System

Abstract

An object of a system according to an embodiment is to reduce brokerage fees in real estate transactions and to enable individuals to easily perform property transactions.SOLUTION: A system includes a reception part, an analysis part, and a price setting part. The reception part inputs property information. The analysis unit analyzes the property information input by the reception unit and performs market analysis for finding a buyer. The price setting unit proposes price setting based on the information obtained by the analysis unit.SELECTED DRAWING: Figure 1

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 had the problem that brokerage fees for real estate transactions are high, making it difficult for individuals to easily carry out property transactions.

[0005] The system according to the embodiment aims to reduce brokerage fees in real estate transactions and enable individuals to easily trade properties. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, and a price setting unit. The reception unit inputs property information. The analysis unit analyzes the property information input by the reception unit and performs market analysis to find a buyer. The price setting unit proposes a price setting based on the information obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment reduces brokerage fees in real estate transactions, allowing individuals to easily conduct property transactions. [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 real estate trading system according to an embodiment of the present invention efficiently performs processes from inputting property information to analyzing and setting a price. This system includes a reception unit for inputting property information, an analysis unit for analyzing the property information input by the reception unit and performing market analysis to find a buyer, and a pricing unit for proposing a price based on the information obtained by the analysis unit. For example, when a seller inputs property information, a generation AI analyzes the information and initiates a process to find a suitable buyer. The generation AI also performs market analysis based on the property information and proposes an appropriate price. The generation AI also responds to inquiries from buyers and provides detailed information about the property. Furthermore, the generation AI prepares sales contracts and other necessary documents, smoothly facilitating the transaction. This system allows sellers and buyers to transact property at prices significantly lower than the market price, and brokerage fees can be kept to 1% of the property price. Furthermore, utilizing the generation AI reduces labor costs for real estate brokerage firms and enables more efficient business operations. This allows the real estate trading system to efficiently perform processes from inputting property information to analyzing and setting a price. For example, when a seller inputs property information, the generation AI analyzes the information and initiates a process to find a suitable buyer. Generator AI performs market analysis based on property information and proposes appropriate pricing. It also responds to inquiries from buyers and provides detailed property information. It also prepares sales contracts and other necessary documents, ensuring smooth transactions. This allows sellers and buyers to transact properties at prices significantly lower than the market price, and brokerage fees can be kept to 1% of the property price. Utilizing generator AI also reduces labor costs for real estate brokerage companies, enabling more efficient business operations.

[0029] A real estate trading system according to an embodiment includes a reception unit, an analysis unit, and a pricing unit. The reception unit inputs property information. The property information includes, but is not limited to, information about the address, area, price, and facilities. The reception unit provides, for example, an interface through which the seller inputs the property information. The interface is provided, for example, through a web form or a mobile application. The analysis unit uses a generation AI to analyze the property information input by the reception unit and perform a market analysis to find a buyer. The market analysis is performed, for example, based on the data, analysis method, and evaluation criteria used. For example, the analysis unit performs a market analysis based on past transaction data for the property and current market trends. The pricing unit uses the generation AI to propose a price setting based on the information obtained by the analysis unit. The price setting is performed, for example, based on the evaluation method, algorithm used, and factors considered. For example, the pricing unit sets a price taking into account the property's location and the level of facilities. This allows the real estate trading system according to an embodiment to efficiently perform processes from inputting property information to analysis and pricing. For example, when a seller enters property information, the Generator AI analyzes that information and begins the process of finding a suitable buyer. The Generator AI also performs market analysis based on the property information and proposes an appropriate price setting. The Generator AI also responds to inquiries from buyers and provides detailed information about the property. The Generator AI also prepares sales contracts and other necessary documents, ensuring the transaction proceeds smoothly. This allows sellers and buyers to transact properties at prices significantly lower than the market price, and brokerage fees can be kept to 1% of the property price. Utilizing the Generator AI also reduces labor costs for real estate brokerage companies, enabling more efficient business operations.

[0030] The real estate buying and selling system includes an inquiry response unit that responds to inquiries from buyers and provides detailed information about the property. The inquiry response unit responds to inquiries from buyers and provides detailed information about the property. Inquiry responses are performed based on, for example, response procedures, tools used, and response time. For example, the inquiry response unit responds to inquiries from buyers via phone, email, chat, etc. The inquiry response unit can also use a generation AI to automatically respond to inquiries from buyers. For example, the generation AI provides pre-defined answers to questions from buyers. The inquiry response unit also manages a database for providing detailed information about the property. For example, the database stores property photos, floor plans, facility information, etc., and provides them to buyers as needed. This allows for responding to inquiries from buyers and providing detailed information about the property. Some or all of the above-described processing in the inquiry response unit may be performed using, or without, the generation AI. For example, the inquiry response unit can input inquiries from buyers into the generation AI, which then automatically generates answers.

[0031] The real estate buying and selling system includes a contract creation unit that creates a sales contract and prepares the necessary documents. The contract creation unit creates the sales contract and prepares the necessary documents. The sales contract includes, for example, contract terms, necessary documents, and a signature method, but is not limited to these examples. The contract creation unit automatically generates the sales contract, for example, using a generation AI. The generation AI creates the sales contract based on a pre-defined template. The contract creation unit also provides tools for preparing the necessary documents. For example, the contract creation unit lists the documents required for the sales contract and provides them to the seller and buyer. The contract creation unit also supports electronic signatures for the sales contract. For example, the contract creation unit enables the seller and buyer to sign the contract online through an electronic signature platform. This allows the sales contract to be created and the necessary documents to be prepared. Some or all of the above-described processing in the contract creation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the contract creation unit can input a sales contract template into the generation AI, which can then automatically generate the contract.

[0032] The reception unit can analyze the seller's past property information input history and provide the optimal input assistance method. For example, the reception unit can automatically display input candidates based on property information previously input by the seller. The reception unit can also prioritize and suggest input methods (voice, text, etc.) that the seller has used in the past. The reception unit can also predict and suggest an input method to be used during a specific time period based on the seller's past input history. This can improve the efficiency of input work by providing the optimal input assistance method based on the seller's past input history. Some or all of the above-described processing in the reception unit can be performed using, or without, a generation AI. For example, the reception unit can input the seller's past input data into the generation AI, which can then automatically suggest the optimal input assistance method.

[0033] When entering property information, the reception unit can customize input items based on the seller's current market conditions and areas of interest. The reception unit, for example, prioritizes displaying relevant input items based on market trends that the seller is interested in. The reception unit can also analyze the seller's current market conditions and suggest optimal input items. The reception unit can also customize input items based on the seller's areas of interest to streamline input work. This allows the input work to be streamlined by customizing input items based on the seller's market conditions and areas of interest. Some or all of the above-described processing in the reception unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the seller's market condition data into the generation AI, which can then automatically suggest optimal input items.

[0034] When entering property information, the reception unit can select an input means according to the seller's input method. For example, if the seller desires voice input, the reception unit can input the property information using voice recognition technology. Furthermore, if the seller desires text input, the reception unit can also provide a text input interface. Furthermore, if the seller desires image input, the reception unit can also input the property information using image recognition technology. This allows the input work to be made more efficient by selecting the optimal input means according to the seller's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the seller's input data into a generation AI, which can then automatically select the optimal input means.

[0035] When inputting property information, the reception unit can prioritize inputting highly relevant information taking into account the seller's geographical location information. For example, the reception unit can prioritize inputting nearby property information based on the seller's current location. The reception unit can also prompt the user to input area-specific information based on the seller's geographical location information. The reception unit can also prompt the user to input related market data based on the seller's geographical location information. This can improve the efficiency of input work by prioritizing inputting highly relevant information taking into account the seller's geographical location information. Some or all of the above-described processing in the reception unit can be performed using, or without, a generation AI. For example, the reception unit can input the seller's geographical location data into the generation AI, which can then automatically prioritize inputting highly relevant information.

