system

Abstract

The system according to the embodiment aims to easily compare the prices and advantages of properties and understand the total costs. [Solution] The system according to the embodiment comprises a collection unit, a comparison unit, a display unit, a calculation unit, and a management unit. The collection unit collects property information. The comparison unit compares prices or advantages based on the property information collected by the collection unit. The display unit displays the comparison results obtained by the comparison unit. The calculation unit calculates the purchase price and rent of the property as well as brokerage fees, registration fees, and moving expenses. The display unit displays the total expenses calculated by the calculation unit. The management unit manages the referral fees.

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has the drawback of making it difficult to easily compare property prices and advantages and understand total costs.

[0005] The system according to the embodiment aims to easily compare the prices and advantages of properties and understand the total costs. [Means for solving the problem]

[0006] The system according to the embodiment comprises a collection unit, a comparison unit, a display unit, a calculation unit, and a management unit. The collection unit collects property information. The comparison unit compares prices or advantages based on the property information collected by the collection unit. The display unit displays the comparison results obtained by the comparison unit. The calculation unit calculates the purchase price and rent of the property as well as brokerage fees, registration fees, and moving expenses. The display unit displays the total expenses calculated by the calculation unit. The management unit manages the referral fees. [Effects of the Invention]

[0007] The system according to the embodiment allows users to easily compare property prices and advantages and understand total costs. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10]1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A property comparison system according to an embodiment of the present invention allows users to easily compare optimal properties on a single screen when searching for a property to purchase or rent. This system allows users to easily compare property prices and advantages, enabling a user-oriented property search. It also provides a function for easily calculating purchase and miscellaneous expenses, allowing users to understand total expenses. The service is operated on a referral fee basis. First, when a user searches for a property, the system provides information for comparing property prices and advantages on a single screen. For example, information such as property price, location, amenities, and surrounding environment is displayed in a list, allowing users to easily compare properties. This allows users to efficiently compare multiple properties and select the optimal one. Second, the system provides a function for easily calculating miscellaneous expenses associated with purchasing or renting. For example, by entering miscellaneous expenses such as brokerage fees, registration fees, and moving costs in addition to the purchase price and rent of the property, the system automatically calculates total expenses. This allows users to understand total property expenses and select the optimal property within their budget. Furthermore, the service is operated on a referral fee basis. For example, the referral fee paid to a referral agent when a user purchases or rents a property is counted as an operating fee for the service. This allows users to use the service free of charge, while the operator earns revenue through referral fees. This service allows users to easily compare property prices and find advantages, and understand the total costs involved in purchasing or renting. Furthermore, by operating on referral fees, users can use the service free of charge, which is expected to attract more users. This allows the property comparison system to enable users to efficiently compare properties, understand the total costs, and manage referral fees.

[0029] A property comparison system according to an embodiment includes a collection unit, a comparison unit, a display unit, a calculation unit, and a management unit. The collection unit collects property information. The property information includes, for example, property price, location, facilities, and surrounding environment, but is not limited to these examples. The collection unit collects property information from, for example, real estate websites on the Internet. The collection unit can also obtain property information directly from real estate agents. The collection unit can also collect property information entered by a user. For example, the collection unit provides a form for a user to input desired property conditions and collects property information based on the information. The comparison unit compares prices and advantages based on the property information collected by the collection unit. The comparison is performed based on criteria such as property price, location, facilities, and surrounding environment, but is not limited to these examples. For example, the comparison unit compares property prices and identifies the cheapest property. The comparison unit can also compare property locations and identify the most convenient property. The comparison unit can also compare property facilities and surrounding environment to identify the best property. The display unit displays the comparison results obtained by the comparison unit. The display may be, for example, in a list format or a graph format, but is not limited to these examples. For example, the display unit may display property prices in a list format, allowing the user to easily compare properties. The display unit may also display the location of the property on a map, allowing the user to visually compare properties. Furthermore, the display unit may display the property's facilities and surrounding environment in a graph format, allowing the user to intuitively compare properties. The calculation unit may calculate various expenses, such as brokerage fees, registration fees, and moving costs, in addition to the purchase price and rent of the property. The calculation may be performed, for example, based on information entered by the user, but is not limited to these examples. For example, the calculation unit may provide a form for entering the purchase price and rent of the property and calculate total expenses based on the information. The calculation unit may also automatically calculate various expenses, such as brokerage fees, registration fees, and moving costs. Furthermore, the calculation unit may perform calculations to help the user select the optimal property within their desired budget. The management unit may manage referral fees paid to the referral agent when the user purchases or rents a property. The management may be performed, for example, based on the calculation and payment management of the referral fees, but is not limited to these examples.For example, the management unit provides a method for calculating referral fees and automatically calculates the fees to be paid by the user. The management unit can also manage the timing of referral fee payments so that the user can pay the fees at an appropriate time. Furthermore, the management unit can manage the history of referral fees so that the user can check the fees paid in the past. In this way, the property comparison system according to the embodiment allows the user to efficiently compare properties, understand total expenses, and manage the referral fees.

[0030] The collection unit can collect information on the price, location, facilities, and surrounding environment of a property. The collection unit, for example, collects property prices. For example, the collection unit collects property prices based on market prices and past transaction prices. The collection unit can also collect property location conditions. For example, the collection unit collects location conditions based on information on transportation access and surrounding facilities. The collection unit can also collect property facilities. For example, the collection unit collects facilities based on information on interior facilities and shared facilities. The collection unit can also collect information on the surrounding environment of a property. For example, the collection unit collects information on the surrounding environment based on information on public safety and noise levels. This allows detailed property information to be collected, enabling a user to select a more appropriate property. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can automatically collect information using AI when collecting property information from real estate sites on the Internet.

[0031] The comparison unit can compare prices and advantages based on the collected property information. The comparison unit, for example, compares property prices. For example, the comparison unit identifies the cheapest property based on the collected property prices. The comparison unit can also compare the location conditions of properties. For example, the comparison unit identifies the most convenient property based on the collected property location conditions. The comparison unit can also compare the facilities and surrounding environment of properties. For example, the comparison unit identifies the best property based on the facilities and surrounding environment of the collected properties. This allows the user to select the optimal property by comparing property prices and advantages based on the collected information. Some or all of the above-mentioned processing in the comparison unit may be performed using, for example, AI, or may be performed without using AI. For example, the comparison unit can input the collected property information into AI, which can automatically compare prices and advantages.

[0032] The calculation unit can calculate the purchase price and rent of the property, as well as the brokerage fee, registration fee, and moving fee. The calculation unit, for example, calculates the purchase price and rent of the property. For example, the calculation unit calculates the total expenses based on the purchase price and rent of the property entered by the user. The calculation unit can also calculate the brokerage fee. For example, the calculation unit calculates the brokerage fee based on a commission rate and fixed costs. The calculation unit can also calculate the registration fee. For example, the calculation unit calculates the registration fee based on the registration fee and registration license tax. The calculation unit can also calculate the moving fee. For example, the calculation unit calculates the moving fee based on the moving company's fees and packing fees. By calculating the total expenses of the property, the user can select the optimal property within their budget. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, AI, or may be performed without AI. For example, the calculation unit can input information entered by the user into AI, and the AI ​​can automatically calculate the total expenses.

[0033] The management unit can manage the referral commission paid to the referral agent when the user purchases or rents a property. The management unit, for example, calculates the referral commission. For example, the management unit calculates the referral commission based on a commission rate and fixed costs. The management unit can also manage the timing of referral commission payments. For example, the management unit manages a payment schedule so that the user can pay the commission at the appropriate time. Furthermore, the management unit can also manage the history of referral commissions. For example, the management unit manages the history so that the user can check the commissions paid in the past. In this way, by managing the referral commissions, the user can understand the details of the commissions. Some or all of the above-mentioned processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit can input the calculation of the referral commission and payment management into AI, which can then automatically manage the process.

[0034] The display unit can display collected property information, comparison results, calculated total expenses, and information on referral fees. The display unit, for example, displays collected property information. For example, the display unit displays information such as property price, location, facilities, and surrounding environment in a list format. The display unit can also display comparison results. For example, the display unit displays the results of comparing property prices and advantages in a graph format. The display unit can also display calculated total expenses. For example, the display unit displays total expenses such as the property purchase price, rent, brokerage fee, registration fee, and moving expenses in a list format. The display unit can also display information on referral fees. For example, the display unit displays details of the referral fees to be paid by the user. This allows the user to check the property information, comparison results, total expenses, and referral fees on a single screen. Some or all of the above-described processing in the display unit may be performed using, or without, AI. For example, the display unit can input collected property information and comparison results into AI, which then automatically generates the display content.

