system

Abstract

The system according to this embodiment aims to select the most suitable property based on the user's desired conditions, lifestyle, and budget, and to provide simple support for making viewing reservations and negotiating. [Solution] The system according to the embodiment comprises a reception unit, a selection unit, a provision unit, and a support unit. The reception unit receives information such as the user's desired conditions, lifestyle, and budget. The selection unit analyzes the information received by the reception unit and selects the most suitable property. The provision unit provides details of the property selected by the selection unit. The support unit assists with making viewing reservations for the property provided by the provision unit, preparing necessary documents, and negotiating.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the process of selecting an optimal property based on the user's desired conditions, lifestyle, and budget and supporting private viewing reservations and negotiations is complicated.

[0005] The system according to the embodiment aims to select an optimal property based on the user's desired conditions, lifestyle, and budget and to easily support private viewing reservations and negotiations.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a selection unit, a provision unit, and a support unit. The reception unit receives information such as the user's desired conditions, lifestyle, and budget. The selection unit analyzes the information received by the reception unit and selects the most suitable property. The provision unit provides details of the property selected by the selection unit. The support unit assists with scheduling viewings of the properties provided by the provision unit, preparing necessary documents, and negotiating. [Effects of the Invention]

[0007] The system according to this embodiment can select the most suitable property based on the user's desired conditions, lifestyle, and budget, and can easily support the user in making viewing reservations and negotiating. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

[0017] <0^000093>As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The housing consultation system according to an embodiment of the present invention is a system that utilizes generative AI to support individuals considering purchasing or renting a home in finding the optimal property. The housing consultation system allows users to input information such as their desired conditions, lifestyle, and budget. The generative AI analyzes this information and selects the most suitable property from a vast database. Furthermore, the housing consultation system provides detailed suggestions for the selected property and automatically handles tasks such as scheduling viewings, preparing necessary documents, and providing negotiation support. This mechanism allows users to easily find the most suitable property, significantly simplifying the real estate purchase and rental process. For example, searching for and comparing property information and arranging viewings are automated, saving users time and effort. Additionally, personalized property suggestions from the generative AI improve user satisfaction. Thus, the housing consultation system can propose the most suitable property based on the user's desired conditions, lifestyle, and budget, simplifying the real estate purchase and rental process.

[0029] The housing consultation system according to this embodiment comprises a reception unit, a selection unit, a provision unit, and a support unit. The reception unit receives information such as the user's desired conditions, lifestyle, and budget. The reception unit can, for example, store the information entered by the user in a database and use it for analysis later. The reception unit can also analyze the information entered by the user in real time and provide immediate feedback. For example, the reception unit can immediately display relevant property information based on the desired conditions entered by the user. The selection unit uses a generation AI to analyze the information received by the reception unit and select the most suitable property. For example, the generation AI can pick out the most suitable property from a vast amount of property data based on information such as the user's desired conditions, lifestyle, and budget. The selection unit can also have the generation AI analyze property data and display the most suitable properties to the user in a ranking format. For example, the generation AI can analyze information such as the price, location, and facilities of a property and propose the most suitable property to the user. The provision unit provides details of the property selected by the selection unit. The provisioning unit can, for example, display detailed property information to the user, schedule viewings, prepare necessary documents, and provide negotiation support. The provisioning unit can also provide additional information about the property selected by the user. For example, the provisioning unit can provide the user with photos, videos, and information about the surrounding environment. The support unit provides support for scheduling viewings, preparing necessary documents, and negotiating for properties provided by the provisioning unit. For example, the support unit can assist the user in preparing necessary documents when scheduling a viewing. Furthermore, the support unit can assist the user in negotiating the purchase or lease of a property. For example, the support unit can provide appropriate advice when the user is negotiating the price of a property. As a result, the housing consultation system according to this embodiment can propose the most suitable property based on the user's desired conditions, lifestyle, and budget, simplifying the real estate purchase and lease process.