[0036] When entering property information, the reception unit can analyze the seller's social media activity and enter related information. For example, the reception unit automatically enters property information shared by the seller on social media. The reception unit can also analyze the seller's social media activity and enter related property information. The reception unit can also enter related property information by referring to the activity of the seller's friends on social media. In this way, by analyzing the seller's social media activity and entering related information, the entry work can be made more efficient. Some or all of the above-mentioned processing in the reception unit may be performed using, or without, a generation AI, for example. For example, the reception unit can enter the seller's social media data into the generation AI, which then automatically enters related information.

[0037] When entering property information, the reception unit can customize the input method by reflecting the seller's past feedback. The reception unit, for example, customizes the input interface based on feedback provided by the seller in the past. The reception unit can also optimize the input procedure by reflecting the seller's past feedback. The reception unit can also customize the input items based on the seller's past feedback. This allows the input work to be made more efficient by customizing the input method by reflecting the seller's past feedback. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the seller's past feedback data into the generation AI, which can then automatically customize the input method.

[0038] The analysis unit can improve the accuracy of the analysis by taking into account the interrelationships between properties when analyzing the market. The analysis unit, for example, takes into account the neighborhood relationships between properties and performs the market analysis based on the interrelationships. The analysis unit can also perform the market analysis by taking into account the interrelationships based on the past transaction history of properties. The analysis unit can also perform the market analysis by taking into account the geographical location of properties and performing the market analysis based on the interrelationships. In this way, by performing the market analysis by taking into account the interrelationships between properties, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input property interrelationship data into the generation AI, and the generation AI can automatically perform the market analysis by taking into account the interrelationships.

[0039] The analysis unit can perform market analysis taking into account attribute information of the property seller. The analysis unit performs market analysis based on attribute information such as the seller's age and occupation, for example. The analysis unit can also perform market analysis based on the seller's past transaction history. The analysis unit can also perform market analysis based on the seller's geographical location information. In this way, by performing market analysis taking into account attribute information of the property seller, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input attribute information data of the seller into the generation AI, and the generation AI can automatically perform market analysis taking into account the attribute information.

[0040] During market analysis, the analysis unit can weight the analysis based on the frequency of property sales. For example, the analysis unit may perform market analysis by prioritizing properties with a high sales frequency. The analysis unit may also perform market analysis by disregarding properties with a low sales frequency. The analysis unit can also adjust the weighting of the market analysis based on the sales frequency. This improves the accuracy of the analysis by weighting the market analysis based on the sales frequency of the property. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the analysis unit may input property sales frequency data into the generation AI, and the generation AI may automatically weight the market analysis based on the sales frequency.

[0041] The analysis unit can perform market analysis taking into account the geographic distribution of properties. For example, the analysis unit performs market analysis based on the geographic distribution of properties. The analysis unit can also perform market analysis taking into account geographical location relationships. The analysis unit can also improve the accuracy of the market analysis based on the geographic distribution. As a result, by performing market analysis taking into account the geographic distribution of properties, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the analysis unit can input geographic distribution data of properties into the generation AI, and the generation AI can automatically perform market analysis taking into account the geographic distribution.

[0042] The analysis unit can improve the accuracy of the analysis by referring to literature related to the property during market analysis. The analysis unit, for example, performs market analysis by referring to academic papers related to the property. The analysis unit can also perform market analysis by referring to news articles related to the property. The analysis unit can also perform market analysis by referring to market reports related to the property. In this way, by performing market analysis by referring to literature related to the property, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input literature data related to the property into the generation AI, and the generation AI can automatically perform market analysis by referring to the related literature.

[0043] The analysis unit can perform market analysis taking into account the market value of the property. For example, the analysis unit performs market analysis based on the market value of the property. The analysis unit can also perform market analysis by prioritizing properties with high market values. The analysis unit can also perform market analysis by disregarding properties with low market values. In this way, by performing market analysis taking into account the market value of the property, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the analysis unit can input market value data of the property into the generation AI, and the generation AI can automatically perform market analysis taking into account the market value.

[0044] The pricing unit can adjust the level of detail of the pricing based on the importance of the property when setting the price. For example, the pricing unit sets a detailed price for a property with a high importance. The pricing unit can also set a simplified price for a property with a low importance. The pricing unit can also adjust the level of detail of the pricing based on the importance of the property. This allows for appropriate pricing by adjusting the level of detail of the pricing based on the importance of the property. Some or all of the above-mentioned processing in the pricing unit may be performed using, or without, a generation AI. For example, the pricing unit can input property importance data into the generation AI, and the generation AI can automatically adjust the level of detail of the pricing.

[0045] The pricing unit can apply different pricing algorithms depending on the property category when setting a price. For example, the pricing unit can apply a residential pricing algorithm to residential properties. The pricing unit can also apply a commercial pricing algorithm to commercial properties. The pricing unit can also select the optimal pricing algorithm depending on the property category. This enables appropriate pricing by applying the optimal pricing algorithm depending on the property category. Some or all of the above-mentioned processing in the pricing unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the pricing unit can input property category data into the generation AI, and the generation AI can automatically apply the optimal pricing algorithm.

[0046] When setting a price, the pricing unit can improve the accuracy of pricing by referring to the seller's past pricing results. The pricing unit, for example, proposes an optimal price based on the seller's past pricing results. The pricing unit can also analyze the seller's past pricing history to improve the accuracy of pricing. The pricing unit can also adjust the pricing algorithm by referring to the seller's past pricing results. This improves the accuracy of pricing by referring to the seller's past pricing results. Some or all of the above-mentioned processing in the pricing unit may be performed, for example, using a generation AI or may be performed without using a generation AI. For example, the pricing unit can input the seller's past pricing data into the generation AI, which can automatically improve the accuracy of pricing.

[0047] The pricing unit can determine the priority of pricing based on the time of submission of the property when setting the price. For example, the pricing unit prioritizes pricing of properties submitted earlier. The pricing unit can also price properties submitted later later. The pricing unit can also adjust the priority of pricing based on the time of submission. This enables appropriate pricing by determining the priority of pricing based on the time of submission of the property. Some or all of the above-mentioned processing in the pricing unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the pricing unit can input property submission time data into the generation AI, and the generation AI can automatically determine the priority of pricing.

[0048] The pricing unit can adjust the order of pricing based on the relevance of properties when setting prices. For example, the pricing unit prioritizes pricing of highly relevant properties. The pricing unit can also price less relevant properties later. The pricing unit can also adjust the order of pricing based on the relevance of properties. This enables appropriate pricing by adjusting the order of pricing based on the relevance of properties. Some or all of the above-described processing in the pricing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the pricing unit can input property relevance data into the generation AI, and the generation AI can automatically adjust the order of pricing.

[0049] When setting a price, the pricing unit can adjust the use of pricing terminology depending on the seller's level of expertise. For example, if the seller has specialized knowledge, the pricing unit can set a price using detailed terminology. Alternatively, if the seller does not have specialized knowledge, the pricing unit can set a price using simple language. The pricing unit can also adjust the use of pricing terminology depending on the seller's level of expertise. This enables appropriate pricing by adjusting the use of pricing terminology depending on the seller's level of expertise. Some or all of the above-described processing in the pricing unit may be performed using, or without, a generation AI. For example, the pricing unit can input the seller's level of expertise data into the generation AI, which can then automatically adjust the use of terminology.

[0050] When responding to an inquiry, the inquiry response unit can adjust the level of detail of the response based on the importance of the property. For example, the inquiry response unit provides detailed information for properties with high importance. The inquiry response unit can also provide simplified information for properties with low importance. The inquiry response unit can also adjust the level of detail of the response based on the importance of the property. This allows for an appropriate response by adjusting the level of detail of the response based on the importance of the property. Some or all of the above-mentioned processing in the inquiry response unit may be performed using, or without, a generation AI. For example, the inquiry response unit can input property importance data into the generation AI, and the generation AI can automatically adjust the level of detail of the response.

[0051] When responding to an inquiry, the inquiry response unit can apply different response algorithms depending on the property category. For example, the inquiry response unit can apply a residential-specific response algorithm to a residential property. The inquiry response unit can also apply a commercial-specific response algorithm to a commercial property. The inquiry response unit can also select the optimal response algorithm depending on the property category. This enables an appropriate response by applying the optimal response algorithm depending on the property category. Some or all of the above-mentioned processing in the inquiry response unit can be performed using, or without, a generation AI, for example. For example, the inquiry response unit can input property category data into the generation AI, and the generation AI can automatically apply the optimal response algorithm.