[0035] The collection unit can analyze the user's past property search history and select the optimal collection method. For example, the collection unit analyzes the characteristics of properties previously searched by the user and prioritizes collection of property information with similar characteristics. For example, the collection unit collects property information based on the user's search keywords and browsing history. The collection unit can also collect property information in the same price range based on the price range of properties previously searched by the user. For example, the collection unit analyzes the user's past search history and selects the optimal collection method. Furthermore, the collection unit can collect property information in the same area by taking into account the location conditions of properties previously searched by the user. For example, the collection unit selects the optimal collection method based on the user's past search history. This allows the collection of optimal property information based on the user's past search history, thereby providing information that meets the user's needs. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without AI. For example, the collection unit can input the user's past search history into AI, which can automatically select the optimal collection method.

[0036] When collecting property information, the collection unit can filter the property information based on the user's current living situation and areas of interest. For example, if the user lives with their family, the collection unit prioritizes collecting property information for families. For example, the collection unit collects property information based on the user's family composition and income status. In addition, if the user has a pet, the collection unit can also collect property information that allows pets. For example, the collection unit filters property information based on the user's living situation. Furthermore, if the user wants to shorten their commute time, the collection unit can collect property information close to their workplace. For example, the collection unit filters property information based on the user's areas of interest. In this way, by filtering property information based on the user's living situation and areas of interest, more appropriate property information can be provided. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the user's living situation and areas of interest into AI, and the AI ​​can automatically filter property information.

[0037] When collecting property information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. The collection unit, for example, prioritizes collecting property information close to the user's current location. For example, the collection unit collects property information based on the user's GPS data or address information. Furthermore, if the user is interested in a specific area, the collection unit can prioritize collecting property information in that area. For example, the collection unit collects property information based on the user's geographical location information. Furthermore, the collection unit can prioritize collecting property information in areas that are convenient for the user to commute to work or school. For example, the collection unit collects property information based on the user's geographical location information. In this way, by collecting property information by taking the user's geographical location information into account, more relevant information can be provided. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location information into AI, and the AI ​​can automatically collect highly relevant property information.

[0038] When collecting property information, the collection unit can analyze the user's social media activity and collect related property information. The collection unit, for example, collects related property information based on property information shared by the user on social media. For example, the collection unit collects property information based on the content of the user's social media posts and the number of followers. The collection unit can also collect information on real estate accounts the user follows on social media. For example, the collection unit collects property information based on the user's social media activity. Furthermore, the collection unit can collect property information in areas in which the user has shown interest on social media. For example, the collection unit collects property information based on the user's social media activity. In this way, related property information can be provided by analyzing the user's social media activity. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's social media activity into AI, which then automatically collects related property information.

[0039] The comparison unit can improve the accuracy of the comparison by taking into account the interrelationships between properties when making a comparison. For example, the comparison unit evaluates the price of a property being compared based on the prices of nearby properties. For example, the comparison unit evaluates the price based on the property's past transaction history. The comparison unit can also compare the facilities and conditions of properties in the same area to evaluate their superiority. For example, the comparison unit evaluates the superiority based on the property's facilities and conditions. Furthermore, the comparison unit can improve the accuracy of the comparison by taking into account the interrelationships between properties. For example, the comparison unit makes a comparison taking into account the influence of facilities surrounding the property. In this way, the accuracy of the comparison is improved by taking into account the interrelationships between properties. Some or all of the above-mentioned processing in the comparison unit may be performed using, for example, AI, or may be performed without using AI. For example, the comparison unit can input the interrelationships between properties into AI, which can automatically improve the accuracy of the comparison.

[0040] The comparison unit can make the comparison while taking into account the attribute information of the property provider. For example, the comparison unit evaluates whether the property provider is a trustworthy business. For example, the comparison unit evaluates the reliability based on the property provider's past transaction history. The comparison unit can also evaluate the properties being compared based on the reputation and reviews of the property provider. For example, the comparison unit evaluates the properties based on the reputation of the property provider. Furthermore, the comparison unit can make the comparison while taking into account the attribute information of the property provider. For example, the comparison unit makes the comparison based on the reliability of the property provider. In this way, by taking into account the attribute information of the property provider, more reliable comparison results can be provided. Some or all of the above-mentioned processing in the comparison unit may be performed using, for example, AI, or may be performed without using AI. For example, the comparison unit can input the attribute information of the property provider into AI, and the AI ​​can automatically make the comparison.

[0041] The comparison unit can take into account the geographical distribution of properties when making the comparison. The comparison unit, for example, evaluates properties to be compared based on the geographical proximity of the properties. For example, the comparison unit evaluates properties based on the surrounding environment and ease of access. The comparison unit can also make the comparison taking into account the geographical characteristics of the properties. For example, the comparison unit evaluates properties based on the geographical characteristics of the properties. Furthermore, the comparison unit can make the comparison taking into account the geographical distribution of the properties. For example, the comparison unit evaluates properties based on the geographical distribution of the properties. In this way, by taking the geographical distribution of the properties into account, more appropriate comparison results can be provided. Some or all of the above-mentioned processing in the comparison unit may be performed using, for example, AI, or may be performed without using AI. For example, the comparison unit can input the geographical distribution of properties into AI, and the AI ​​can automatically make the comparison.

[0042] The comparison unit can improve the accuracy of the comparison by referring to related literature on the property when making the comparison. The comparison unit, for example, evaluates the current price and conditions by referring to the property's past transaction history. For example, the comparison unit evaluates the advantages of the facilities and conditions based on the related literature on the property. The comparison unit can also evaluate the reliability of the property being compared by referring to the related literature on the property. For example, the comparison unit evaluates the property based on the related literature on the property. Furthermore, the comparison unit can improve the accuracy of the comparison by referring to the related literature on the property. For example, the comparison unit makes the comparison based on the related literature on the property. In this way, the accuracy of the comparison is improved by referring to the related literature on the property. Some or all of the above-mentioned processing in the comparison unit may be performed using, for example, AI, or may be performed without using AI. For example, the comparison unit can input related literature on the property into AI, which can automatically improve the accuracy of the comparison.

[0043] The display unit can select the optimal display method by referring to the user's past operation history when displaying information. For example, the display unit prioritizes displaying information that the user has viewed in the past. For example, the display unit selects the display method based on the user's click history or viewing time. The display unit can also select the optimal display method based on display methods used by the user in the past. For example, the display unit selects the display method based on the user's past operation history. Furthermore, the display unit can analyze the user's past operation history and select the most efficient display method. For example, the display unit selects the display method based on the user's operation pattern. This improves user convenience by providing the optimal display method based on the user's past operation history. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the user's operation history into AI, and the AI ​​can automatically select the optimal display method.

[0044] The display unit can apply different display algorithms depending on the property category when displaying. For example, in the case of a rental property, the display unit highlights the rent and contract terms. For example, the display unit applies a display algorithm based on the property category. In addition, in the case of a purchase property, the display unit can also highlight the purchase price and loan terms. For example, the display unit applies a display algorithm based on the property category. Furthermore, in the case of a commercial property, the display unit can also highlight the location and surrounding environment. For example, the display unit applies a display algorithm based on the property category. This allows the user to quickly obtain the information they need by providing an optimal display method according to the property category. Some or all of the above-mentioned processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input property category information into AI, and the AI ​​can automatically apply a display algorithm.

[0045] The display unit can select the optimal display method when displaying information by taking into account the user's device information. For example, if the user is using a smartphone, the display unit provides a display method that matches the screen size. For example, the display unit selects the display method based on the user's device information. Furthermore, if the user is using a tablet, the display unit can also provide a display method optimized for a large screen. For example, the display unit selects the display method based on the user's device information. Furthermore, if the user is using a desktop, the display unit can also display detailed information. For example, the display unit selects the display method based on the user's device information. In this way, the optimal display method can be provided by taking the user's device information into consideration. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the user's device information into AI, and the AI ​​can automatically select the optimal display method.

