI. For example, the organization unit can input user emotion data into a generating AI and have the generating AI adjust the organization method.

[0078] The organization unit can adjust the level of detail in the organization of property information based on the importance of the property. For example, the organization unit can organize important property information in detail and display it preferentially. The organization unit can also simplify the organization of less important property information and postpone it. The organization unit can also adjust the display order of information according to the importance of the property. For example, the organization unit can organize important property information in detail and display it preferentially. The organization unit can simplify the organization of less important property information and postpone it. The organization unit adjusts the display order of information according to the importance of the property. In this way, the organization unit can prioritize the organization of important property information by adjusting the level of detail in the organization based on the importance of the property. Some or all of the above processing in the organization unit may be performed using AI, for example, or not using AI. For example, the organization unit can input property importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in the organization.

[0079] The sorting unit can apply different sorting algorithms depending on the property category when sorting property information. For example, the sorting unit can apply different sorting algorithms for rental properties and properties for sale. The sorting unit can also apply different sorting algorithms for commercial properties and residential properties. The sorting unit can also apply different sorting algorithms for new properties and used properties. For example, the sorting unit can apply different sorting algorithms for rental properties and properties for sale. The sorting unit can apply different sorting algorithms for commercial properties and residential properties. The sorting unit can apply different sorting algorithms for new properties and used properties. This allows the sorting unit to sort more appropriately by applying different sorting algorithms depending on the property category. Some or all of the above processing in the sorting unit may be performed using AI, for example, or without AI. For example, the sorting unit can input property category data into a generating AI and have the generating AI execute the application of sorting algorithms.

[0080] The sorting unit can estimate the user's emotions and determine the priority of property information to sort based on the estimated user emotions. For example, if the user is stressed, the sorting unit will prioritize sorting property information with a relaxing environment. If the user is excited, the sorting unit may also prioritize sorting property information that allows for an active lifestyle. If the user is tired, the sorting unit may also prioritize sorting property information with a quiet environment. For example, if the user is stressed, the sorting unit will prioritize sorting property information with a relaxing environment. If the user is excited, the sorting unit will prioritize sorting property information that allows for an active lifestyle. If the user is tired, the sorting unit will prioritize sorting property information with a quiet environment. In this way, the sorting unit can sort more appropriate property information by determining the priority of property information 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 sorting unit may be performed using AI, for example, or without AI. For example, the organization unit can input user sentiment data into a generating AI and have the AI ​​determine the priority of property information.

[0081] The sorting unit can determine the sorting priority based on the property submission date when sorting property information. For example, the sorting unit can prioritize sorting newly submitted property information. The sorting unit can also postpone sorting older property information. The sorting unit can also adjust the display order of information according to the submission date. For example, the sorting unit prioritizes sorting newly submitted property information. The sorting unit postpones sorting older property information. The sorting unit adjusts the display order of information according to the submission date. In this way, the sorting unit can prioritize sorting the latest property information by determining the sorting priority based on the property submission date. Some or all of the above processing in the sorting unit may be performed using AI, for example, or not using AI. For example, the sorting unit can input property submission date data into a generating AI and have the generating AI perform the determination of sorting priority.

[0082] The sorting unit can adjust the sorting order based on the relevance of properties when sorting property information. For example, the sorting unit can group similar property information together and display it in order of relevance. The sorting unit can also postpone sorting less relevant property information. The sorting unit can also adjust the display order of information according to the relevance of properties. For example, the sorting unit can group similar property information together and display it in order of relevance. The sorting unit postpones sorting less relevant property information. The sorting unit adjusts the display order of information according to the relevance of properties. In this way, the sorting unit can prioritize sorting highly relevant property information by adjusting the sorting order based on the relevance of properties. Some or all of the above processing in the sorting unit may be performed using AI, for example, or not using AI. For example, the sorting unit can input property relevance data into a generating AI and have the generating AI perform the sorting order adjustment.

[0083] The proposal unit can estimate the user's emotions and adjust the way property suggestions are presented based on the estimated emotions. For example, if the user is stressed, the proposal unit provides a simple and easy-to-understand presentation. If the user is relaxed, the proposal unit can also provide a presentation that includes detailed information. If the user is in a hurry, the proposal unit can also provide a presentation that gets straight to the point. For example, if the user is stressed, the proposal unit provides a simple and easy-to-understand presentation. If the user is relaxed, the proposal unit provides a presentation that includes detailed information. If the user is in a hurry, the proposal unit provides a presentation that gets straight to the point. This allows the proposal unit to make more appropriate suggestions by adjusting the way property suggestions are presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal department can input user emotion data into a generation AI and have the generation AI adjust the way property proposals are presented.