[0030] The reception desk receives information such as the user's desired conditions, lifestyle, and budget. Specifically, it stores information entered by the user through web forms and applications in a database, which can then be used for analysis. For example, users can enter detailed conditions such as the floor plan, location, price range, and availability of surrounding facilities for their desired property. The reception desk can also analyze the information entered by the user in real time and provide immediate feedback. For example, when a user enters their desired conditions, the system immediately displays relevant property information and provides the user with options. Furthermore, the reception desk also collects information about the user's lifestyle. For example, it can collect information tailored to individual needs, such as whether the user owns pets, desired commute time, and proximity to children's schools. This allows the reception desk to respond to the diverse needs of users and provide more personalized services. In addition, based on the information entered by the user, the reception desk can analyze past data and trends to suggest the most suitable properties to the user. For example, based on past user data, it can identify trends in properties that match specific conditions and popular areas, and suggest them to the user. This allows the reception desk to provide property information quickly and accurately based on the user's desired conditions, improving user satisfaction.

[0031] The selection department uses generative AI to analyze information received by the reception department and select the most suitable properties. Specifically, the generative AI picks out the best properties from a vast amount of property data based on the user's desired conditions, lifestyle, budget, and other information. Using machine learning algorithms, the generative AI analyzes information such as property price, location, facilities, and surrounding environment, and can display the most suitable properties to the user in a ranking format. For example, the generative AI considers the price range and location conditions of properties to identify the property that best matches the user's budget. The generative AI can also suggest properties that suit the user's lifestyle. For example, it will suggest pet-friendly properties to users with pets, and properties with convenient transportation to users who prioritize commute time. Furthermore, the generative AI can evaluate the future value and risks of properties based on past user data and market trends. This allows the selection department to suggest the most suitable properties to users and improve user satisfaction. In addition, the selection department can incorporate user feedback when the generative AI analyzes property data. For example, if a user enters evaluations and comments on the suggested properties, the generative AI can learn from that feedback and reflect it in future suggestions. This allows the selection department to continue proposing properties that are better suited to the user's needs.

[0032] The provision department provides details of properties selected by the selection department. Specifically, it can display detailed property information to users and provide support for scheduling viewings, preparing necessary documents, and negotiations. For example, the provision department can provide users with photos, videos, floor plans, and information about the surrounding environment, allowing them to check the property details. The provision department can also provide additional information about properties selected by users. For example, it can provide the property's past transaction history, future value predictions, and information on the safety of the surrounding area. Furthermore, the provision department can support users in preparing necessary documents when scheduling viewings. For example, it can provide a viewing reservation form, allowing users to easily schedule viewings by entering the necessary information. The provision department can also support users in negotiations when purchasing or leasing a property. For example, it can provide support to help users proceed smoothly with the contract, such as negotiating the property price and adjusting contract terms. In this way, the provision department can improve user satisfaction by providing users with detailed property information and supporting the viewing and contract process. Furthermore, the provision department can respond quickly to any questions or concerns users may have about the property. For example, it is possible to answer user questions in real time through chatbots or customer support. This allows the service provider to provide users with quick and accurate information and alleviate their concerns and questions.

[0033] The support department assists with property viewings, document preparation, and negotiations for properties provided by the property offering department. Specifically, they can help users prepare necessary documents when booking a viewing. For example, they can provide a viewing reservation form, allowing users to easily book a viewing by simply entering the required information. The support department can also assist users with negotiations when purchasing or leasing a property. For example, they can provide support to help users smoothly proceed with the contract, such as negotiating the property price or adjusting contract terms. Furthermore, the support department can respond quickly to any questions or concerns users may have about the property. For example, they can provide real-time answers to user questions through chatbots or customer support. This allows the support department to provide users with quick and accurate information, alleviating their anxieties and doubts. In addition, the support department can assist users with preparing necessary documents when purchasing or leasing a property. For example, they can assist with preparing contracts and necessary supporting documents, ensuring a smooth transaction. The support department can also provide appropriate advice to users when purchasing or leasing a property. For example, they can provide support to help users smoothly proceed with the contract, such as negotiating the property price or adjusting contract terms. This allows the support department to provide users with quick and accurate information and resolve their concerns and questions.