[0046] The display unit can adjust the display order based on the relevance of properties when displaying them. For example, the display unit displays property information in an area in which the user is interested at the top. For example, the display unit displays property information based on conditions set by the user. The display unit can also display related information of properties previously viewed by the user at the top. For example, the display unit displays property information based on the user's past browsing history. Furthermore, the display unit can display property information with high relevance at the top based on conditions set by the user. For example, the display unit displays property information based on conditions set by the user. This allows the user to quickly obtain the information they need by adjusting the display order based on the relevance of properties. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the relevance of properties into AI, and the AI ​​can automatically adjust the display order.

[0047] During calculation, the calculation unit can analyze the user's past consumption behavior and select the optimal calculation method. The calculation unit selects the optimal calculation method, for example, based on the prices of properties purchased by the user in the past. For example, the calculation unit selects the calculation method based on the user's purchase history and spending patterns. The calculation unit can also analyze the user's past consumption behavior and select the most efficient calculation method. For example, the calculation unit selects the calculation method based on the user's consumption pattern. Furthermore, the calculation unit can provide a calculation method for selecting the optimal property within the budget based on the user's past consumption behavior. For example, the calculation unit selects the calculation method based on the user's consumption pattern. In this way, the optimal calculation method can be provided by analyzing the user's past consumption behavior. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit can input the user's consumption behavior data into AI, and the AI ​​can automatically select the optimal calculation method.

[0048] The calculation unit can customize the calculation method based on the user's current living situation during calculation. For example, if the user lives with a family, the calculation unit provides a calculation method for families. For example, the calculation unit customizes the calculation method based on the user's family composition and income status. Furthermore, if the user lives alone, the calculation unit can also provide a calculation method for single living. For example, the calculation unit customizes the calculation method based on the user's living situation. Furthermore, if the user has a pet, the calculation unit can provide a calculation method that takes pet-related expenses into account. For example, the calculation unit customizes the calculation method based on the user's living situation. This allows for more appropriate calculation results to be provided by providing a calculation method that suits the user's living situation. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, AI, or may be performed without AI. For example, the calculation unit can input the user's living situation data into AI, and the AI ​​can automatically customize the calculation method.

[0049] The calculation unit can select the optimal calculation method by taking into account the user's geographical location information during calculation. For example, the calculation unit prioritizes calculations for properties close to the user's current location. For example, the calculation unit selects a calculation method based on the user's GPS data or address information. Furthermore, if the user is interested in a specific area, the calculation unit can prioritize calculations for properties in that area. For example, the calculation unit selects a calculation method based on the user's geographical location information. Furthermore, the calculation unit can prioritize calculations for properties in areas that are convenient for the user to commute to work or school. For example, the calculation unit selects a calculation method based on the user's geographical location information. This allows the optimal calculation method to be provided by taking the user's geographical location information into consideration. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit can input the user's geographical location information into AI, and the AI ​​can automatically select the optimal calculation method.

[0050] During calculation, the calculation unit can analyze the user's social media activity to suggest a calculation method. The calculation unit can suggest a relevant calculation method, for example, based on property information shared by the user on social media. For example, the calculation unit can suggest a calculation method based on the content of the user's social media posts and the number of followers. The calculation unit can also suggest a calculation method based on information about real estate accounts the user follows on social media. For example, the calculation unit can suggest a calculation method based on the user's social media activity. Furthermore, the calculation unit can suggest a calculation method for properties in an area in which the user has expressed interest on social media. For example, the calculation unit can suggest a calculation method based on the user's social media activity. In this way, relevant calculation methods can be provided by analyzing the user's social media activity. Some or all of the above-mentioned processing in the calculation unit can be performed, for example, using AI, or can be performed without using AI. For example, the calculation unit can input the user's social media activity into AI, and the AI ​​can automatically suggest a calculation method.

[0051] During management, the management unit can select the optimal management method by referring to past referral fee data. The management unit, for example, selects the optimal fee management method based on past referral fee data. For example, the management unit selects the management method based on past transaction data and fee history. The management unit can also analyze past referral fee data to select an efficient fee management method. For example, the management unit selects the management method based on past fee data. Furthermore, the management unit can refer to past referral fee data to predict fee fluctuations and adjust the management method. For example, the management unit selects the management method based on past fee data. In this way, by referring to past referral fee data, the optimal management method can be provided. Some or all of the above-mentioned processing in the management unit may be performed, for example, using AI, or may be performed without using AI. For example, the management unit can input past referral fee data into AI, which can automatically select the optimal management method.

[0052] During management, the management unit can apply different management algorithms depending on the category of referral fees. For example, in the case of a rental property, the management unit applies a management algorithm for rental fees. For example, the management unit applies a management algorithm based on the category of referral fees. In addition, in the case of a purchased property, the management unit can also apply a management algorithm for purchase fees. For example, the management unit applies a management algorithm based on the category of referral fees. In addition, in the case of a commercial property, the management unit can also apply a management algorithm for commercial fees. For example, the management unit applies a management algorithm based on the category of referral fees. This enables efficient management by providing an optimal management method according to the category of referral fees. Some or all of the above-mentioned processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit can input referral fee category information into AI, which then automatically applies the management algorithm.

[0053] The management unit can perform management taking into account the geographic distribution of referral fees. For example, the management unit prioritizes management of referral fees in geographically close areas. For example, the management unit selects a management method based on the geographic distribution of referral fees. The management unit can also perform management taking into account fluctuations in referral fees in a specific area. For example, the management unit selects a management method based on the geographic distribution of referral fees. Furthermore, the management unit can also select an efficient referral fee management method based on the geographic distribution. For example, the management unit selects a management method based on the geographic distribution of referral fees. This enables efficient management by taking into account the geographic distribution of referral fees. Some or all of the above-mentioned processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit can input the geographic distribution of referral fees into AI, and the AI ​​can automatically select a management method.

[0054] The management unit can improve the accuracy of management by referring to literature related to referral fees during management. The management unit, for example, evaluates current fees by referring to past data on referral fees. For example, the management unit selects a management method based on literature related to referral fees. The management unit can also select an efficient fee management method by referring to literature related to referral fees. For example, the management unit selects a management method based on literature related to referral fees. Furthermore, the management unit can predict fee fluctuations by referring to literature related to referral fees and adjust the management method. For example, the management unit selects a management method based on literature related to referral fees. In this way, referring to literature related to referral fees improves the accuracy of management. Some or all of the above-mentioned processing in the management unit may be performed, for example, using AI, or may be performed without using AI. For example, the management unit can input literature related to referral fees into AI, which can then automatically select a management method.

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

[0056] The collection unit can also analyze the user's purchasing history and collect new property information based on the characteristics of properties purchased in the past. For example, the collection unit analyzes the price range and location conditions of properties purchased in the past by the user, and prioritizes collecting property information with similar characteristics. The collection unit can also take into account the facilities and surrounding environment of properties purchased in the past by the user and collect property information with similar conditions. Furthermore, the collection unit can also collect property information taking into account seasonal and market trends based on the purchase dates of properties purchased in the past by the user. This makes it possible to provide optimal property information based on the user's past purchasing history.

[0057] The comparison unit can also compare the energy efficiency of properties. For example, the comparison unit can evaluate energy efficiency based on the energy consumption and insulation performance of the property. The comparison unit can also compare the renewable energy usage status of properties to identify environmentally friendly properties. Furthermore, the comparison unit can also take into account government subsidies and tax incentives related to the energy efficiency of properties. This makes it possible to select properties that take energy efficiency into consideration.

[0058] The calculation unit can also propose a long-term payment plan based on the user's predicted future income. For example, the calculation unit can predict future income increases taking into account the user's occupation and career path. The calculation unit can also predict future increases in expenses based on the user's age and family composition and adjust the payment plan accordingly. Furthermore, the calculation unit can predict future interest rate fluctuations taking into account economic conditions and market trends and reflect them in the payment plan. This allows the user to plan property purchases taking into account future income and expenses.

[0059] The management unit can also propose optimal loan terms based on the user's credit information. For example, the management unit can analyze the user's credit score and past loan history to propose optimal interest rates and repayment periods. The management unit can also propose a reasonable repayment plan taking into account the user's income and expenditures. Furthermore, the management unit can compare loan offers from multiple financial institutions based on the user's credit information and provide the optimal option. This allows the user to purchase a property with optimal loan terms based on their own credit information.