[0084] The proposal unit can adjust the level of detail in a property proposal based on the importance of the property. For example, the proposal unit can propose important properties in detail and display them preferentially. The proposal unit can also propose less important properties in a simplified manner and postpone them. The proposal unit can also adjust the display order of information according to the importance of the property. For example, the proposal unit can propose important properties in detail and display them preferentially. The proposal unit can propose less important properties in a simplified manner and postpone them. The proposal unit can adjust the display order of information according to the importance of the property. In this way, the proposal unit can prioritize proposing important properties by adjusting the level of detail in the proposal based on the importance of the property. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input property importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in the proposal.

[0085] The proposal unit can apply different proposal algorithms depending on the property category when proposing properties. For example, the proposal unit can apply different proposal algorithms for rental properties and properties for sale. The proposal unit can also apply different proposal algorithms for commercial properties and residential properties. The proposal unit can also apply different proposal algorithms for new properties and used properties. For example, the proposal unit can apply different proposal algorithms for rental properties and properties for sale. The proposal unit can apply different proposal algorithms for commercial properties and residential properties. The proposal unit can apply different proposal algorithms for new properties and used properties. This allows the proposal unit to make more appropriate proposals by applying different proposal algorithms depending on the property category. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input property category data into a generating AI and have the generating AI execute the application of the proposal algorithm.

[0086] The suggestion unit can estimate the user's emotions and adjust the length of property suggestions based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit will provide short, to-the-point suggestions. If the user is relaxed, the suggestion unit may provide longer suggestions with detailed explanations. If the user is excited, the suggestion unit may provide suggestions with visually stimulating effects. For example, if the user is in a hurry, the suggestion unit will provide short, to-the-point suggestions. If the user is relaxed, the suggestion unit will provide longer suggestions with detailed explanations. If the user is excited, the suggestion unit may provide suggestions with visually stimulating effects. This allows the suggestion unit to provide more appropriate suggestions by adjusting the length of property suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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 suggestion unit may be performed using AI, for example, or without AI. For example, the proposal department can input user emotion data into the generating AI and have the AI ​​adjust the length of the property proposals.

[0087] The proposal department can determine the priority of property proposals based on the submission date. For example, the proposal department can prioritize newly submitted properties. The proposal department can also postpone older properties. The proposal department can also adjust the display order of information according to the submission date. For example, the proposal department can prioritize newly submitted properties. The proposal department can postpone older properties. The proposal department can adjust the display order of information according to the submission date. This allows the proposal department to prioritize the most recent properties by determining the priority of proposals based on the submission date. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input property submission date data into a generating AI and have the generating AI perform the determination of proposal priority.

[0088] The proposal unit can adjust the order of proposals based on the relevance of the properties when proposing properties. For example, the proposal unit can group similar properties together and display them in order of relevance. The proposal unit can also postpone less relevant properties. The proposal unit can also adjust the display order of information according to the relevance of the properties. For example, the proposal unit can group similar properties together and display them in order of relevance. The proposal unit can postpone less relevant properties. The proposal unit adjusts the display order of information according to the relevance of the properties. In this way, the proposal unit can prioritize proposing highly relevant properties by adjusting the order of proposals based on the relevance of the properties. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input property relevance data into a generating AI and have the generating AI perform the adjustment of the proposal order.

[0089] The contracts department can estimate the user's emotions and adjust its contract support methods based on those emotions. For example, if the user is stressed, the contracts department can provide simple and easy-to-understand contract support. If the user is relaxed, the contracts department can also provide contract support that includes detailed information. If the user is in a hurry, the contracts department can also provide contract support that gets straight to the point. For example, if the user is stressed, the contracts department can provide simple and easy-to-understand contract support. If the user is relaxed, the contracts department can provide contract support that includes detailed information. If the user is in a hurry, the contracts department can provide contract support that gets straight to the point. This allows the contracts department to provide more appropriate support by adjusting its contract support methods 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 processing in the contracts department may be performed using AI, for example, or not using AI. For example, the contracts department can input user emotion data into a generating AI and have the AI ​​adjust the contract support methods.