[0034] The reception desk can analyze the user's past property selection history and suggest the optimal input method. For example, the reception desk can automatically display as candidates the user has frequently entered in the past. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest desired conditions to be used at specific times based on the user's past property selection history. This improves user convenience by suggesting the optimal input method based on past history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's past property selection history data into a generating AI and have the generating AI suggest the optimal input method.

[0035] The reception desk can filter the user's search criteria based on their current living situation and future plans. For example, if the user enters their family structure, the reception desk will prioritize suggesting properties that match the number of family members. If the user enters their future job change plans, the reception desk can also suggest properties that take commuting time into consideration. Furthermore, if the user enters their current living situation (e.g., whether they have pets, own a car), the reception desk can filter and suggest properties accordingly. This allows the reception desk to support users in choosing a more suitable property by suggesting properties that match their living situation and future plans. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's living situation and future plan data into a generating AI and have the generating AI perform the filtering.

[0036] The reception desk can prioritize the input of highly relevant conditions when users enter their desired conditions, taking into account their geographical location. For example, if a user is in a specific region, the reception desk can prioritize property conditions related to that region. Furthermore, if a user is traveling, the reception desk can prioritize property conditions related to their travel destination. Additionally, if a user is looking for a property within commuting distance, the reception desk can prioritize property conditions that take commuting time into account. This allows the reception desk to suggest properties that are highly relevant to the user by considering their geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's geographical location information into a generating AI and have the AI ​​suggest highly relevant conditions.

[0037] The reception desk can analyze the user's social media activity when they input their desired conditions and suggest relevant conditions. For example, the reception desk can suggest property conditions based on places the user has shared on social media. It can also suggest property conditions based on the activity of accounts the user follows on social media. Furthermore, it can suggest property conditions based on posts the user has "liked" on social media. In this way, by analyzing social media activity, it is possible to suggest property conditions that are highly relevant to the user. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media activity data into a generating AI and have the generating AI generate suggestions for relevant conditions.

[0038] The selection unit can improve the accuracy of property selection by considering the interrelationships between properties. For example, the selection unit can select the optimal property by comparing the prices and conditions of nearby properties. The selection unit can also consider information about surrounding facilities (schools, hospitals, supermarkets, etc.) when making selections. Furthermore, the selection unit can improve the accuracy of selection by analyzing the past transaction history of properties. In this way, the accuracy of selection can be improved by considering the interrelationships between properties. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input interrelationship data between properties into a generating AI and have the generating AI perform the task of improving the accuracy of selection.

[0039] The selection unit can make selections by considering the attribute information of the property provider when selecting properties. For example, the selection unit can evaluate the reliability of the property provider and prioritize the selection of highly reliable properties. The selection unit can also analyze the past transaction history of the property provider and select appropriate properties. Furthermore, the selection unit can make selections by referring to the evaluations and reviews of the property provider. In this way, by considering the attribute information of the property provider, highly reliable properties can be selected. Some or all of the above processes in the selection unit may be performed using AI, for example, or not using AI. For example, the selection unit can input the attribute information of the property provider into a generating AI and have the generating AI perform the selection.

[0040] The selection unit can select properties while considering their geographical distribution. For example, the selection unit can select properties based on the user's desired area. The selection unit can also select properties while considering their geographical convenience (transportation access, surrounding facilities, etc.). Furthermore, the selection unit can also select properties while considering their geographical safety (security, disaster risk, etc.). This allows the selection of properties that are highly convenient for the user by considering geographical distribution. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input geographical distribution data of properties into a generating AI and have the generating AI perform the selection.

[0041] The selection unit can improve the accuracy of its selection process by referring to relevant literature on properties. For example, the selection unit may refer to academic papers and research reports on properties. It can also refer to news articles and reviews on properties. Furthermore, the selection unit may refer to market research reports on properties. This allows for improved selection accuracy by referring to relevant literature. Some or all of the above processes in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input relevant literature data on properties into a generating AI and have the generating AI perform the task of improving selection accuracy.