[0052] When responding to an inquiry, the inquiry response unit can improve the accuracy of the response by referring to the buyer's past inquiry results. The inquiry response unit, for example, proposes an optimal response based on the buyer's past inquiry results. The inquiry response unit can also analyze the buyer's past inquiry history to improve the accuracy of the response. The inquiry response unit can also adjust the response algorithm by referring to the buyer's past inquiry results. In this way, by referring to the buyer's past inquiry results, the accuracy of the response is improved. Some or all of the above-mentioned processing in the inquiry response unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the inquiry response unit can input the buyer's past inquiry data into the generation AI, which can automatically improve the accuracy of the response.

[0053] When responding to an inquiry, the inquiry response unit can determine the priority of the response based on the submission date of the property. For example, the inquiry response unit can prioritize properties that were submitted earlier. The inquiry response unit can also postpone responding to properties that were submitted later. The inquiry response unit can also adjust the priority of the response based on the submission date. This enables appropriate responses by determining the priority of the response based on the submission date of the property. Some or all of the above-mentioned processing in the inquiry response unit may be performed using, or without, a generation AI, for example. For example, the inquiry response unit can input property submission date data into the generation AI, and the generation AI can automatically determine the priority of the response.

[0054] The inquiry response unit can adjust the order of responses based on the relevance of properties when responding to inquiries. For example, the inquiry response unit prioritizes responses to highly relevant properties. The inquiry response unit can also postpone responses to less relevant properties. The inquiry response unit can also adjust the order of responses based on the relevance of properties. This allows for appropriate responses by adjusting the order of responses based on the relevance of properties. Some or all of the above-described processing in the inquiry response unit may be performed using, or without, a generation AI, for example. For example, the inquiry response unit can input property relevance data into the generation AI, and the generation AI can automatically adjust the order of responses.

[0055] When responding to an inquiry, the inquiry response unit can adjust the use of technical terminology in the response depending on the buyer's level of expertise. For example, if the buyer has technical expertise, the inquiry response unit can respond using detailed technical terminology. Alternatively, if the buyer does not have technical expertise, the inquiry response unit can respond using simple language. The inquiry response unit can also adjust the use of technical terminology in the response depending on the buyer's level of expertise. This allows for an appropriate response by adjusting the use of technical terminology in the response depending on the buyer's level of expertise. Some or all of the above-mentioned processing in the inquiry response unit may be performed using, or without, a generation AI. For example, the inquiry response unit can input buyer's expertise level data into the generation AI, which can then automatically adjust the use of technical terminology.

[0056] The contract creation unit can adjust the level of detail of the contract based on the importance of the property when creating the contract. For example, the contract creation unit creates a detailed contract for a property of high importance. The contract creation unit can also create a simplified contract for a property of low importance. The contract creation unit can also adjust the level of detail of the contract based on the importance of the property. This makes it possible to create an appropriate contract by adjusting the level of detail of the contract based on the importance of the property. Some or all of the above-mentioned processing in the contract creation unit may be performed using, or without, a generation AI, for example. For example, the contract creation unit can input property importance data into the generation AI, and the generation AI can automatically adjust the level of detail of the contract.

[0057] When creating a contract, the contract creation unit can apply different contract creation algorithms depending on the property category. For example, the contract creation unit applies a residential contract creation algorithm to a residential property. The contract creation unit can also apply a commercial contract creation algorithm to a commercial property. The contract creation unit can also select the optimal contract creation algorithm depending on the property category. This makes it possible to create an appropriate contract by applying the optimal contract creation algorithm depending on the property category. Some or all of the above-mentioned processing in the contract creation unit may be performed using, or without, a generation AI, for example. For example, the contract creation unit can input property category data into the generation AI, and the generation AI can automatically apply the optimal contract creation algorithm.

[0058] When creating a contract, the contract creation unit can improve the accuracy of the contract creation by referring to past contract creation results between the seller and the buyer. The contract creation unit, for example, creates an optimal contract based on past contract creation results between the seller and the buyer. The contract creation unit can also analyze the seller and the buyer's past contract creation history to improve the accuracy of the contract creation. The contract creation unit can also adjust the contract creation algorithm by referring to past contract creation results between the seller and the buyer. This improves the accuracy of the contract creation by referring to past contract creation results between the seller and the buyer. Some or all of the above-mentioned processing in the contract creation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the contract creation unit can input past contract creation data between the seller and the buyer into the generation AI, which can automatically improve the accuracy of the contract creation.

[0059] When creating a contract, the contract creation unit can determine the priority of the contract based on the submission date of the property. For example, the contract creation unit can create a contract by giving priority to a property that is submitted earlier. The contract creation unit can also create a contract by putting off a property that is submitted later. The contract creation unit can also adjust the priority of the contract based on the submission date. This enables appropriate contract creation by determining the priority of the contract based on the submission date of the property. Some or all of the above-mentioned processing in the contract creation unit may be performed using, or without, a generation AI, for example. For example, the contract creation unit can input data on the submission date of the property into the generation AI, and the generation AI can automatically determine the priority of the contract.

[0060] The contract creation unit can adjust the order of contracts based on the relevance of properties when creating contracts. For example, the contract creation unit creates contracts by prioritizing highly relevant properties. The contract creation unit can also create contracts by putting less relevant properties on hold. The contract creation unit can also adjust the order of contracts based on the relevance of properties. This makes it possible to create appropriate contracts by adjusting the order of contracts based on the relevance of properties. Some or all of the above-mentioned processing in the contract creation unit may be performed using, or without, a generation AI, for example. For example, the contract creation unit can input property relevance data into the generation AI, and the generation AI can automatically adjust the order of contracts.

[0061] When creating a contract, the contract creation unit can adjust the use of technical terminology in the contract depending on the knowledge levels of the seller and buyer. For example, if the seller and buyer have specialized knowledge, the contract creation unit can create the contract using detailed technical terminology. Alternatively, if the seller and buyer do not have specialized knowledge, the contract creation unit can create the contract using simple language. The contract creation unit can also adjust the use of technical terminology in the contract depending on the expertise levels of the seller and buyer. This allows for the creation of appropriate contracts by adjusting the use of technical terminology in the contract depending on the expertise levels of the seller and buyer. Some or all of the above-described processing in the contract creation unit may be performed using, or without, a generation AI. For example, the contract creation unit can input expertise level data of the seller and buyer into the generation AI, which can then automatically adjust the use of technical terminology.

[0062] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0063] The reception unit can analyze the seller's past property information input history and provide the optimal input assistance method. For example, input candidates can be automatically displayed based on the property information previously entered by the seller. The reception unit can also prioritize and suggest input methods (voice, text, etc.) that the seller has used in the past. Furthermore, the reception unit can predict and suggest the input method to be used during a specific time period based on the seller's past input history. This can improve the efficiency of input work by providing the optimal input assistance method based on the seller's past input history. Some or all of the above-described processing in the reception unit can be performed using, or without, a generation AI. For example, the reception unit can input the seller's past input data into the generation AI, which can then automatically suggest the optimal input assistance method.

[0064] The analysis unit can improve the accuracy of the market analysis by taking into account the interrelationships between properties. For example, the analysis unit can consider the neighborhood relationships between properties and perform the market analysis based on the interrelationships. The analysis unit can also perform the market analysis by taking into account the interrelationships based on the past transaction history of properties. Furthermore, the analysis unit can also perform the market analysis by taking into account the geographical location of properties and performing the market analysis based on the interrelationships. In this way, the accuracy of the analysis is improved by performing the market analysis by taking into account the interrelationships between properties. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input property interrelationship data into the generation AI, and the generation AI can automatically perform the market analysis by taking into account the interrelationships.