[0090] The contract department can customize the means of contract support based on the user's current living situation. For example, if the user is busy, the contract department will prioritize providing online contract procedures. If the user is elderly, the contract department can also provide in-person contract support. If the user is a foreigner, the contract department can also provide multilingual contract support. For example, if the user is busy, the contract department will prioritize providing online contract procedures. If the user is elderly, the contract department will provide in-person contract support. If the user is a foreigner, the contract department will provide multilingual contract support. This allows the contract department to provide more appropriate support by customizing the means of contract support based on the user's current living situation. Some or all of the above processing in the contract department may be performed using AI, for example, or not using AI. For example, the contract department can input user living situation data into a generating AI and have the generating AI perform the customization of the means of contract support.

[0091] The contracts department can estimate the user's emotions and prioritize contract support based on those emotions. For example, if the user is stressed, the contracts department will prioritize providing contract support in a relaxing environment. If the user is agitated, the contracts department may also provide prompt contract support. If the user is tired, the contracts department may also provide contract support after the user has had time to rest. For example, if the user is stressed, the contracts department will prioritize providing contract support in a relaxing environment. If the user is agitated, the contracts department will provide prompt contract support. If the user is tired, the contracts department will provide contract support after the user has had time to rest. This allows the contracts department to provide more appropriate support by prioritizing contract support according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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 processing in the contracts department may be performed using AI, for example, or without AI. For example, the contracts department can input user emotion data into a generating AI and have the AI ​​determine the priority of contract support.

[0092] The contract department can select the optimal contract support method by considering the user's geographical location information when providing contract support. For example, the contract department can provide contract support at a location close to the user's current location. The contract department can also provide contract support at a location along the user's commute route. The contract department can also provide contract support in an area the user frequently visits. For example, the contract department can provide contract support at a location close to the user's current location. The contract department can provide contract support at a location along the user's commute route. The contract department can provide contract support in an area the user frequently visits. This allows the contract department to select the optimal contract support method by considering the user's geographical location information. Some or all of the above processing in the contract department may be performed using AI, for example, or without AI. For example, the contract department can input the user's geographical location information data into a generating AI and have the generating AI select the optimal contract support method.

[0093] The contracts department can analyze a user's social media activity and propose contract support methods when providing contract support. For example, the contracts department can propose contract procedures that the user has shown interest in on social media. The contracts department can also propose contract procedures of real estate companies that the user follows on social media. The contracts department can also propose relevant contract support based on contract procedures that the user has shared on social media. For example, the contracts department can propose contract procedures that the user has shown interest in on social media. The contracts department can propose contract procedures of real estate companies that the user follows on social media. The contracts department can propose relevant contract support based on contract procedures that the user has shared on social media. In this way, the contracts department can propose more appropriate contract support methods by analyzing the user's social media activity. Some or all of the above processing in the contracts department may be performed using AI, for example, or not using AI. For example, the contracts department can input the user's social media activity data into a generating AI and have the generating AI propose contract support methods.

[0094] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0095] The next-generation platform can further estimate the user's emotions and adjust the timing of property recommendations based on those emotions. For example, if the recommendation unit is stressed, it can delay the timing of recommendations until the user is relaxed. If the user is excited, it can speed up the timing of recommendations and provide property information immediately. If the user is tired, it can adjust the timing of recommendations until the user has rested. In this way, the recommendation unit can offer property information at a more appropriate time by adjusting the timing of property recommendations 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 recommendation unit may be performed using AI or not using AI. For example, the recommendation unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the recommendation timing.

[0096] The next-generation platform can further estimate the user's emotions and customize property recommendations based on those emotions. For example, if the user is stressed, the recommendation unit can provide simple and easy-to-understand property recommendations. If the user is relaxed, it can provide property recommendations with more detailed information. If the user is in a hurry, it can provide property recommendations that get straight to the point. In this way, the recommendation unit can provide more appropriate recommendations by customizing the content of property recommendations 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 recommendation unit may be performed using AI, or not using AI. For example, the recommendation unit can input user emotion data into the generative AI and have the generative AI perform the customization of the recommendation content.

[0097] The next-generation platform can further estimate the user's emotions and adjust the order of property suggestions based on those emotions. For example, if the user is feeling stressed, the suggestion unit can prioritize suggesting properties with relaxing environments. If the user is excited, it can also prioritize suggesting properties that allow for an active lifestyle. If the user is tired, it can also prioritize suggesting properties with quiet environments. In this way, the suggestion unit can suggest more appropriate properties by adjusting the order of property suggestions 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 suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the order of suggestions.