[0042] The service provider can adjust the level of detail provided based on the importance of the property when providing property details. For example, the service provider can provide detailed information for important properties. It can also provide concise information for less important properties. Furthermore, it can provide detailed information for properties that match the user's desired conditions. In this way, by providing a level of detail according to the importance of the property, the service provider can provide the user with the information they need. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input property importance data into a generating AI and have the generating AI perform the adjustment of the level of detail.

[0043] The service provider can apply different display algorithms depending on the property category when providing property details. For example, it can apply different display algorithms for rental properties and properties for purchase. It can also apply different display algorithms for new properties and used properties. Furthermore, it can apply different display algorithms for commercial properties and residential properties. By applying a display algorithm according to the property category, it is possible to provide users with the most optimal information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input property category data into a generating AI and have the generating AI execute the application of the display algorithm.

[0044] The information provision unit can determine the priority of property details based on when the property was provided. For example, the information provision unit can prioritize providing detailed information for newly listed properties. It can also provide concise information for properties that have been listed for a long time. Furthermore, the information provision unit can prioritize providing detailed information for properties that match the user's desired conditions. By setting priorities based on when the property was provided, it is possible to provide users with important information. Some or all of the above processing in the information provision unit may be performed using AI, for example, or not using AI. For example, the information provision unit can input property provision date data into a generating AI and have the generating AI perform the priority determination.

[0045] The service provider can adjust the order of property details based on their relevance when providing them. For example, the service provider can prioritize displaying properties that best match the user's desired conditions. The service provider can also adjust the display order based on the property's price and conditions. Furthermore, the service provider can adjust the display order based on the property's rating and reviews. This allows the service provider to prioritize providing users with information that is important to them by setting an order based on the relevance of the properties. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input property relevance data into a generating AI and have the generating AI perform the order adjustment.

[0046] The support department can customize support methods based on the user's current living situation when scheduling viewings or preparing documents. For example, if the user is busy with work, the support department will prioritize suggesting online viewing appointments. If the user lives with family, the support department can also suggest viewing appointments that the whole family can attend. Furthermore, if the user has pets, the support department can suggest pet-friendly viewing appointments. This improves user convenience by providing support methods tailored to the user's living situation. Some or all of the above processing in the support department may be performed using AI, for example, or not. For example, the support department can input user living situation data into a generating AI and have the generating AI perform the customization of support methods.

[0047] The support department can select the most suitable support method by considering the user's geographical location when scheduling viewings or preparing documents. For example, if the user is in a specific area, the support department will prioritize suggesting viewings related to that area. Furthermore, if the user is traveling, the support department can prioritize suggesting viewings at their travel destination. Additionally, if the user is looking for a property within commuting distance, the support department can suggest viewings that take commuting time into consideration. This allows the support department to provide the most suitable support method by considering geographical location. Some or all of the above processing in the support department may be performed using AI, or not. For example, the support department can input the user's geographical location information into a generating AI and have the generating AI select the most suitable support method.

[0048] The support department can analyze a user's social media activity and suggest support measures when they are making a viewing appointment or preparing documents. For example, the support department can suggest a viewing appointment based on places the user has shared on social media. It can also suggest document preparation based on the activity of accounts the user follows on social media. Furthermore, the support department can suggest support measures based on posts the user has "liked" on social media. In this way, by analyzing social media activity, it is possible to provide support measures that are highly relevant to the user. Some or all of the above processing in the support department may be performed using AI, for example, or not using AI. For example, the support department can input the user's social media activity data into a generating AI and have the generating AI execute the suggestion of support measures.

[0049] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0050] The housing consultation system can also acquire user health data and incorporate it into property selection. For example, if a user has allergies, it can prioritize suggesting areas and properties with low allergen levels. If a user enjoys exercise, it can suggest properties with nearby gyms or parks. Furthermore, if a user is elderly, it can prioritize suggesting barrier-free properties. This allows the system to support users in choosing properties that suit their health condition.