[0060] The display unit can also customize the display style of the property information according to the user's visual preferences. For example, the display unit can adjust the display style of the property information based on the user's preferred colors and fonts. The display unit can also customize the display method of the property information based on the user's preferred layout and design. Furthermore, the display unit can adjust the display order and emphasis points of the property information according to the user's visual preferences. This allows the user to view visually appealing property information that suits their preferences.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The collection unit collects property information. This property information includes the property's price, location, facilities, surrounding environment, etc. The collection unit can collect property information from real estate sites on the Internet, as well as obtain property information directly from real estate agents. It can also collect property information entered by the user, providing a form for the user to enter the conditions for the property they desire, and collect property information based on that information. Step 2: The comparison unit compares prices and advantages based on the property information collected by the collection unit. The comparison is based on criteria such as property price, location, facilities, and surrounding environment. For example, it can compare property prices to identify the cheapest property, or compare locations to identify the most convenient property. It can also compare facilities and surrounding environment to identify the best property. Step 3: The display unit displays the comparison results obtained by the comparison unit. The display is done in list format or graph format. For example, property prices can be displayed in list format to allow users to easily compare properties. The location conditions of the properties can also be displayed on a map to allow users to compare visually. Furthermore, the facilities and surrounding environment of the properties can also be displayed in graph format to allow users to intuitively compare properties. Step 4: The calculation unit calculates the purchase price and rent of the property, as well as various expenses such as brokerage fees, registration fees, and moving costs. The calculation is performed based on the information entered by the user. For example, a form is provided for entering the purchase price and rent of the property, and the total expenses are calculated based on that information. It is also possible to automatically calculate various expenses such as brokerage fees, registration fees, and moving costs. It is also possible to perform calculations to help the user select the best property within their desired budget. Step 5: The management unit manages the referral commission to be paid to the referral agent when the user purchases or rents a property. The management is carried out based on the calculation and payment management of the referral commission. For example, it provides a method for calculating the referral commission and automatically calculates the commission to be paid by the user. It can also manage the timing of referral commission payments so that the user can pay the commission at the appropriate time. Furthermore, it can manage the history of referral commissions so that the user can check the commissions they have paid in the past.

[0063] (Example 2) A property comparison system according to an embodiment of the present invention allows users to easily compare optimal properties on a single screen when searching for a property to purchase or rent. This system allows users to easily compare property prices and advantages, enabling a user-oriented property search. It also provides a function for easily calculating purchase and miscellaneous expenses, allowing users to understand total expenses. The service is operated on a referral fee basis. First, when a user searches for a property, the system provides information for comparing property prices and advantages on a single screen. For example, information such as property price, location, amenities, and surrounding environment is displayed in a list, allowing users to easily compare properties. This allows users to efficiently compare multiple properties and select the optimal one. Second, the system provides a function for easily calculating miscellaneous expenses associated with purchasing or renting. For example, by entering miscellaneous expenses such as brokerage fees, registration fees, and moving costs in addition to the purchase price and rent of the property, the system automatically calculates total expenses. This allows users to understand total property expenses and select the optimal property within their budget. Furthermore, the service is operated on a referral fee basis. For example, the referral fee paid to a referral agent when a user purchases or rents a property is counted as an operating fee for the service. This allows users to use the service free of charge, while the operator earns revenue through referral fees. This service allows users to easily compare property prices and find advantages, and understand the total costs involved in purchasing or renting. Furthermore, by operating on referral fees, users can use the service free of charge, which is expected to attract more users. This allows the property comparison system to enable users to efficiently compare properties, understand the total costs, and manage referral fees.

[0064] A property comparison system according to an embodiment includes a collection unit, a comparison unit, a display unit, a calculation unit, and a management unit. The collection unit collects property information. The property information includes, for example, property price, location, facilities, and surrounding environment, but is not limited to these examples. The collection unit collects property information from, for example, real estate websites on the Internet. The collection unit can also obtain property information directly from real estate agents. The collection unit can also collect property information entered by a user. For example, the collection unit provides a form for a user to input desired property conditions and collects property information based on the information. The comparison unit compares prices and advantages based on the property information collected by the collection unit. The comparison is performed based on criteria such as property price, location, facilities, and surrounding environment, but is not limited to these examples. For example, the comparison unit compares property prices and identifies the cheapest property. The comparison unit can also compare property locations and identify the most convenient property. The comparison unit can also compare property facilities and surrounding environment to identify the best property. The display unit displays the comparison results obtained by the comparison unit. The display may be, for example, in a list format or a graph format, but is not limited to these examples. For example, the display unit may display property prices in a list format, allowing the user to easily compare properties. The display unit may also display the location of the property on a map, allowing the user to visually compare properties. Furthermore, the display unit may display the property's facilities and surrounding environment in a graph format, allowing the user to intuitively compare properties. The calculation unit may calculate various expenses, such as brokerage fees, registration fees, and moving costs, in addition to the purchase price and rent of the property. The calculation may be performed, for example, based on information entered by the user, but is not limited to these examples. For example, the calculation unit may provide a form for entering the purchase price and rent of the property and calculate total expenses based on the information. The calculation unit may also automatically calculate various expenses, such as brokerage fees, registration fees, and moving costs. Furthermore, the calculation unit may perform calculations to help the user select the optimal property within their desired budget. The management unit may manage referral fees paid to the referral agent when the user purchases or rents a property. The management may be performed, for example, based on the calculation and payment management of the referral fees, but is not limited to these examples.For example, the management unit provides a method for calculating referral fees and automatically calculates the fees to be paid by the user. The management unit can also manage the timing of referral fee payments so that the user can pay the fees at an appropriate time. Furthermore, the management unit can manage the history of referral fees so that the user can check the fees paid in the past. In this way, the property comparison system according to the embodiment allows the user to efficiently compare properties, understand total expenses, and manage the referral fees.

[0065] The collection unit can collect information on the price, location, facilities, and surrounding environment of a property. The collection unit, for example, collects property prices. For example, the collection unit collects property prices based on market prices and past transaction prices. The collection unit can also collect property location conditions. For example, the collection unit collects location conditions based on information on transportation access and surrounding facilities. The collection unit can also collect property facilities. For example, the collection unit collects facilities based on information on interior facilities and shared facilities. The collection unit can also collect information on the surrounding environment of a property. For example, the collection unit collects information on the surrounding environment based on information on public safety and noise levels. This allows detailed property information to be collected, enabling a user to select a more appropriate property. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can automatically collect information using AI when collecting property information from real estate sites on the Internet.

[0066] The comparison unit can compare prices and advantages based on the collected property information. The comparison unit, for example, compares property prices. For example, the comparison unit identifies the cheapest property based on the collected property prices. The comparison unit can also compare the location conditions of properties. For example, the comparison unit identifies the most convenient property based on the collected property location conditions. The comparison unit can also compare the facilities and surrounding environment of properties. For example, the comparison unit identifies the best property based on the facilities and surrounding environment of the collected properties. This allows the user to select the optimal property by comparing property prices and advantages based on the collected information. Some or all of the above-mentioned processing in the comparison unit may be performed using, for example, AI, or may be performed without using AI. For example, the comparison unit can input the collected property information into AI, which can automatically compare prices and advantages.

[0067] The calculation unit can calculate the purchase price and rent of the property, as well as the brokerage fee, registration fee, and moving fee. The calculation unit, for example, calculates the purchase price and rent of the property. For example, the calculation unit calculates the total expenses based on the purchase price and rent of the property entered by the user. The calculation unit can also calculate the brokerage fee. For example, the calculation unit calculates the brokerage fee based on a commission rate and fixed costs. The calculation unit can also calculate the registration fee. For example, the calculation unit calculates the registration fee based on the registration fee and registration license tax. The calculation unit can also calculate the moving fee. For example, the calculation unit calculates the moving fee based on the moving company's fees and packing fees. By calculating the total expenses of the property, the user can select the optimal property within their budget. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, AI, or may be performed without AI. For example, the calculation unit can input information entered by the user into AI, and the AI ​​can automatically calculate the total expenses.

[0068] The management unit can manage the referral commission paid to the referral agent when the user purchases or rents a property. The management unit, for example, calculates the referral commission. For example, the management unit calculates the referral commission based on a commission rate and fixed costs. The management unit can also manage the timing of referral commission payments. For example, the management unit manages a payment schedule so that the user can pay the commission at the appropriate time. Furthermore, the management unit can also manage the history of referral commissions. For example, the management unit manages the history so that the user can check the commissions paid in the past. In this way, by managing the referral commissions, the user can understand the details of the commissions. Some or all of the above-mentioned processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit can input the calculation of the referral commission and payment management into AI, which can then automatically manage the process.