[0065] When setting a price, the pricing unit can apply different pricing algorithms depending on the property category. For example, a residential pricing algorithm can be applied to a residential property. The pricing unit can also apply a commercial pricing algorithm to a commercial property. Furthermore, the pricing unit can select the optimal pricing algorithm depending on the property category. This enables appropriate pricing by applying the optimal pricing algorithm depending on the property category. Some or all of the above-mentioned processing in the pricing unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the pricing unit can input property category data into the generation AI, which can then automatically apply the optimal pricing algorithm.

[0066] When responding to an inquiry, the inquiry response unit can adjust the level of detail of the response based on the importance of the property. For example, detailed information is provided for properties with high importance. The inquiry response unit can also provide simplified information for properties with low importance. Furthermore, the inquiry response unit can adjust the level of detail of the response based on the importance of the property. This allows for an appropriate response by adjusting the level of detail of the response based on the importance of the property. Some or all of the above-mentioned processing in the inquiry response unit may be performed using, or without, a generation AI. For example, the inquiry response unit can input property importance data into the generation AI, and the generation AI can automatically adjust the level of detail of the response.

[0067] When creating a contract, the contract creation unit can apply different contract creation algorithms depending on the property category. For example, a residential contract creation algorithm can be applied to a residential property. The contract creation unit can also apply a commercial contract creation algorithm to a commercial property. Furthermore, the contract creation unit can select the optimal contract creation algorithm depending on the property category. This makes it possible to create an appropriate contract by applying the optimal contract creation algorithm depending on the property category. Some or all of the above-mentioned processing in the contract creation unit may be performed using, or without, a generation AI, for example. For example, the contract creation unit can input property category data into the generation AI, and the generation AI can automatically apply the optimal contract creation algorithm.

[0068] The processing flow of the first embodiment will be briefly explained below.

[0069] Step 1: The reception unit inputs property information. The property information includes, for example, the address, area, price, and facility information. The reception unit provides an interface for the seller to input the property information, and the interface is provided via a web form or a mobile application. Step 2: The analysis unit uses the generation AI to analyze the property information entered by the reception unit and conducts a market analysis to find a buyer. The market analysis is based on the property's past transaction data and current market trends. Step 3: The pricing unit uses the generation AI to propose a price based on the information obtained by the analysis unit, taking into account the property's location and the level of amenities.

[0070] (Example 2) A real estate trading system according to an embodiment of the present invention efficiently performs processes from inputting property information to analyzing and setting a price. This system includes a reception unit for inputting property information, an analysis unit for analyzing the property information input by the reception unit and performing market analysis to find a buyer, and a pricing unit for proposing a price based on the information obtained by the analysis unit. For example, when a seller inputs property information, a generation AI analyzes the information and initiates a process to find a suitable buyer. The generation AI also performs market analysis based on the property information and proposes an appropriate price. The generation AI also responds to inquiries from buyers and provides detailed information about the property. Furthermore, the generation AI prepares sales contracts and other necessary documents, smoothly facilitating the transaction. This system allows sellers and buyers to transact property at prices significantly lower than the market price, and brokerage fees can be kept to 1% of the property price. Furthermore, utilizing the generation AI reduces labor costs for real estate brokerage firms and enables more efficient business operations. This allows the real estate trading system to efficiently perform processes from inputting property information to analyzing and setting a price. For example, when a seller inputs property information, the generation AI analyzes the information and initiates a process to find a suitable buyer. Generator AI performs market analysis based on property information and proposes appropriate pricing. It also responds to inquiries from buyers and provides detailed property information. It also prepares sales contracts and other necessary documents, ensuring smooth transactions. This allows sellers and buyers to transact properties at prices significantly lower than the market price, and brokerage fees can be kept to 1% of the property price. Utilizing generator AI also reduces labor costs for real estate brokerage companies, enabling more efficient business operations.

[0071] A real estate trading system according to an embodiment includes a reception unit, an analysis unit, and a pricing unit. The reception unit inputs property information. The property information includes, but is not limited to, information about the address, area, price, and facilities. The reception unit provides, for example, an interface through which the seller inputs the property information. The interface is provided, for example, through a web form or a mobile application. The analysis unit uses a generation AI to analyze the property information input by the reception unit and perform a market analysis to find a buyer. The market analysis is performed, for example, based on the data, analysis method, and evaluation criteria used. For example, the analysis unit performs a market analysis based on past transaction data for the property and current market trends. The pricing unit uses the generation AI to propose a price setting based on the information obtained by the analysis unit. The price setting is performed, for example, based on the evaluation method, algorithm used, and factors considered. For example, the pricing unit sets a price taking into account the property's location and the level of facilities. This allows the real estate trading system according to an embodiment to efficiently perform processes from inputting property information to analysis and pricing. For example, when a seller enters property information, the Generator AI analyzes that information and begins the process of finding a suitable buyer. The Generator AI also performs market analysis based on the property information and proposes an appropriate price setting. The Generator AI also responds to inquiries from buyers and provides detailed information about the property. The Generator AI also prepares sales contracts and other necessary documents, ensuring the transaction proceeds smoothly. This allows sellers and buyers to transact properties at prices significantly lower than the market price, and brokerage fees can be kept to 1% of the property price. Utilizing the Generator AI also reduces labor costs for real estate brokerage companies, enabling more efficient business operations.

[0072] The real estate buying and selling system includes an inquiry response unit that responds to inquiries from buyers and provides detailed information about the property. The inquiry response unit responds to inquiries from buyers and provides detailed information about the property. Inquiry responses are performed based on, for example, response procedures, tools used, and response time. For example, the inquiry response unit responds to inquiries from buyers via phone, email, chat, etc. The inquiry response unit can also use a generation AI to automatically respond to inquiries from buyers. For example, the generation AI provides pre-defined answers to questions from buyers. The inquiry response unit also manages a database for providing detailed information about the property. For example, the database stores property photos, floor plans, facility information, etc., and provides them to buyers as needed. This allows for responding to inquiries from buyers and providing detailed information about the property. Some or all of the above-described processing in the inquiry response unit may be performed using, or without, the generation AI. For example, the inquiry response unit can input inquiries from buyers into the generation AI, which then automatically generates answers.

[0073] The real estate buying and selling system includes a contract creation unit that creates a sales contract and prepares the necessary documents. The contract creation unit creates the sales contract and prepares the necessary documents. The sales contract includes, for example, contract terms, necessary documents, and a signature method, but is not limited to these examples. The contract creation unit automatically generates the sales contract, for example, using a generation AI. The generation AI creates the sales contract based on a pre-defined template. The contract creation unit also provides tools for preparing the necessary documents. For example, the contract creation unit lists the documents required for the sales contract and provides them to the seller and buyer. The contract creation unit also supports electronic signatures for the sales contract. For example, the contract creation unit enables the seller and buyer to sign the contract online through an electronic signature platform. This allows the sales contract to be created and the necessary documents to be prepared. Some or all of the above-described processing in the contract creation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the contract creation unit can input a sales contract template into the generation AI, which can then automatically generate the contract.

[0074] The reception unit can estimate the seller's emotions and adjust the property information input method based on the estimated emotions. For example, if the seller is nervous, the reception unit can provide a simple and intuitive interface to minimize input steps. Furthermore, if the seller is relaxed, the reception unit can provide detailed input options and suggest customizable input methods. Furthermore, if the seller is in a hurry, the reception unit can prioritize voice input to enable quick input of property information. This can improve the efficiency of input work by adjusting the property information input method according to the seller's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation 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 reception unit can be performed using, for example, the generation AI. For example, the reception unit can input the seller's facial expression data into the generation AI, which can then automatically estimate the emotion and adjust the input method.

[0075] The reception unit can analyze the seller's past property information input history and provide the optimal input assistance method. For example, the reception unit can automatically display input candidates based on property information previously input by the seller. The reception unit can also prioritize and suggest input methods (voice, text, etc.) that the seller has used in the past. The reception unit can also predict and suggest an input method to be used during a specific time period based on the seller's past input history. This can improve the efficiency of input work by providing the optimal input assistance method based on the seller's past input history. Some or all of the above-described processing in the reception unit can be performed using, or without, a generation AI. For example, the reception unit can input the seller's past input data into the generation AI, which can then automatically suggest the optimal input assistance method.