[0051] The housing consultation system can analyze a user's past property selection history and propose an optimal property selection algorithm. For example, it can analyze the characteristics of properties the user has previously selected and prioritize suggesting properties with similar characteristics. It can also consider the characteristics of properties the user has avoided in the past and exclude properties with those characteristics. Furthermore, it can suggest properties related to specific seasons or events based on the user's past selection history. In this way, it can support the selection of the optimal property based on past history.

[0052] The housing consultation system can adjust property selection criteria based on the user's current living situation and future plans. For example, if a user plans to change their family structure, it can suggest properties that suit that plan. If a user plans to change jobs in the future, it can suggest properties that take commuting time into consideration. Furthermore, if a user plans to own a pet, it can prioritize suggesting pet-friendly properties. This allows the system to support users in choosing a property that suits their living situation and future plans.

[0053] The housing consultation system can adjust property selection criteria based on the user's geographical location. For example, if a user is in a specific area, it will prioritize suggesting properties related to that area. If a user is traveling, it can also prioritize suggesting properties in their travel destination. Furthermore, if a user is looking for a property within commuting distance, it can prioritize suggesting properties that take commuting time into consideration. This allows the system to suggest properties that are highly relevant to the user by taking geographical location into account.

[0054] The housing consultation system can analyze users' social media activity and reflect this in property selection. For example, it can suggest properties based on places users have shared on social media. It can also suggest properties based on the activity of accounts users follow. Furthermore, it can suggest properties based on posts users have "liked." In this way, by analyzing social media activity, it can suggest properties that are highly relevant to the user.

[0055] The housing consultation system can improve the accuracy of property selection by considering the interrelationships between properties. For example, it can select the optimal property by comparing the prices and conditions of nearby properties. It can also select properties by considering information about surrounding facilities (schools, hospitals, supermarkets, etc.). Furthermore, it can improve selection accuracy by analyzing the past transaction history of properties. In this way, the accuracy of selection can be improved by considering the interrelationships between properties.

[0056] The housing consultation system can select properties by considering the attribute information of the property provider. For example, it can evaluate the reliability of the property provider and prioritize the selection of highly reliable properties. It can also analyze the past transaction history of the property provider to select appropriate properties. Furthermore, it can select properties by referring to the ratings and reviews of the property provider. In this way, by considering the attribute information of the property provider, it is possible to select highly reliable properties.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The reception desk receives information such as the user's preferences, lifestyle, and budget. The reception desk stores the information entered by the user in a database, which can then be used for analysis. The reception desk can also analyze the information entered by the user in real time and provide immediate feedback. For example, it can instantly display relevant property information based on the preferences entered by the user. Step 2: The selection unit uses a generation AI to analyze the information received by the reception unit and select the most suitable property. The selection unit's generation AI can pick out the most suitable property from a vast amount of property data based on information such as the user's desired conditions, lifestyle, and budget. The generation AI can also analyze the property data and display the most suitable properties to the user in a ranking format. For example, it can analyze information such as property price, location, and facilities to suggest the most suitable property to the user. Step 3: The provisioning department provides details of the properties selected by the selection department. The provisioning department can display detailed property information to the user and provide support for scheduling viewings, preparing necessary documents, and negotiations. They can also provide additional information about the properties selected by the user, such as photos and videos of the property and information about the surrounding environment. Step 4: The support department assists with scheduling viewings, preparing necessary documents, and negotiating for properties provided by the offering department. The support department can assist users in preparing necessary documents when scheduling viewings. They can also assist users in negotiating when purchasing or leasing a property. For example, they can provide appropriate advice when users are negotiating the price of a property.

[0059] (Example of form 2) The housing consultation system according to an embodiment of the present invention is a system that utilizes generative AI to support individuals considering purchasing or renting a home in finding the optimal property. The housing consultation system allows users to input information such as their desired conditions, lifestyle, and budget. The generative AI analyzes this information and selects the most suitable property from a vast database. Furthermore, the housing consultation system provides detailed suggestions for the selected property and automatically handles tasks such as scheduling viewings, preparing necessary documents, and providing negotiation support. This mechanism allows users to easily find the most suitable property, significantly simplifying the real estate purchase and rental process. For example, searching for and comparing property information and arranging viewings are automated, saving users time and effort. Additionally, personalized property suggestions from the generative AI improve user satisfaction. Thus, the housing consultation system can propose the most suitable property based on the user's desired conditions, lifestyle, and budget, simplifying the real estate purchase and rental process.