[0069] The display unit can display collected property information, comparison results, calculated total expenses, and information on referral fees. The display unit, for example, displays collected property information. For example, the display unit displays information such as property price, location, facilities, and surrounding environment in a list format. The display unit can also display comparison results. For example, the display unit displays the results of comparing property prices and advantages in a graph format. The display unit can also display calculated total expenses. For example, the display unit displays total expenses such as the property purchase price, rent, brokerage fee, registration fee, and moving expenses in a list format. The display unit can also display information on referral fees. For example, the display unit displays details of the referral fees to be paid by the user. This allows the user to check the property information, comparison results, total expenses, and referral fees on a single screen. Some or all of the above-described processing in the display unit may be performed using, or without, AI. For example, the display unit can input collected property information and comparison results into AI, which then automatically generates the display content.

[0070] The collection unit can estimate the user's emotions and adjust the timing of collecting property information based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit delays the collection timing and collects property information when the user is relaxed. For example, the collection unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. Furthermore, if the user is excited, the collection unit can immediately collect property information to attract the user's interest. For example, the collection unit records the user's voice and estimates the emotion using voice analysis technology. Furthermore, if the user is tired, the collection unit can adjust the collection timing to collect property information after the user has rested. For example, the collection unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. This allows the timing of collecting property information to be adjusted according to the user's emotions, thereby providing information at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit may input the user's emotion data into the generation AI, and the generation AI may automatically adjust the collection timing.

[0071] The collection unit can analyze the user's past property search history and select the optimal collection method. For example, the collection unit analyzes the characteristics of properties previously searched by the user and prioritizes collection of property information with similar characteristics. For example, the collection unit collects property information based on the user's search keywords and browsing history. The collection unit can also collect property information in the same price range based on the price range of properties previously searched by the user. For example, the collection unit analyzes the user's past search history and selects the optimal collection method. Furthermore, the collection unit can collect property information in the same area by taking into account the location conditions of properties previously searched by the user. For example, the collection unit selects the optimal collection method based on the user's past search history. This allows the collection of optimal property information based on the user's past search history, thereby providing information that meets the user's needs. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without AI. For example, the collection unit can input the user's past search history into AI, which can automatically select the optimal collection method.

[0072] When collecting property information, the collection unit can filter the property information based on the user's current living situation and areas of interest. For example, if the user lives with their family, the collection unit prioritizes collecting property information for families. For example, the collection unit collects property information based on the user's family composition and income status. In addition, if the user has a pet, the collection unit can also collect property information that allows pets. For example, the collection unit filters property information based on the user's living situation. Furthermore, if the user wants to shorten their commute time, the collection unit can collect property information close to their workplace. For example, the collection unit filters property information based on the user's areas of interest. In this way, by filtering property information based on the user's living situation and areas of interest, more appropriate property information can be provided. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the user's living situation and areas of interest into AI, and the AI ​​can automatically filter property information.

[0073] The collection unit can estimate the user's emotions and prioritize the property information to be collected based on the estimated user emotions. For example, if the user is relaxed, the collection unit prioritizes collecting detailed property information. For example, the collection unit captures the user's facial expressions with a camera and estimates the user's emotions using an emotion estimation algorithm. Furthermore, if the user is in a hurry, the collection unit can prioritize collecting basic information such as price and location. For example, the collection unit records the user's voice and estimates the user's emotions using voice analysis technology. Furthermore, if the user is excited, the collection unit can prioritize collecting the latest property information. For example, the collection unit collects the user's biometric data (heart rate and electrodermal activity) using a sensor and estimates the user's emotions using an emotion estimation algorithm. This allows the system to prioritize property information according to the user's emotions, thereby providing more appropriate information. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generative AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input user emotion data into the generation AI, and the generation AI may automatically determine the priority of property information.

[0074] When collecting property information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. The collection unit, for example, prioritizes collecting property information close to the user's current location. For example, the collection unit collects property information based on the user's GPS data or address information. Furthermore, if the user is interested in a specific area, the collection unit can prioritize collecting property information in that area. For example, the collection unit collects property information based on the user's geographical location information. Furthermore, the collection unit can prioritize collecting property information in areas that are convenient for the user to commute to work or school. For example, the collection unit collects property information based on the user's geographical location information. In this way, by collecting property information by taking the user's geographical location information into account, more relevant information can be provided. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location information into AI, and the AI ​​can automatically collect highly relevant property information.

[0075] When collecting property information, the collection unit can analyze the user's social media activity and collect related property information. The collection unit, for example, collects related property information based on property information shared by the user on social media. For example, the collection unit collects property information based on the content of the user's social media posts and the number of followers. The collection unit can also collect information on real estate accounts the user follows on social media. For example, the collection unit collects property information based on the user's social media activity. Furthermore, the collection unit can collect property information in areas in which the user has shown interest on social media. For example, the collection unit collects property information based on the user's social media activity. In this way, related property information can be provided by analyzing the user's social media activity. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's social media activity into AI, which then automatically collects related property information.

[0076] The comparison unit can estimate the user's emotions and adjust the comparison criteria based on the estimated user emotions. For example, if the user is relaxed, the comparison unit provides detailed comparison criteria. For example, the comparison unit captures the user's facial expressions with a camera and estimates the emotion using an emotion estimation algorithm. Furthermore, if the user is in a hurry, the comparison unit can provide basic comparison criteria such as price and location. For example, the comparison unit records the user's voice and estimates the emotion using voice analysis technology. Furthermore, if the user is excited, the comparison unit can adjust the comparison criteria based on the latest property information. For example, the comparison unit collects the user's biometric data (heart rate and electrodermal activity) using a sensor and estimates the emotion using an emotion estimation algorithm. This allows the comparison unit to adjust the comparison criteria according to the user's emotions, thereby providing more appropriate comparison results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the comparison unit may be performed using, for example, AI, or may be performed without using AI. For example, the comparison unit may input user emotion data into the generation AI, which may then automatically adjust the comparison criteria.

[0077] The comparison unit can improve the accuracy of the comparison by taking into account the interrelationships between properties when making a comparison. For example, the comparison unit evaluates the price of a property being compared based on the prices of nearby properties. For example, the comparison unit evaluates the price based on the property's past transaction history. The comparison unit can also compare the facilities and conditions of properties in the same area to evaluate their superiority. For example, the comparison unit evaluates the superiority based on the property's facilities and conditions. Furthermore, the comparison unit can improve the accuracy of the comparison by taking into account the interrelationships between properties. For example, the comparison unit makes a comparison taking into account the influence of facilities surrounding the property. In this way, the accuracy of the comparison is improved by taking into account the interrelationships between properties. Some or all of the above-mentioned processing in the comparison unit may be performed using, for example, AI, or may be performed without using AI. For example, the comparison unit can input the interrelationships between properties into AI, which can automatically improve the accuracy of the comparison.

[0078] The comparison unit can make the comparison while taking into account the attribute information of the property provider. For example, the comparison unit evaluates whether the property provider is a trustworthy business. For example, the comparison unit evaluates the reliability based on the property provider's past transaction history. The comparison unit can also evaluate the properties being compared based on the reputation and reviews of the property provider. For example, the comparison unit evaluates the properties based on the reputation of the property provider. Furthermore, the comparison unit can make the comparison while taking into account the attribute information of the property provider. For example, the comparison unit makes the comparison based on the reliability of the property provider. In this way, by taking into account the attribute information of the property provider, more reliable comparison results can be provided. Some or all of the above-mentioned processing in the comparison unit may be performed using, for example, AI, or may be performed without using AI. For example, the comparison unit can input the attribute information of the property provider into AI, and the AI ​​can automatically make the comparison.

[0079] The comparison unit can estimate the user's emotions and adjust the display order of the comparison results based on the estimated user emotions. For example, if the user is relaxed, the comparison unit displays detailed comparison results at the top. For example, the comparison unit captures the user's facial expressions with a camera and estimates their emotions using an emotion estimation algorithm. Furthermore, if the user is in a hurry, the comparison unit can display basic comparison results such as price and location at the top. For example, the comparison unit records the user's voice and estimates their emotions using voice analysis technology. Furthermore, if the user is excited, the comparison unit can display the latest property information at the top. For example, the comparison unit collects the user's biometric data (heart rate and electrodermal activity) using a sensor and estimates their emotions using an emotion estimation algorithm. This allows the display order of the comparison results to be adjusted according to the user's emotions, thereby providing more appropriate information. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the comparison unit may be performed using, for example, AI, or may be performed without using AI. For example, the comparison unit may input user emotion data into the generation AI, which may then automatically adjust the display order.