[0076] When entering property information, the reception unit can customize input items based on the seller's current market conditions and areas of interest. The reception unit, for example, prioritizes displaying relevant input items based on market trends that the seller is interested in. The reception unit can also analyze the seller's current market conditions and suggest optimal input items. The reception unit can also customize input items based on the seller's areas of interest to streamline input work. This allows the input work to be streamlined by customizing input items based on the seller's market conditions and areas of interest. Some or all of the above-described processing in the reception unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the seller's market condition data into the generation AI, which can then automatically suggest optimal input items.

[0077] When entering property information, the reception unit can select an input means according to the seller's input method. For example, if the seller desires voice input, the reception unit can input the property information using voice recognition technology. Furthermore, if the seller desires text input, the reception unit can also provide a text input interface. Furthermore, if the seller desires image input, the reception unit can also input the property information using image recognition technology. This allows the input work to be made more efficient by selecting the optimal input means according to the seller's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the seller's input data into a generation AI, which can then automatically select the optimal input means.

[0078] The reception unit can estimate the seller's emotions and determine the priority of the property information to be entered based on the estimated emotions. For example, if the seller is nervous, the reception unit can prompt the seller to enter important information first. Furthermore, if the seller is relaxed, the reception unit can prompt the seller to enter detailed information first. Furthermore, if the seller is in a hurry, the reception unit can prompt the seller to enter the minimum amount of information first. This allows the priority of property information to be determined according to the seller's emotions, thereby allowing important information to be entered first. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the reception unit can input the seller's facial expression data into the generation AI, which can then automatically estimate the seller's emotions and determine the priority of the property information to be entered.

[0079] When inputting property information, the reception unit can prioritize inputting highly relevant information taking into account the seller's geographical location information. For example, the reception unit can prioritize inputting nearby property information based on the seller's current location. The reception unit can also prompt the user to input area-specific information based on the seller's geographical location information. The reception unit can also prompt the user to input related market data based on the seller's geographical location information. This can improve the efficiency of input work by prioritizing inputting highly relevant information taking into account the seller's geographical location information. Some or all of the above-described processing in the reception unit can be performed using, or without, a generation AI. For example, the reception unit can input the seller's geographical location data into the generation AI, which can then automatically prioritize inputting highly relevant information.

[0080] When entering property information, the reception unit can analyze the seller's social media activity and enter related information. For example, the reception unit automatically enters property information shared by the seller on social media. The reception unit can also analyze the seller's social media activity and enter related property information. The reception unit can also enter related property information by referring to the activity of the seller's friends on social media. In this way, by analyzing the seller's social media activity and entering related information, the entry work can be made more efficient. Some or all of the above-mentioned processing in the reception unit may be performed using, or without, a generation AI, for example. For example, the reception unit can enter the seller's social media data into the generation AI, which then automatically enters related information.

[0081] When entering property information, the reception unit can customize the input method by reflecting the seller's past feedback. The reception unit, for example, customizes the input interface based on feedback provided by the seller in the past. The reception unit can also optimize the input procedure by reflecting the seller's past feedback. The reception unit can also customize the input items based on the seller's past feedback. This allows the input work to be made more efficient by customizing the input method by reflecting the seller's past feedback. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the seller's past feedback data into the generation AI, which can then automatically customize the input method.

[0082] The analysis unit can estimate the seller's emotions and adjust the market analysis criteria based on the estimated seller's emotions. For example, if the seller is nervous, the analysis unit can provide simple and intuitive market analysis results. Furthermore, if the seller is relaxed, the analysis unit can provide detailed market analysis results. Furthermore, if the seller is in a hurry, the analysis unit can provide market analysis results that focus on the key points. By adjusting the market analysis criteria according to the seller's emotions, appropriate market analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the seller's facial expression data into the generation AI, which can then automatically estimate the emotions and adjust the market analysis criteria.

[0083] The analysis unit can improve the accuracy of the analysis by taking into account the interrelationships between properties when analyzing the market. The analysis unit, for example, takes into account the neighborhood relationships between properties and performs the market analysis based on the interrelationships. The analysis unit can also perform the market analysis by taking into account the interrelationships based on the past transaction history of properties. The analysis unit can also perform the market analysis by taking into account the geographical location of properties and performing the market analysis based on the interrelationships. In this way, by performing the market analysis by taking into account the interrelationships between properties, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input property interrelationship data into the generation AI, and the generation AI can automatically perform the market analysis by taking into account the interrelationships.

[0084] The analysis unit can perform market analysis taking into account attribute information of the property seller. The analysis unit performs market analysis based on attribute information such as the seller's age and occupation, for example. The analysis unit can also perform market analysis based on the seller's past transaction history. The analysis unit can also perform market analysis based on the seller's geographical location information. In this way, by performing market analysis taking into account attribute information of the property seller, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input attribute information data of the seller into the generation AI, and the generation AI can automatically perform market analysis taking into account the attribute information.

[0085] During market analysis, the analysis unit can weight the analysis based on the frequency of property sales. For example, the analysis unit may perform market analysis by prioritizing properties with a high sales frequency. The analysis unit may also perform market analysis by disregarding properties with a low sales frequency. The analysis unit can also adjust the weighting of the market analysis based on the sales frequency. This improves the accuracy of the analysis by weighting the market analysis based on the sales frequency of the property. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the analysis unit may input property sales frequency data into the generation AI, and the generation AI may automatically weight the market analysis based on the sales frequency.

[0086] The analysis unit can estimate the seller's emotions and adjust the order in which market analysis results are displayed based on the estimated emotions. For example, if the seller is nervous, the analysis unit can prioritize displaying important information. Furthermore, if the seller is relaxed, the analysis unit can prioritize displaying detailed information. Furthermore, if the seller is in a hurry, the analysis unit can prioritize displaying information that covers the main points. Thus, by adjusting the order in which market analysis results are displayed according to the seller's emotions, important information can be prioritized. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation 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 analysis unit can be performed using, for example, the generation AI, or without the generation AI. For example, the analysis unit can input facial expression data of the seller into the generation AI, which can then automatically estimate the emotions and adjust the order in which market analysis results are displayed.

[0087] The analysis unit can perform market analysis taking into account the geographic distribution of properties. For example, the analysis unit performs market analysis based on the geographic distribution of properties. The analysis unit can also perform market analysis taking into account geographical location relationships. The analysis unit can also improve the accuracy of the market analysis based on the geographic distribution. As a result, by performing market analysis taking into account the geographic distribution of properties, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the analysis unit can input geographic distribution data of properties into the generation AI, and the generation AI can automatically perform market analysis taking into account the geographic distribution.

[0088] The analysis unit can improve the accuracy of the analysis by referring to literature related to the property during market analysis. The analysis unit, for example, performs market analysis by referring to academic papers related to the property. The analysis unit can also perform market analysis by referring to news articles related to the property. The analysis unit can also perform market analysis by referring to market reports related to the property. In this way, by performing market analysis by referring to literature related to the property, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input literature data related to the property into the generation AI, and the generation AI can automatically perform market analysis by referring to the related literature.

[0089] The analysis unit can perform market analysis taking into account the market value of the property. For example, the analysis unit performs market analysis based on the market value of the property. The analysis unit can also perform market analysis by prioritizing properties with high market values. The analysis unit can also perform market analysis by disregarding properties with low market values. In this way, by performing market analysis taking into account the market value of the property, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the analysis unit can input market value data of the property into the generation AI, and the generation AI can automatically perform market analysis taking into account the market value.

[0090] The pricing unit can estimate the seller's emotions and adjust the pricing method based on the estimated emotions. For example, if the seller is nervous, the pricing unit can provide a simple and intuitive pricing method. If the seller is relaxed, the pricing unit can also provide detailed pricing options. If the seller is in a hurry, the pricing unit can also provide a quick pricing method. This enables appropriate pricing by adjusting the pricing method according to the seller's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the pricing unit can be performed using, for example, the generation AI, or can be performed without the generation AI. For example, the pricing unit can input facial expression data of the seller into the generation AI, which can automatically estimate the emotions and adjust the pricing method.