[0060] The housing consultation system according to this embodiment comprises a reception unit, a selection unit, a provision unit, and a support unit. The reception unit receives information such as the user's desired conditions, lifestyle, and budget. The reception unit can, for example, store the information entered by the user in a database and use it for analysis later. The reception unit can also analyze the information entered by the user in real time and provide immediate feedback. For example, the reception unit can immediately display relevant property information based on the desired conditions entered by the user. The selection unit uses a generation AI to analyze the information received by the reception unit and select the most suitable property. For example, the generation AI can pick out the most suitable property from a vast amount of property data based on information such as the user's desired conditions, lifestyle, and budget. The selection unit can also have the generation AI analyze property data and display the most suitable properties to the user in a ranking format. For example, the generation AI can analyze information such as the price, location, and facilities of a property and propose the most suitable property to the user. The provision unit provides details of the property selected by the selection unit. The provisioning unit can, for example, display detailed property information to the user, schedule viewings, prepare necessary documents, and provide negotiation support. The provisioning unit can also provide additional information about the property selected by the user. For example, the provisioning unit can provide the user with photos, videos, and information about the surrounding environment. The support unit provides support for scheduling viewings, preparing necessary documents, and negotiating for properties provided by the provisioning unit. For example, the support unit can assist the user in preparing necessary documents when scheduling a viewing. Furthermore, the support unit can assist the user in negotiating the purchase or lease of a property. For example, the support unit can provide appropriate advice when the user is negotiating the price of a property. As a result, the housing consultation system according to this embodiment can propose the most suitable property based on the user's desired conditions, lifestyle, and budget, simplifying the real estate purchase and lease process.

[0061] The reception desk receives information such as the user's desired conditions, lifestyle, and budget. Specifically, it stores information entered by the user through web forms and applications in a database, which can then be used for analysis. For example, users can enter detailed conditions such as the floor plan, location, price range, and availability of surrounding facilities for their desired property. The reception desk can also analyze the information entered by the user in real time and provide immediate feedback. For example, when a user enters their desired conditions, the system immediately displays relevant property information and provides the user with options. Furthermore, the reception desk also collects information about the user's lifestyle. For example, it can collect information tailored to individual needs, such as whether the user owns pets, desired commute time, and proximity to children's schools. This allows the reception desk to respond to the diverse needs of users and provide more personalized services. In addition, based on the information entered by the user, the reception desk can analyze past data and trends to suggest the most suitable properties to the user. For example, based on past user data, it can identify trends in properties that match specific conditions and popular areas, and suggest them to the user. This allows the reception desk to provide property information quickly and accurately based on the user's desired conditions, improving user satisfaction.

[0062] The selection department uses generative AI to analyze information received by the reception department and select the most suitable properties. Specifically, the generative AI picks out the best properties from a vast amount of property data based on the user's desired conditions, lifestyle, budget, and other information. Using machine learning algorithms, the generative AI analyzes information such as property price, location, facilities, and surrounding environment, and can display the most suitable properties to the user in a ranking format. For example, the generative AI considers the price range and location conditions of properties to identify the property that best matches the user's budget. The generative AI can also suggest properties that suit the user's lifestyle. For example, it will suggest pet-friendly properties to users with pets, and properties with convenient transportation to users who prioritize commute time. Furthermore, the generative AI can evaluate the future value and risks of properties based on past user data and market trends. This allows the selection department to suggest the most suitable properties to users and improve user satisfaction. In addition, the selection department can incorporate user feedback when the generative AI analyzes property data. For example, if a user enters evaluations and comments on the suggested properties, the generative AI can learn from that feedback and reflect it in future suggestions. This allows the selection department to continue proposing properties that are better suited to the user's needs.