[0080] The comparison unit can take into account the geographical distribution of properties when making the comparison. The comparison unit, for example, evaluates properties to be compared based on the geographical proximity of the properties. For example, the comparison unit evaluates properties based on the surrounding environment and ease of access. The comparison unit can also make the comparison taking into account the geographical characteristics of the properties. For example, the comparison unit evaluates properties based on the geographical characteristics of the properties. Furthermore, the comparison unit can make the comparison taking into account the geographical distribution of the properties. For example, the comparison unit evaluates properties based on the geographical distribution of the properties. In this way, by taking the geographical distribution of the properties into account, more appropriate comparison results can be provided. Some or all of the above-mentioned processing in the comparison unit may be performed using, for example, AI, or may be performed without using AI. For example, the comparison unit can input the geographical distribution of properties into AI, and the AI ​​can automatically make the comparison.

[0081] The comparison unit can improve the accuracy of the comparison by referring to related literature on the property when making the comparison. The comparison unit, for example, evaluates the current price and conditions by referring to the property's past transaction history. For example, the comparison unit evaluates the advantages of the facilities and conditions based on the related literature on the property. The comparison unit can also evaluate the reliability of the property being compared by referring to the related literature on the property. For example, the comparison unit evaluates the property based on the related literature on the property. Furthermore, the comparison unit can improve the accuracy of the comparison by referring to the related literature on the property. For example, the comparison unit makes the comparison based on the related literature on the property. In this way, the accuracy of the comparison is improved by referring to the related literature on the property. Some or all of the above-mentioned processing in the comparison unit may be performed using, for example, AI, or may be performed without using AI. For example, the comparison unit can input related literature on the property into AI, which can automatically improve the accuracy of the comparison.

[0082] The display unit can estimate the user's emotions and adjust the display method based on the estimated user emotions. For example, if the user is relaxed, the display unit displays detailed information. For example, the display unit captures the user's facial expression with a camera and estimates the user's emotions using an emotion estimation algorithm. Furthermore, if the user is in a hurry, the display unit can display basic information such as price and location. For example, the display unit records the user's voice and estimates the user's emotions using voice analysis technology. Furthermore, if the user is excited, the display unit can display the latest property information. For example, the display unit collects the user's biometric data (heart rate and electrodermal activity) using a sensor and estimates the user's emotions using an emotion estimation algorithm. This allows the display method to be adjusted according to the user's emotions, thereby providing more appropriate information. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit may input user emotion data into a generation AI, which may then automatically adjust the display method.

[0083] The display unit can select the optimal display method by referring to the user's past operation history when displaying information. For example, the display unit prioritizes displaying information that the user has viewed in the past. For example, the display unit selects the display method based on the user's click history or viewing time. The display unit can also select the optimal display method based on display methods used by the user in the past. For example, the display unit selects the display method based on the user's past operation history. Furthermore, the display unit can analyze the user's past operation history and select the most efficient display method. For example, the display unit selects the display method based on the user's operation pattern. This improves user convenience by providing the optimal display method based on the user's past operation history. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the user's operation history into AI, and the AI ​​can automatically select the optimal display method.

[0084] The display unit can apply different display algorithms depending on the property category when displaying. For example, in the case of a rental property, the display unit highlights the rent and contract terms. For example, the display unit applies a display algorithm based on the property category. In addition, in the case of a purchase property, the display unit can also highlight the purchase price and loan terms. For example, the display unit applies a display algorithm based on the property category. Furthermore, in the case of a commercial property, the display unit can also highlight the location and surrounding environment. For example, the display unit applies a display algorithm based on the property category. This allows the user to quickly obtain the information they need by providing an optimal display method according to the property category. Some or all of the above-mentioned processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input property category information into AI, and the AI ​​can automatically apply a display algorithm.

[0085] The display unit can estimate the user's emotions and prioritize display content based on the estimated user emotions. For example, if the user is relaxed, the display unit prioritizes displaying detailed information. For example, the display unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. Furthermore, if the user is in a hurry, the display unit can prioritize displaying basic information such as price and location. For example, the display unit records the user's voice and estimates the emotion using voice analysis technology. Furthermore, if the user is excited, the display unit can prioritize displaying the latest property information. For example, the display unit collects the user's biometric data (heart rate and electrodermal activity) using a sensor and estimates the emotion using an emotion estimation algorithm. This allows for prioritizing display content based on the user's emotions, thereby providing more appropriate information. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit may input user emotion data into a generation AI, which may then automatically determine the priority of the display content.

[0086] The display unit can select the optimal display method when displaying information by taking into account the user's device information. For example, if the user is using a smartphone, the display unit provides a display method that matches the screen size. For example, the display unit selects the display method based on the user's device information. Furthermore, if the user is using a tablet, the display unit can also provide a display method optimized for a large screen. For example, the display unit selects the display method based on the user's device information. Furthermore, if the user is using a desktop, the display unit can also display detailed information. For example, the display unit selects the display method based on the user's device information. In this way, the optimal display method can be provided by taking the user's device information into consideration. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the user's device information into AI, and the AI ​​can automatically select the optimal display method.

[0087] The display unit can adjust the display order based on the relevance of properties when displaying them. For example, the display unit displays property information in an area in which the user is interested at the top. For example, the display unit displays property information based on conditions set by the user. The display unit can also display related information of properties previously viewed by the user at the top. For example, the display unit displays property information based on the user's past browsing history. Furthermore, the display unit can display property information with high relevance at the top based on conditions set by the user. For example, the display unit displays property information based on conditions set by the user. This allows the user to quickly obtain the information they need by adjusting the display order based on the relevance of properties. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the relevance of properties into AI, and the AI ​​can automatically adjust the display order.

[0088] The calculation unit can estimate the user's emotion and adjust the calculation method based on the estimated user emotion. For example, if the user is relaxed, the calculation unit provides a detailed calculation method. For example, the calculation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. Furthermore, if the user is in a hurry, the calculation unit can provide a simplified calculation method. For example, the calculation unit records the user's voice and estimates the emotion using voice analysis technology. Furthermore, if the user is excited, the calculation unit can provide a more updated calculation method. For example, the calculation unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. This allows the calculation method to be adjusted according to the user's emotion, thereby providing more appropriate calculation results. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit may input user emotion data into the generation AI, and the generation AI may automatically adjust the calculation method.

[0089] During calculation, the calculation unit can analyze the user's past consumption behavior and select the optimal calculation method. The calculation unit selects the optimal calculation method, for example, based on the prices of properties purchased by the user in the past. For example, the calculation unit selects the calculation method based on the user's purchase history and spending patterns. The calculation unit can also analyze the user's past consumption behavior and select the most efficient calculation method. For example, the calculation unit selects the calculation method based on the user's consumption pattern. Furthermore, the calculation unit can provide a calculation method for selecting the optimal property within the budget based on the user's past consumption behavior. For example, the calculation unit selects the calculation method based on the user's consumption pattern. In this way, the optimal calculation method can be provided by analyzing the user's past consumption behavior. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit can input the user's consumption behavior data into AI, and the AI ​​can automatically select the optimal calculation method.

[0090] The calculation unit can customize the calculation method based on the user's current living situation during calculation. For example, if the user lives with a family, the calculation unit provides a calculation method for families. For example, the calculation unit customizes the calculation method based on the user's family composition and income status. Furthermore, if the user lives alone, the calculation unit can also provide a calculation method for single living. For example, the calculation unit customizes the calculation method based on the user's living situation. Furthermore, if the user has a pet, the calculation unit can provide a calculation method that takes pet-related expenses into account. For example, the calculation unit customizes the calculation method based on the user's living situation. This allows for more appropriate calculation results to be provided by providing a calculation method that suits the user's living situation. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, AI, or may be performed without AI. For example, the calculation unit can input the user's living situation data into AI, and the AI ​​can automatically customize the calculation method.