[0091] The pricing unit can adjust the level of detail of the pricing based on the importance of the property when setting the price. For example, the pricing unit sets a detailed price for a property with a high importance. The pricing unit can also set a simplified price for a property with a low importance. The pricing unit can also adjust the level of detail of the pricing based on the importance of the property. This allows for appropriate pricing by adjusting the level of detail of the pricing based on the importance of the property. Some or all of the above-mentioned processing in the pricing unit may be performed using, or without, a generation AI. For example, the pricing unit can input property importance data into the generation AI, and the generation AI can automatically adjust the level of detail of the pricing.

[0092] The pricing unit can apply different pricing algorithms depending on the property category when setting a price. For example, the pricing unit can apply a residential pricing algorithm to residential properties. The pricing unit can also apply a commercial pricing algorithm to commercial properties. The pricing unit can also select the optimal pricing algorithm depending on the property category. This enables appropriate pricing by applying the optimal pricing algorithm depending on the property category. Some or all of the above-mentioned processing in the pricing unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the pricing unit can input property category data into the generation AI, and the generation AI can automatically apply the optimal pricing algorithm.

[0093] When setting a price, the pricing unit can improve the accuracy of pricing by referring to the seller's past pricing results. The pricing unit, for example, proposes an optimal price based on the seller's past pricing results. The pricing unit can also analyze the seller's past pricing history to improve the accuracy of pricing. The pricing unit can also adjust the pricing algorithm by referring to the seller's past pricing results. This improves the accuracy of pricing by referring to the seller's past pricing results. Some or all of the above-mentioned processing in the pricing unit may be performed, for example, using a generation AI or may be performed without using a generation AI. For example, the pricing unit can input the seller's past pricing data into the generation AI, which can automatically improve the accuracy of pricing.

[0094] The pricing unit can estimate the seller's emotions and adjust the length of the pricing based on the estimated emotions. For example, if the seller is nervous, the pricing unit can set the price quickly. Also, if the seller is relaxed, the pricing unit can take time to set a detailed price. Also, if the seller is in a hurry, the pricing unit can set the price quickly. This allows for appropriate pricing by adjusting the length of the pricing according to the seller's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the pricing unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the pricing unit can input facial expression data of the seller into the generation AI, which can automatically estimate the emotion and adjust the length of the pricing.

[0095] The pricing unit can determine the priority of pricing based on the time of submission of the property when setting the price. For example, the pricing unit prioritizes pricing of properties submitted earlier. The pricing unit can also price properties submitted later later. The pricing unit can also adjust the priority of pricing based on the time of submission. This enables appropriate pricing by determining the priority of pricing based on the time of submission of the property. Some or all of the above-mentioned processing in the pricing unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the pricing unit can input property submission time data into the generation AI, and the generation AI can automatically determine the priority of pricing.

[0096] The pricing unit can adjust the order of pricing based on the relevance of properties when setting prices. For example, the pricing unit prioritizes pricing of highly relevant properties. The pricing unit can also price less relevant properties later. The pricing unit can also adjust the order of pricing based on the relevance of properties. This enables appropriate pricing by adjusting the order of pricing based on the relevance of properties. Some or all of the above-described processing in the pricing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the pricing unit can input property relevance data into the generation AI, and the generation AI can automatically adjust the order of pricing.

[0097] When setting a price, the pricing unit can adjust the use of pricing terminology depending on the seller's level of expertise. For example, if the seller has specialized knowledge, the pricing unit can set a price using detailed terminology. Alternatively, if the seller does not have specialized knowledge, the pricing unit can set a price using simple language. The pricing unit can also adjust the use of pricing terminology depending on the seller's level of expertise. This enables appropriate pricing by adjusting the use of pricing terminology depending on the seller's level of expertise. Some or all of the above-described processing in the pricing unit may be performed using, or without, a generation AI. For example, the pricing unit can input the seller's level of expertise data into the generation AI, which can then automatically adjust the use of terminology.

[0098] The inquiry handling unit can estimate the buyer's emotions and adjust the inquiry handling method based on the estimated buyer's emotions. For example, if the buyer is nervous, the inquiry handling unit can respond in a calm tone. If the buyer is relaxed, the inquiry handling unit can also respond in a friendly tone. If the buyer is in a hurry, the inquiry handling unit can also respond quickly. This allows for an appropriate response by adjusting the inquiry handling method according to the buyer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the inquiry handling unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the inquiry handling unit can input the buyer's facial expression data into the generation AI, which can automatically estimate the buyer's emotions and adjust the inquiry handling method.

[0099] When responding to an inquiry, the inquiry response unit can adjust the level of detail of the response based on the importance of the property. For example, the inquiry response unit provides detailed information for properties with high importance. The inquiry response unit can also provide simplified information for properties with low importance. The inquiry response unit can also adjust the level of detail of the response based on the importance of the property. This allows for an appropriate response by adjusting the level of detail of the response based on the importance of the property. Some or all of the above-mentioned processing in the inquiry response unit may be performed using, or without, a generation AI. For example, the inquiry response unit can input property importance data into the generation AI, and the generation AI can automatically adjust the level of detail of the response.

[0100] When responding to an inquiry, the inquiry response unit can apply different response algorithms depending on the property category. For example, the inquiry response unit can apply a residential-specific response algorithm to a residential property. The inquiry response unit can also apply a commercial-specific response algorithm to a commercial property. The inquiry response unit can also select the optimal response algorithm depending on the property category. This enables an appropriate response by applying the optimal response algorithm depending on the property category. Some or all of the above-mentioned processing in the inquiry response unit can be performed using, or without, a generation AI, for example. For example, the inquiry response unit can input property category data into the generation AI, and the generation AI can automatically apply the optimal response algorithm.

[0101] When responding to an inquiry, the inquiry response unit can improve the accuracy of the response by referring to the buyer's past inquiry results. The inquiry response unit, for example, proposes an optimal response based on the buyer's past inquiry results. The inquiry response unit can also analyze the buyer's past inquiry history to improve the accuracy of the response. The inquiry response unit can also adjust the response algorithm by referring to the buyer's past inquiry results. In this way, by referring to the buyer's past inquiry results, the accuracy of the response is improved. Some or all of the above-mentioned processing in the inquiry response unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the inquiry response unit can input the buyer's past inquiry data into the generation AI, which can automatically improve the accuracy of the response.

[0102] The inquiry handling unit can estimate the buyer's emotions and adjust the length of the inquiry response based on the estimated buyer's emotions. For example, if the buyer is nervous, the inquiry handling unit can respond quickly. If the buyer is relaxed, the inquiry handling unit can take time to provide a detailed response. If the buyer is in a hurry, the inquiry handling unit can respond quickly. This allows for an appropriate response by adjusting the length of the inquiry response according to the buyer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the inquiry handling unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the inquiry handling unit can input the buyer's facial expression data into the generation AI, which can automatically estimate the buyer's emotions and adjust the length of the inquiry response.

[0103] When responding to an inquiry, the inquiry response unit can determine the priority of the response based on the submission date of the property. For example, the inquiry response unit can prioritize properties that were submitted earlier. The inquiry response unit can also postpone responding to properties that were submitted later. The inquiry response unit can also adjust the priority of the response based on the submission date. This enables appropriate responses by determining the priority of the response based on the submission date of the property. Some or all of the above-mentioned processing in the inquiry response unit may be performed using, or without, a generation AI, for example. For example, the inquiry response unit can input property submission date data into the generation AI, and the generation AI can automatically determine the priority of the response.

[0104] The inquiry response unit can adjust the order of responses based on the relevance of properties when responding to inquiries. For example, the inquiry response unit prioritizes responses to highly relevant properties. The inquiry response unit can also postpone responses to less relevant properties. The inquiry response unit can also adjust the order of responses based on the relevance of properties. This allows for appropriate responses by adjusting the order of responses based on the relevance of properties. Some or all of the above-described processing in the inquiry response unit may be performed using, or without, a generation AI, for example. For example, the inquiry response unit can input property relevance data into the generation AI, and the generation AI can automatically adjust the order of responses.