[0063] The provision department provides details of properties selected by the selection department. Specifically, it can display detailed property information to users and provide support for scheduling viewings, preparing necessary documents, and negotiations. For example, the provision department can provide users with photos, videos, floor plans, and information about the surrounding environment, allowing them to check the property details. The provision department can also provide additional information about properties selected by users. For example, it can provide the property's past transaction history, future value predictions, and information on the safety of the surrounding area. Furthermore, the provision department can support users in preparing necessary documents when scheduling viewings. For example, it can provide a viewing reservation form, allowing users to easily schedule viewings by entering the necessary information. The provision department can also support users in negotiations when purchasing or leasing a property. For example, it can provide support to help users proceed smoothly with the contract, such as negotiating the property price and adjusting contract terms. In this way, the provision department can improve user satisfaction by providing users with detailed property information and supporting the viewing and contract process. Furthermore, the provision department can respond quickly to any questions or concerns users may have about the property. For example, it is possible to answer user questions in real time through chatbots or customer support. This allows the service provider to provide users with quick and accurate information and alleviate their concerns and questions.

[0064] The support department assists with property viewings, document preparation, and negotiations for properties provided by the property offering department. Specifically, they can help users prepare necessary documents when booking a viewing. For example, they can provide a viewing reservation form, allowing users to easily book a viewing by simply entering the required information. The support department can also assist users with negotiations when purchasing or leasing a property. For example, they can provide support to help users smoothly proceed with the contract, such as negotiating the property price or adjusting contract terms. Furthermore, the support department can respond quickly to any questions or concerns users may have about the property. For example, they can provide real-time answers to user questions through chatbots or customer support. This allows the support department to provide users with quick and accurate information, alleviating their anxieties and doubts. In addition, the support department can assist users with preparing necessary documents when purchasing or leasing a property. For example, they can assist with preparing contracts and necessary supporting documents, ensuring a smooth transaction. The support department can also provide appropriate advice to users when purchasing or leasing a property. For example, they can provide support to help users smoothly proceed with the contract, such as negotiating the property price or adjusting contract terms. This allows the support department to provide users with quick and accurate information and resolve their concerns and questions.

[0065] The reception desk can estimate the user's emotions and adjust the input interface for desired conditions based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of desired conditions. This reduces the stress of input by providing an interface that responds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0066] The reception desk can analyze the user's past property selection history and suggest the optimal input method. For example, the reception desk can automatically display as candidates the user has frequently entered in the past. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest desired conditions to be used at specific times based on the user's past property selection history. This improves user convenience by suggesting the optimal input method based on past history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's past property selection history data into a generating AI and have the generating AI suggest the optimal input method.

[0067] The reception desk can filter the user's search criteria based on their current living situation and future plans. For example, if the user enters their family structure, the reception desk will prioritize suggesting properties that match the number of family members. If the user enters their future job change plans, the reception desk can also suggest properties that take commuting time into consideration. Furthermore, if the user enters their current living situation (e.g., whether they have pets, own a car), the reception desk can filter and suggest properties accordingly. This allows the reception desk to support users in choosing a more suitable property by suggesting properties that match their living situation and future plans. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's living situation and future plan data into a generating AI and have the generating AI perform the filtering.

[0068] The reception desk can estimate the user's emotions and determine the priority of the input preferences based on the estimated emotions. For example, if the user is stressed, the reception desk may prioritize inputting important preferences. If the user is relaxed, the reception desk may also prioritize inputting detailed preferences. Furthermore, if the user is in a hurry, the reception desk may prioritize inputting minimal preferences. This supports efficient input by setting priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk may input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0069] The reception desk can prioritize the input of highly relevant conditions when users enter their desired conditions, taking into account their geographical location. For example, if a user is in a specific region, the reception desk can prioritize property conditions related to that region. Furthermore, if a user is traveling, the reception desk can prioritize property conditions related to their travel destination. Additionally, if a user is looking for a property within commuting distance, the reception desk can prioritize property conditions that take commuting time into account. This allows the reception desk to suggest properties that are highly relevant to the user by considering their geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's geographical location information into a generating AI and have the AI ​​suggest highly relevant conditions.