[0091] The calculation unit can estimate the user's emotions and determine the priority of calculations based on the estimated user emotions. For example, if the user is relaxed, the calculation unit can prioritize detailed calculations. For example, the calculation unit can capture the user's facial expressions with a camera and estimate the user's emotions using an emotion estimation algorithm. Furthermore, if the user is in a hurry, the calculation unit can prioritize basic calculations. For example, the calculation unit can record the user's voice and estimate the user's emotions using voice analysis technology. Furthermore, if the user is excited, the calculation unit can prioritize the most recent calculations. For example, the calculation unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor and estimate the user's emotions using an emotion estimation algorithm. This allows for calculation prioritization based on the user's emotions, thereby providing more appropriate calculation results. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit may input user emotion data into the generation AI, and the generation AI may automatically determine the priority of calculations.

[0092] The calculation unit can select the optimal calculation method by taking into account the user's geographical location information during calculation. For example, the calculation unit prioritizes calculations for properties close to the user's current location. For example, the calculation unit selects a calculation method based on the user's GPS data or address information. Furthermore, if the user is interested in a specific area, the calculation unit can prioritize calculations for properties in that area. For example, the calculation unit selects a calculation method based on the user's geographical location information. Furthermore, the calculation unit can prioritize calculations for properties in areas that are convenient for the user to commute to work or school. For example, the calculation unit selects a calculation method based on the user's geographical location information. This allows the optimal calculation method to be provided by taking the user's geographical location information into consideration. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit can input the user's geographical location information into AI, and the AI ​​can automatically select the optimal calculation method.

[0093] During calculation, the calculation unit can analyze the user's social media activity to suggest a calculation method. The calculation unit can suggest a relevant calculation method, for example, based on property information shared by the user on social media. For example, the calculation unit can suggest a calculation method based on the content of the user's social media posts and the number of followers. The calculation unit can also suggest a calculation method based on information about real estate accounts the user follows on social media. For example, the calculation unit can suggest a calculation method based on the user's social media activity. Furthermore, the calculation unit can suggest a calculation method for properties in an area in which the user has expressed interest on social media. For example, the calculation unit can suggest a calculation method based on the user's social media activity. In this way, relevant calculation methods can be provided by analyzing the user's social media activity. Some or all of the above-mentioned processing in the calculation unit can be performed, for example, using AI, or can be performed without using AI. For example, the calculation unit can input the user's social media activity into AI, and the AI ​​can automatically suggest a calculation method.

[0094] The management unit can estimate the user's emotions and adjust the referral fee management method based on the estimated user emotions. For example, if the user is relaxed, the management unit provides detailed fee information. For example, the management unit captures the user's facial expressions with a camera and estimates the user's emotions using an emotion estimation algorithm. Furthermore, if the user is in a hurry, the management unit can provide simplified fee information. For example, the management unit records the user's voice and estimates the user's emotions using voice analysis technology. Furthermore, if the user is excited, the management unit can provide updated fee information. For example, the management unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the user's emotions using an emotion estimation algorithm. This allows the system to provide more appropriate information by adjusting the referral fee management method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit may input user emotion data into the generation AI, which may then automatically adjust the management method.

[0095] During management, the management unit can select the optimal management method by referring to past referral fee data. The management unit, for example, selects the optimal fee management method based on past referral fee data. For example, the management unit selects the management method based on past transaction data and fee history. The management unit can also analyze past referral fee data to select an efficient fee management method. For example, the management unit selects the management method based on past fee data. Furthermore, the management unit can refer to past referral fee data to predict fee fluctuations and adjust the management method. For example, the management unit selects the management method based on past fee data. In this way, by referring to past referral fee data, the optimal management method can be provided. Some or all of the above-mentioned processing in the management unit may be performed, for example, using AI, or may be performed without using AI. For example, the management unit can input past referral fee data into AI, which can automatically select the optimal management method.

[0096] During management, the management unit can apply different management algorithms depending on the category of referral fees. For example, in the case of a rental property, the management unit applies a management algorithm for rental fees. For example, the management unit applies a management algorithm based on the category of referral fees. In addition, in the case of a purchased property, the management unit can also apply a management algorithm for purchase fees. For example, the management unit applies a management algorithm based on the category of referral fees. In addition, in the case of a commercial property, the management unit can also apply a management algorithm for commercial fees. For example, the management unit applies a management algorithm based on the category of referral fees. This enables efficient management by providing an optimal management method according to the category of referral fees. Some or all of the above-mentioned processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit can input referral fee category information into AI, which then automatically applies the management algorithm.

[0097] The management unit can estimate the user's emotions and determine the priority of referral fees based on the estimated user emotions. For example, if the user is relaxed, the management unit can prioritize displaying detailed fee information. For example, the management unit can capture the user's facial expressions with a camera and estimate the user's emotions using an emotion estimation algorithm. Furthermore, if the user is in a hurry, the management unit can prioritize displaying basic fee information. For example, the management unit can record the user's voice and estimate the user's emotions using voice analysis technology. Furthermore, if the user is excited, the management unit can prioritize displaying the latest fee information. For example, the management unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor and estimate the user's emotions using an emotion estimation algorithm. This allows for more appropriate information to be provided by prioritizing referral fees based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit may input user emotion data into the generation AI, which may then automatically determine priorities.

[0098] The management unit can perform management taking into account the geographic distribution of referral fees. For example, the management unit prioritizes management of referral fees in geographically close areas. For example, the management unit selects a management method based on the geographic distribution of referral fees. The management unit can also perform management taking into account fluctuations in referral fees in a specific area. For example, the management unit selects a management method based on the geographic distribution of referral fees. Furthermore, the management unit can also select an efficient referral fee management method based on the geographic distribution. For example, the management unit selects a management method based on the geographic distribution of referral fees. This enables efficient management by taking into account the geographic distribution of referral fees. Some or all of the above-mentioned processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit can input the geographic distribution of referral fees into AI, and the AI ​​can automatically select a management method.

[0099] The management unit can improve the accuracy of management by referring to literature related to referral fees during management. The management unit, for example, evaluates current fees by referring to past data on referral fees. For example, the management unit selects a management method based on literature related to referral fees. The management unit can also select an efficient fee management method by referring to literature related to referral fees. For example, the management unit selects a management method based on literature related to referral fees. Furthermore, the management unit can predict fee fluctuations by referring to literature related to referral fees and adjust the management method. For example, the management unit selects a management method based on literature related to referral fees. In this way, referring to literature related to referral fees improves the accuracy of management. Some or all of the above-mentioned processing in the management unit may be performed, for example, using AI, or may be performed without using AI. For example, the management unit can input literature related to referral fees into AI, which can then automatically select a management method. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, comparison unit, display unit, calculation unit, and management unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart device 14 and collects property information from real estate sites on the Internet. The comparison unit is realized by the specific processing unit 290 of the data processing device 12 and compares prices and advantages based on the collected property information. The display unit is realized by the display 40A of the smart device 14 and displays the comparison results in a list or graph format. The calculation unit is realized by the specific processing unit 290 of the data processing device 12 and calculates the purchase price, rent, and various expenses of the property. The management unit is realized by the specific processing unit 290 of the data processing device 12 and calculates the referral fee and manages payments. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, comparison unit, display unit, calculation unit, and management unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart glasses 214 and collects property information from real estate sites on the Internet. The comparison unit is realized by the specific processing unit 290 of the data processing device 12 and compares prices and advantages based on the collected property information. The display unit is realized by the display of the smart glasses 214 and displays the comparison results in a list or graph format. The calculation unit is realized by the specific processing unit 290 of the data processing device 12 and calculates the purchase price and rent of the property as well as various expenses. The management unit is realized by the specific processing unit 290 of the data processing device 12 and manages the calculation and payment of referral fees. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, comparison unit, display unit, calculation unit, and management unit, described above, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the headset terminal 314 and collects property information from real estate sites on the Internet. The comparison unit is realized by the specific processing unit 290 of the data processing device 12 and compares prices and advantages based on the collected property information. The display unit is realized by the display 343 of the headset terminal 314 and displays the comparison results in a list or graph format. The calculation unit is realized by the specific processing unit 290 of the data processing device 12 and calculates the purchase price, rent, and various expenses of the property. The management unit is realized by the specific processing unit 290 of the data processing device 12 and calculates the referral fee and manages payments. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, comparison unit, display unit, calculation unit, and management unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the robot 414 and collects property information from real estate sites on the Internet. The comparison unit is realized by the specific processing unit 290 of the data processing device 12 and compares prices and advantages based on the collected property information. The display unit is realized by the display of the robot 414 and displays the comparison results in a list or graph format. The calculation unit is realized by the specific processing unit 290 of the data processing device 12 and calculates the purchase price and rent of the property as well as various expenses. The management unit is realized by the specific processing unit 290 of the data processing device 12 and calculates the referral fee and manages payments.