[0105] When responding to an inquiry, the inquiry response unit can adjust the use of technical terminology in the response depending on the buyer's level of expertise. For example, if the buyer has technical expertise, the inquiry response unit can respond using detailed technical terminology. Alternatively, if the buyer does not have technical expertise, the inquiry response unit can respond using simple language. The inquiry response unit can also adjust the use of technical terminology in the response depending on the buyer's level of expertise. This allows for an appropriate response by adjusting the use of technical terminology in the response depending on the buyer's level of expertise. Some or all of the above-mentioned processing in the inquiry response unit may be performed using, or without, a generation AI. For example, the inquiry response unit can input buyer's expertise level data into the generation AI, which can then automatically adjust the use of technical terminology.

[0106] The contract creation unit can estimate the emotions of the seller and buyer and adjust the contract creation method based on the estimated emotions. For example, if the seller and buyer are nervous, the contract creation unit can create a simple and intuitive contract. The contract creation unit can also create a detailed contract if the seller and buyer are relaxed. The contract creation unit can also quickly create a contract if the seller and buyer are in a hurry. This enables appropriate contract creation by adjusting the contract creation method according to the emotions of the seller and buyer. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the contract creation unit may be performed using, for example, the generative AI, or may be performed without using the generative AI. For example, the contract creation unit can input facial expression data of the seller and buyer into the generative AI, which can automatically estimate their emotions and adjust the contract creation method.

[0107] The contract creation unit can adjust the level of detail of the contract based on the importance of the property when creating the contract. For example, the contract creation unit creates a detailed contract for a property of high importance. The contract creation unit can also create a simplified contract for a property of low importance. The contract creation unit can also adjust the level of detail of the contract based on the importance of the property. This makes it possible to create an appropriate contract by adjusting the level of detail of the contract based on the importance of the property. Some or all of the above-mentioned processing in the contract creation unit may be performed using, or without, a generation AI, for example. For example, the contract creation unit can input property importance data into the generation AI, and the generation AI can automatically adjust the level of detail of the contract.

[0108] When creating a contract, the contract creation unit can apply different contract creation algorithms depending on the property category. For example, the contract creation unit applies a residential contract creation algorithm to a residential property. The contract creation unit can also apply a commercial contract creation algorithm to a commercial property. The contract creation unit can also select the optimal contract creation algorithm depending on the property category. This makes it possible to create an appropriate contract by applying the optimal contract creation algorithm depending on the property category. Some or all of the above-mentioned processing in the contract creation unit may be performed using, or without, a generation AI, for example. For example, the contract creation unit can input property category data into the generation AI, and the generation AI can automatically apply the optimal contract creation algorithm.

[0109] When creating a contract, the contract creation unit can improve the accuracy of the contract creation by referring to past contract creation results between the seller and the buyer. The contract creation unit, for example, creates an optimal contract based on past contract creation results between the seller and the buyer. The contract creation unit can also analyze the seller and the buyer's past contract creation history to improve the accuracy of the contract creation. The contract creation unit can also adjust the contract creation algorithm by referring to past contract creation results between the seller and the buyer. This improves the accuracy of the contract creation by referring to past contract creation results between the seller and the buyer. Some or all of the above-mentioned processing in the contract creation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the contract creation unit can input past contract creation data between the seller and the buyer into the generation AI, which can automatically improve the accuracy of the contract creation.

[0110] The contract creation unit can estimate the emotions of the seller and buyer and adjust the length of the contract based on the estimated emotions. For example, if the seller and buyer are nervous, the contract creation unit can create a short, concise contract. The contract creation unit can also create a detailed contract if the seller and buyer are relaxed. The contract creation unit can also quickly create a contract if the seller and buyer are in a hurry. This allows for appropriate contract creation by adjusting the length of the contract according to the emotions of the seller and buyer. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the contract creation unit can be performed using, for example, the generation AI, or without the generation AI. For example, the contract creation unit can input facial expression data of the seller and buyer into the generation AI, which can automatically estimate their emotions and adjust the length of the contract.

[0111] When creating a contract, the contract creation unit can determine the priority of the contract based on the submission date of the property. For example, the contract creation unit can create a contract by giving priority to a property that is submitted earlier. The contract creation unit can also create a contract by putting off a property that is submitted later. The contract creation unit can also adjust the priority of the contract based on the submission date. This enables appropriate contract creation by determining the priority of the contract based on the submission date of the property. Some or all of the above-mentioned processing in the contract creation unit may be performed using, or without, a generation AI, for example. For example, the contract creation unit can input data on the submission date of the property into the generation AI, and the generation AI can automatically determine the priority of the contract.

[0112] The contract creation unit can adjust the order of contracts based on the relevance of properties when creating contracts. For example, the contract creation unit creates contracts by prioritizing highly relevant properties. The contract creation unit can also create contracts by putting less relevant properties on hold. The contract creation unit can also adjust the order of contracts based on the relevance of properties. This makes it possible to create appropriate contracts by adjusting the order of contracts based on the relevance of properties. Some or all of the above-mentioned processing in the contract creation unit may be performed using, or without, a generation AI, for example. For example, the contract creation unit can input property relevance data into the generation AI, and the generation AI can automatically adjust the order of contracts.

[0113] When creating a contract, the contract creation unit can adjust the use of technical terminology in the contract depending on the knowledge levels of the seller and buyer. For example, if the seller and buyer have specialized knowledge, the contract creation unit can create the contract using detailed technical terminology. Alternatively, if the seller and buyer do not have specialized knowledge, the contract creation unit can create the contract using simple language. The contract creation unit can also adjust the use of technical terminology in the contract depending on the expertise levels of the seller and buyer. This allows for the creation of appropriate contracts by adjusting the use of technical terminology in the contract depending on the expertise levels of the seller and buyer. Some or all of the above-described processing in the contract creation unit may be performed using, or without, a generation AI. For example, the contract creation unit can input expertise level data of the seller and buyer into the generation AI, which can then automatically adjust the use of technical terminology. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, analysis unit, pricing unit, inquiry response unit, and contract creation unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and receives input of property information. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the property information and performs market analysis. The price setting unit is realized by the specific processing unit 290 of the data processing device 12 and proposes a price setting based on the analysis results. The inquiry response unit is realized by the control unit 46A of the smart device 14 and responds to inquiries from buyers and provides detailed information about the property. The contract creation unit is realized by the specific processing unit 290 of the data processing device 12 and creates a sales contract and prepares necessary documents. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, analysis unit, pricing unit, inquiry response unit, and contract creation 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 reception unit is realized by the control unit 46A of the smart glasses 214 and accepts input of property information. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the property information and performs market analysis. The price setting unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes pricing based on the analysis results. The inquiry response unit is realized, for example, by the control unit 46A of the smart glasses 214 and responds to inquiries from buyers and provides detailed property information. The contract creation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and creates a sales contract and prepares necessary documents. === Hard Collateral 1-3 === Each of the multiple elements, including the reception unit, analysis unit, pricing unit, inquiry response unit, and contract creation 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 reception unit is realized by the control unit 46A of the headset terminal 314 and accepts input of property information. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the property information and performs market analysis. The price setting unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes pricing based on the analysis results. The inquiry response unit is realized, for example, by the control unit 46A of the headset terminal 314 and responds to inquiries from buyers and provides detailed information about the property. The contract creation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and creates a sales contract and prepares the necessary documents. === Hard Collateral 1-4 === Each of the multiple elements, including the reception unit, analysis unit, pricing unit, inquiry response unit, and contract creation unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and receives input of property information. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the property information and performs market analysis. The price setting unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes a price setting based on the analysis results. The inquiry response unit is realized, for example, by the control unit 46A of the robot 414 and responds to inquiries from buyers and provides detailed information about the property. The contract creation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and creates a sales contract and prepares the necessary documents.