[0098] The next-generation platform can further estimate the user's emotions and adjust the visual representation of property suggestions based on those emotions. For example, if the user is stressed, the suggestion unit can provide a simple, calming visual representation. If the user is excited, it can also provide a bright, stimulating visual representation. If the user is relaxed, it can also provide a natural visual representation. This allows the suggestion unit to provide more appropriate suggestions by adjusting the visual representation of property suggestions 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 suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the visual representation.

[0099] The next-generation platform can further estimate the user's emotions and adjust the frequency of property suggestions based on those emotions. For example, if the suggestion unit is stressed, it can reduce the frequency of suggestions and offer suggestions when the user is relaxed. If the user is excited, it can increase the frequency of suggestions and provide property information immediately. If the user is tired, it can adjust the frequency of suggestions and offer suggestions after the user has rested. This allows the suggestion unit to offer property information at a more appropriate time by adjusting the frequency of suggestions 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 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 suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into the generative AI and have the generative AI adjust the suggestion frequency.

[0100] The next-generation platform can further analyze a user's past property viewing history to provide optimal property recommendations. For example, the recommendation unit can analyze the characteristics of properties the user has previously viewed and prioritize suggesting similar properties. It can also analyze the price range of properties the user has previously viewed and suggest properties in the same price range. It can also analyze the location conditions of properties the user has previously viewed and suggest properties in the same area. In this way, the recommendation unit can provide optimal property recommendations by analyzing the user's past property viewing history. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or not. For example, the recommendation unit can input the user's property viewing history data into a generating AI and have the generating AI execute optimal property recommendations.

[0101] The next-generation platform can further suggest properties based on the user's current living situation and areas of interest. For example, if the user owns a pet, the suggestion unit will prioritize suggesting pet-friendly properties. If the user has children, it can also suggest properties within a school district. If the user owns a car, it can also suggest properties with parking. In this way, the suggestion unit can suggest more appropriate properties by suggesting properties based on the user's current living situation and areas of interest. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input data on the user's living situation and areas of interest into a generating AI and have the generating AI execute property suggestions.

[0102] The next-generation platform can further consider the user's geographical location when suggesting properties. For example, the suggestion unit can prioritize suggesting properties close to the user's current location. It can also suggest properties along the user's commute route. It can also suggest properties in areas the user frequently visits. In this way, the suggestion unit can prioritize suggesting highly relevant properties by considering the user's geographical location. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the user's geographical location data into a generating AI and have the generating AI execute property suggestions.

[0103] The next-generation platform can further analyze users' social media activity and provide relevant property suggestions. For example, the suggestion unit can suggest properties in areas where the user has shown interest on social media. It can also suggest properties from real estate companies that the user follows on social media. It can also suggest relevant properties based on property information shared by the user on social media. This allows the suggestion unit to provide relevant property suggestions by analyzing the user's social media activity. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can input the user's social media activity data into a generating AI and have the generating AI generate property suggestions.

[0104] The next-generation platform can further adjust the level of detail in property suggestions based on the importance of the property. For example, the suggestion section can suggest important properties in detail and display them preferentially. Less important properties can be suggested in a simplified manner and postponed. The display order of information can also be adjusted according to the importance of the property. This allows the suggestion section to prioritize suggesting important properties by adjusting the level of detail in suggestions based on the importance of the property. Some or all of the above processing in the suggestion section may be performed using AI, for example, or not using AI. For example, the suggestion section can input property importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in suggestions.

[0105] The following briefly describes the processing flow for example form 2.

[0106] Step 1: The collection unit automatically collects property information. The collection unit collects property information from the internet, for example, using web scraping technology. The collection unit can also obtain property information from real estate databases through API integration. Furthermore, the collection unit can receive data feeds from property information provision services. Step 2: The organization department organizes the property information collected by the collection department. For example, the organization department categorizes the property information by location, price, floor plan, and year built. The organization department can also store the property information in a database and organize it to facilitate searching and filtering. Furthermore, the organization department can perform data cleansing to eliminate duplication of property information and maintain consistency. Step 3: The proposal department proposes properties to customers based on the property information organized by the organization department. For example, the proposal department proposes properties that meet the customer's needs. By collecting and analyzing customer requests, the proposal department can propose the most suitable properties. Furthermore, the proposal department can also analyze the customer's potential needs and propose the most suitable properties. Step 4: The contracts department supports the contract based on the property proposed by the proposal department. For example, the contracts department supports the creation of the contract. The contracts department can streamline the creation of the contract by providing templates or using automated generation tools. In addition, the contracts department can manage the progress of the contract and provide appropriate support to the customer.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] Each of the multiple elements described above, including the collection unit, organization unit, proposal unit, and contract unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 and collects property information through web scraping technology and API integration. The organization unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and organizes the collected property information and stores it in the database 24. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and makes property proposals according to customer requests. The contract unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and supports the creation of contracts and the management of their progress. 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.