[0100] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0101] The collection unit can also analyze the user's purchasing history and collect new property information based on the characteristics of properties purchased in the past. For example, the collection unit analyzes the price range and location conditions of properties purchased in the past by the user, and prioritizes collecting property information with similar characteristics. The collection unit can also take into account the facilities and surrounding environment of properties purchased in the past by the user and collect property information with similar conditions. Furthermore, the collection unit can also collect property information taking into account seasonal and market trends based on the purchase dates of properties purchased in the past by the user. This makes it possible to provide optimal property information based on the user's past purchasing history.

[0102] The comparison unit can also compare the energy efficiency of properties. For example, the comparison unit can evaluate energy efficiency based on the energy consumption and insulation performance of the property. The comparison unit can also compare the renewable energy usage status of properties to identify environmentally friendly properties. Furthermore, the comparison unit can also take into account government subsidies and tax incentives related to the energy efficiency of properties. This makes it possible to select properties that take energy efficiency into consideration.

[0103] The calculation unit can also propose a long-term payment plan based on the user's predicted future income. For example, the calculation unit can predict future income increases taking into account the user's occupation and career path. The calculation unit can also predict future increases in expenses based on the user's age and family composition and adjust the payment plan accordingly. Furthermore, the calculation unit can predict future interest rate fluctuations taking into account economic conditions and market trends and reflect them in the payment plan. This allows the user to plan property purchases taking into account future income and expenses.

[0104] The management unit can also propose optimal loan terms based on the user's credit information. For example, the management unit can analyze the user's credit score and past loan history to propose optimal interest rates and repayment periods. The management unit can also propose a reasonable repayment plan taking into account the user's income and expenditures. Furthermore, the management unit can compare loan offers from multiple financial institutions based on the user's credit information and provide the optimal option. This allows the user to purchase a property with optimal loan terms based on their own credit information.

[0105] The display unit can also customize the display style of the property information according to the user's visual preferences. For example, the display unit can adjust the display style of the property information based on the user's preferred colors and fonts. The display unit can also customize the display method of the property information based on the user's preferred layout and design. Furthermore, the display unit can adjust the display order and emphasis points of the property information according to the user's visual preferences. This allows the user to view visually appealing property information that suits their preferences.

[0106] The collection unit can also estimate the user's emotions and adjust the property information collection method based on the estimated user's emotions. For example, if the user is feeling stressed, the collection unit can simplify the collection method and collect detailed information when the user is relaxed. Also, if the user is excited, the collection unit can prioritize collecting the latest property information to attract the user's interest. Furthermore, if the user is tired, the collection unit can adjust the collection method and collect property information after the user has rested. This makes it possible to provide an optimal collection method according to the user's emotions.

[0107] The comparison unit can also estimate the user's emotions and adjust the display method of the comparison results based on the estimated user's emotions. For example, the comparison unit can display detailed comparison results when the user is relaxed, and display concise comparison results when the user is in a hurry. The comparison unit can also highlight the latest property information when the user is excited. Furthermore, the comparison unit can display the comparison results using visually easy-to-understand graphs and charts when the user is tired. This makes it possible to provide an optimal display method according to the user's emotions.

[0108] The calculation unit can also estimate the user's emotions and adjust the presentation method of the calculation results based on the estimated user's emotions. For example, the calculation unit can present detailed calculation results when the user is relaxed, and present simple calculation results when the user is in a hurry. The calculation unit can also highlight the most recent calculation results when the user is excited. Furthermore, the calculation unit can present the calculation results using visually easy-to-understand graphs and charts when the user is tired. This makes it possible to provide an optimal presentation method of the calculation results according to the user's emotions.

[0109] The management unit can also estimate the user's emotions and adjust the referral fee management method based on the estimated user's emotions. For example, the management unit can provide detailed fee information when the user is relaxed, and simple fee information when the user is in a hurry. The management unit can also provide the latest fee information when the user is excited. Furthermore, the management unit can provide fee information using visually easy-to-understand graphs and charts when the user is tired. This makes it possible to provide an optimal fee management method according to the user's emotions.

[0110] The display unit can also estimate the user's emotions and prioritize the display content based on the estimated user's emotions. For example, the display unit can prioritize displaying detailed information when the user is relaxed, and prioritize displaying basic information such as price and location when the user is in a hurry. The display unit can also prioritize displaying the latest property information when the user is excited. Furthermore, the display unit can display information using visually easy-to-understand graphs and charts when the user is tired. This makes it possible to provide optimal display content according to the user's emotions.

[0111] The processing flow of the second embodiment will be briefly explained below.

[0112] Step 1: The collection unit collects property information. This property information includes the property's price, location, facilities, surrounding environment, etc. The collection unit can collect property information from real estate sites on the Internet, as well as obtain property information directly from real estate agents. It can also collect property information entered by the user, providing a form for the user to enter the conditions for the property they desire, and collect property information based on that information. Step 2: The comparison unit compares prices and advantages based on the property information collected by the collection unit. The comparison is based on criteria such as property price, location, facilities, and surrounding environment. For example, it can compare property prices to identify the cheapest property, or compare locations to identify the most convenient property. It can also compare facilities and surrounding environment to identify the best property. Step 3: The display unit displays the comparison results obtained by the comparison unit. The display is done in list format or graph format. For example, property prices can be displayed in list format to allow users to easily compare properties. The location conditions of the properties can also be displayed on a map to allow users to compare visually. Furthermore, the facilities and surrounding environment of the properties can also be displayed in graph format to allow users to intuitively compare properties. Step 4: The calculation unit calculates the purchase price and rent of the property, as well as various expenses such as brokerage fees, registration fees, and moving costs. The calculation is performed based on the information entered by the user. For example, a form is provided for entering the purchase price and rent of the property, and the total expenses are calculated based on that information. It is also possible to automatically calculate various expenses such as brokerage fees, registration fees, and moving costs. It is also possible to perform calculations to help the user select the best property within their desired budget. Step 5: The management unit manages the referral commission to be paid to the referral agent when the user purchases or rents a property. The management is carried out based on the calculation and payment management of the referral commission. For example, it provides a method for calculating the referral commission and automatically calculates the commission to be paid by the user. It can also manage the timing of referral commission payments so that the user can pay the commission at the appropriate time. Furthermore, it can manage the history of referral commissions so that the user can check the commissions they have paid in the past.

[0113] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0114] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0115] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0116] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0117] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0118] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0119] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0120] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0121] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0123] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0124] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0125] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0126] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0127] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0129] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0131] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0132] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0133] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0134] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0135] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0136] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0137] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0139] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0140] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0141] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0142] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0143] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0144] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0145] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0147] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0148] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0149] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0150] 7, a 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.

[0151] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0152] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0153] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0155] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0156] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0157] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0158] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0159] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0160] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0161] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0162] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0163] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0164] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0165] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0166] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0167] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0168] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0169] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0170] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0171] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0172] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0173] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0174] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0175] 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.

[0176] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0177] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0178] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0179] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0180] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0181] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0182] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0183] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0184] [Explanation of symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection department that collects property information; a comparison unit that compares prices or advantages based on the property information collected by the collection unit; a display unit that displays the comparison result obtained by the comparison unit; A calculation section calculates the purchase price and rental fee of the property, as well as brokerage fees, registration fees, and moving expenses. a display unit that displays the total cost calculated by the calculation unit; A management unit that manages referral fees. A system characterized by:

2. The collecting unit Collect information on the property's price, location, facilities, and surrounding environment 2. The system of claim 1.

3. The comparison unit Compare prices and advantages based on collected property information 2. The system of claim 1.

4. The calculation unit Calculate the purchase price and rent of the property, as well as brokerage fees, registration fees, and moving expenses.

2. The system of claim 1.

5. The management unit Manage the referral fees paid to referral agents when users purchase or rent properties 2. The system of claim 1.

6. The display unit View collected property information, comparison results, calculated total costs, and referral commission information 2. The system of claim 1.

7. The collecting unit Estimate user emotions and adjust the timing of property information collection based on the estimated user emotions 2. The system of claim 1.

8. The collecting unit Analyze users' past property search history and select the optimal collection method 2. The system of claim 1.

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