[0114] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0115] The reception unit can analyze the seller's past property information input history and provide the optimal input assistance method. For example, input candidates can be automatically displayed based on the property information previously entered by the seller. The reception unit can also prioritize and suggest input methods (voice, text, etc.) that the seller has used in the past. Furthermore, the reception unit can predict and suggest the input method to be used during a specific time period based on the seller's past input history. This can improve the efficiency of input work by providing the optimal input assistance method based on the seller's past input history. Some or all of the above-described processing in the reception unit can be performed using, or without, a generation AI. For example, the reception unit can input the seller's past input data into the generation AI, which can then automatically suggest the optimal input assistance method.

[0116] The analysis unit can improve the accuracy of the market analysis by taking into account the interrelationships between properties. For example, the analysis unit can consider the neighborhood relationships between properties and perform the market analysis based on the interrelationships. The analysis unit can also perform the market analysis by taking into account the interrelationships based on the past transaction history of properties. Furthermore, the analysis unit can also perform the market analysis by taking into account the geographical location of properties and performing the market analysis based on the interrelationships. In this way, the accuracy of the analysis is improved by performing the market analysis by taking into account the interrelationships between properties. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input property interrelationship data into the generation AI, and the generation AI can automatically perform the market analysis by taking into account the interrelationships.

[0117] When setting a price, the pricing unit can apply different pricing algorithms depending on the property category. For example, a residential pricing algorithm can be applied to a residential property. The pricing unit can also apply a commercial pricing algorithm to a commercial property. Furthermore, the pricing unit can select the optimal pricing algorithm depending on the property category. This enables appropriate pricing by applying the optimal pricing algorithm depending on the property category. Some or all of the above-mentioned processing in the pricing unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the pricing unit can input property category data into the generation AI, which can then automatically apply the optimal pricing algorithm.

[0118] When responding to an inquiry, the inquiry response unit can adjust the level of detail of the response based on the importance of the property. For example, detailed information is provided for properties with high importance. The inquiry response unit can also provide simplified information for properties with low importance. Furthermore, the inquiry response unit can adjust the level of detail of the response based on the importance of the property. This allows for an appropriate response by adjusting the level of detail of the response based on the importance of the property. Some or all of the above-mentioned processing in the inquiry response unit may be performed using, or without, a generation AI. For example, the inquiry response unit can input property importance data into the generation AI, and the generation AI can automatically adjust the level of detail of the response.

[0119] When creating a contract, the contract creation unit can apply different contract creation algorithms depending on the property category. For example, a residential contract creation algorithm can be applied to a residential property. The contract creation unit can also apply a commercial contract creation algorithm to a commercial property. Furthermore, the contract creation unit can select the optimal contract creation algorithm depending on the property category. This makes it possible to create an appropriate contract by applying the optimal contract creation algorithm depending on the property category. Some or all of the above-mentioned processing in the contract creation unit may be performed using, or without, a generation AI, for example. For example, the contract creation unit can input property category data into the generation AI, and the generation AI can automatically apply the optimal contract creation algorithm.

[0120] The reception unit can estimate the seller's emotions and adjust the property information input method based on the estimated emotions. For example, if the seller is nervous, a simple and intuitive interface can be provided to minimize input steps. Alternatively, if the seller is relaxed, the reception unit can provide detailed input options and suggest customizable input methods. Furthermore, if the seller is in a hurry, the reception unit can prioritize voice input to enable quick input of property information. This can improve the efficiency of input work by adjusting the property information input method according to the seller's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation 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 reception unit can be performed using, for example, the generation AI. For example, the reception unit can input the seller's facial expression data into the generation AI, which can then automatically estimate the seller's emotions and adjust the input method.

[0121] The analysis unit can estimate the seller's emotions and adjust the market analysis criteria based on the estimated seller's emotions. For example, if the seller is nervous, the analysis unit can provide simple and intuitive market analysis results. Furthermore, if the seller is relaxed, the analysis unit can provide detailed market analysis results. Furthermore, if the seller is in a hurry, the analysis unit can provide market analysis results that focus on the key points. By adjusting the market analysis criteria according to the seller's emotions, appropriate market analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, 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 analysis unit can be performed using, for example, the generative AI, or without the generative AI. For example, the analysis unit can input the seller's facial expression data into the generative AI, which can then automatically estimate the emotions and adjust the market analysis criteria.

[0122] The pricing unit can estimate the seller's emotions and adjust the pricing method based on the estimated emotions. For example, if the seller is nervous, a simple and intuitive pricing method is provided. The pricing unit can also provide detailed pricing options if the seller is relaxed. Furthermore, if the seller is in a hurry, the pricing unit can provide a quick pricing method. This allows for appropriate pricing by adjusting the pricing method according to the seller's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation 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 pricing unit can be performed using, for example, the generation AI, or without the generation AI. For example, the pricing unit can input facial expression data of the seller into the generation AI, which can then automatically estimate the seller's emotions and adjust the pricing method.

[0123] The inquiry handling unit can estimate the buyer's emotions and adjust the inquiry handling method based on the estimated buyer's emotions. For example, if the buyer is nervous, the inquiry handling unit can respond in a calm tone. If the buyer is relaxed, the inquiry handling unit can also respond in a friendly tone. Furthermore, if the buyer is in a hurry, the inquiry handling unit can respond quickly. This allows for an appropriate response by adjusting the inquiry handling method according to the buyer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the inquiry handling unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the inquiry handling unit can input the buyer's facial expression data into the generation AI, which can automatically estimate the buyer's emotions and adjust the inquiry handling method.

[0124] The contract creation unit can estimate the emotions of the seller and buyer and adjust the contract creation method based on the estimated emotions. For example, if the seller and buyer are nervous, the contract creation unit can create a simple and intuitive contract. The contract creation unit can also create a detailed contract if the seller and buyer are relaxed. Furthermore, the contract creation unit can quickly create a contract if the seller and buyer are in a hurry. This allows for appropriate contract creation by adjusting the contract creation method according to the emotions of the seller and buyer. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the contract creation unit may be performed using, for example, the generative AI, or may be performed without using the generative AI. For example, the contract creation unit can input facial expression data of the seller and buyer into the generative AI, which can automatically estimate their emotions and adjust the contract creation method.

[0125] The processing flow of the second embodiment will be briefly explained below.

[0126] Step 1: The reception unit inputs property information. The property information includes, for example, the address, area, price, and facility information. The reception unit provides an interface for the seller to input the property information, and the interface is provided via a web form or a mobile application. Step 2: The analysis unit uses the generation AI to analyze the property information entered by the reception unit and conducts a market analysis to find a buyer. The market analysis is based on the property's past transaction data and current market trends. Step 3: The pricing unit uses the generation AI to propose a price based on the information obtained by the analysis unit, taking into account the property's location and the level of amenities.

[0127] 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.

[0128] 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 generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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 a data format such as voice data and text data. 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0129] 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.

[0130] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0131] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0132] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 model 58 includes AI 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0145] 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.

[0146] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0147] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 model 58 includes AI 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0161] 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.

[0162] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0163] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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).

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 model 58 includes AI 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0178] 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.

[0179] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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).

[0184] 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.

[0185] 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."

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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, in order to avoid confusion and to 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.

[0197] 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.

[0198] [Explanation of symbols]

[0199] 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 reception section for inputting property information; an analysis unit that analyzes the property information input by the reception unit and performs a market analysis to find a buyer; a price setting unit that proposes price setting based on the information obtained by the analysis unit; Equipped with A system characterized by:

2. Equipped with an inquiry department that responds to inquiries from buyers and provides detailed property information 2. The system of claim 1.

3. A contract preparation department is in place to prepare sales contracts and other necessary documents.

2. The system of claim 1.

4. The reception unit Estimate the seller's sentiment and adjust how property information is entered based on the estimated sentiment 2. The system of claim 1.

5. The reception unit Analyze sellers' past property information entry history and provide input assistance methods 2. The system of claim 1.

6. The reception unit Customize your property information based on your current market conditions and interests 2. The system of claim 1.

7. The reception unit When entering property information, select the input method according to the seller's input method.

2. The system of claim 1.

8. The reception unit Estimate the seller's sentiment and determine the priority of the property information to be entered based on the estimated sentiment of the seller 2. The system of claim 1.

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