[0111] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements described above, including the collection unit, organization unit, proposal unit, and contract unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214 and collects property information through web scraping technology and API integration. The organization unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and organizes the collected property information and stores it in the database 24. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and makes property proposals according to customer requests. The contract unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and supports the creation of contracts and the management of their progress. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be changed in various ways.

[0127] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Each of the multiple elements described above, including the collection unit, organization unit, proposal unit, and contract unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314 and collects property information through web scraping technology and API integration. The organization unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and organizes the collected property information and stores it in the database 24. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and makes property proposals according to customer requests. The contract unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and supports the creation of contracts and the management of their progress. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be changed in various ways.

[0143] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] Each of the multiple elements described above, including the collection unit, organization unit, proposal unit, and contract unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 and collects property information through web scraping technology and API integration. The organization unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and organizes the collected property information and stores it in the database 24. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and makes property proposals according to customer requests. The contract unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and supports the creation of contracts and the management of their progress. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be changed in various ways.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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."

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] (Note 1) A collection unit that automatically collects property information, A sorting unit that organizes property information collected by the aforementioned collection unit, Based on the property information organized by the aforementioned organization department, there is a proposal department that proposes properties to customers, The system includes a contract department that supports contracts based on the properties proposed by the aforementioned proposal department. A system characterized by the following features. (Note 2) The aforementioned proposal section is, We propose properties that meet the customer's needs. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, We analyze the customer's potential needs and propose the most suitable property. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned contracts department, Support for contract creation The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned contracts department, Manage the progress of the contract. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of property information collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is When collecting property information, the system analyzes the user's past property viewing history to select the most suitable collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting property information, filtering is performed based on the user's current living situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is The system estimates the user's emotions and prioritizes the property information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting property information, the system prioritizes collecting highly relevant property information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting property information, we analyze users' social media activity to gather relevant property information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned editing unit, We estimate the user's emotions and adjust how property information is organized based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned editing unit, When organizing property information, adjust the level of detail based on the importance of each property. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned editing unit, When organizing property information, different sorting algorithms are applied depending on the property category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned editing unit, The system estimates the user's emotions and prioritizes the property information based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned editing unit, When organizing property information, prioritize the organization based on when the property was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned editing unit, When organizing property information, adjust the order of organization based on the relationships between properties. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, The system estimates the user's emotions and adjusts the way property suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When proposing properties, adjust the level of detail in the proposal based on the importance of the property. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When proposing properties, different proposal algorithms are applied depending on the property category. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, The system estimates the user's emotions and adjusts the length of property suggestions based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When submitting property proposals, we prioritize proposals based on when they were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When proposing properties, adjust the order of proposals based on the relevance of the properties. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned contracts department, We estimate the user's emotions and adjust the contract support method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned contracts department, During contract support, the support methods are customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned contracts department, The system estimates the user's emotions and prioritizes contract support based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned contracts department, During contract support, the optimal support method will be selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned contracts department, During contract support, we analyze the user's social media activity and propose methods for contract support. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0179] 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 collection unit that automatically collects property information, A sorting unit that organizes property information collected by the aforementioned collection unit, Based on the property information organized by the aforementioned organization department, there is a proposal department that proposes properties to customers, The system includes a contract department that supports contracts based on the properties proposed by the aforementioned proposal department. A system characterized by the following features.

2. The aforementioned proposal section is, We propose properties that meet the customer's needs. The system according to feature 1.

3. The aforementioned proposal section is, We analyze the customer's potential needs and propose the most suitable property. The system according to feature 1.

4. The aforementioned contracts department, Support for contract creation The system according to feature 1.

5. The aforementioned contracts department, Manage the progress of the contract. The system according to feature 1.

6. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of property information collection based on those estimated emotions. The system according to feature 1.

7. The aforementioned collection unit is When collecting property information, the system analyzes the user's past property viewing history to select the most suitable collection method. The system according to feature 1.

8. The aforementioned collection unit is When collecting property information, filtering is performed based on the user's current living situation and areas of interest. The system according to feature 1.

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