[0070] The reception desk can analyze the user's social media activity when they input their desired conditions and suggest relevant conditions. For example, the reception desk can suggest property conditions based on places the user has shared on social media. It can also suggest property conditions based on the activity of accounts the user follows on social media. Furthermore, it can suggest property conditions based on posts the user has "liked" on social media. In this way, by analyzing social media activity, it is possible to suggest property conditions that are highly relevant to the user. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media activity data into a generating AI and have the generating AI generate suggestions for relevant conditions.

[0071] The selection unit can estimate the user's emotions and adjust the property selection criteria based on the estimated emotions. For example, if the user is stressed, the selection unit can apply simple property selection criteria. If the user is relaxed, the selection unit can also apply detailed property selection criteria. Furthermore, if the user is in a hurry, the selection unit can apply criteria for quickly selecting a property. This allows for the selection of a more appropriate property by applying property selection criteria that are tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the selection unit may be performed using AI, or not using AI. For example, the selection unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0072] The selection unit can improve the accuracy of property selection by considering the interrelationships between properties. For example, the selection unit can select the optimal property by comparing the prices and conditions of nearby properties. The selection unit can also consider information about surrounding facilities (schools, hospitals, supermarkets, etc.) when making selections. Furthermore, the selection unit can improve the accuracy of selection by analyzing the past transaction history of properties. In this way, the accuracy of selection can be improved by considering the interrelationships between properties. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input interrelationship data between properties into a generating AI and have the generating AI perform the task of improving the accuracy of selection.

[0073] The selection unit can make selections by considering the attribute information of the property provider when selecting properties. For example, the selection unit can evaluate the reliability of the property provider and prioritize the selection of highly reliable properties. The selection unit can also analyze the past transaction history of the property provider and select appropriate properties. Furthermore, the selection unit can make selections by referring to the evaluations and reviews of the property provider. In this way, by considering the attribute information of the property provider, highly reliable properties can be selected. Some or all of the above processes in the selection unit may be performed using AI, for example, or not using AI. For example, the selection unit can input the attribute information of the property provider into a generating AI and have the generating AI perform the selection.

[0074] The selection unit can estimate the user's emotions and adjust the display order of selected properties based on the estimated user emotions. For example, if the user is feeling stressed, the selection unit may prioritize displaying important properties. It can also prioritize displaying properties with detailed information if the user is relaxed. Furthermore, if the user is in a hurry, it may prioritize displaying properties that can be quickly reviewed. This allows for efficient property selection by setting a display order that matches the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the selection unit may be performed using AI or not. For example, the selection unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0075] The selection unit can select properties while considering their geographical distribution. For example, the selection unit can select properties based on the user's desired area. The selection unit can also select properties while considering their geographical convenience (transportation access, surrounding facilities, etc.). Furthermore, the selection unit can also select properties while considering their geographical safety (security, disaster risk, etc.). This allows the selection of properties that are highly convenient for the user by considering geographical distribution. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input geographical distribution data of properties into a generating AI and have the generating AI perform the selection.

[0076] The selection unit can improve the accuracy of its selection process by referring to relevant literature on properties. For example, the selection unit may refer to academic papers and research reports on properties. It can also refer to news articles and reviews on properties. Furthermore, the selection unit may refer to market research reports on properties. This allows for improved selection accuracy by referring to relevant literature. Some or all of the above processes in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input relevant literature data on properties into a generating AI and have the generating AI perform the task of improving selection accuracy.

[0077] The service provider can estimate the user's emotions and adjust the display method of property details based on the estimated emotions. For example, if the user is stressed, the service provider can provide a simple and highly visible display method. If the user is relaxed, the service provider can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the service provider can provide a concise display method. This allows for a deeper understanding of property details by providing a display method that matches the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, or not using AI. For example, the service provider can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0078] The service provider can adjust the level of detail provided based on the importance of the property when providing property details. For example, the service provider can provide detailed information for important properties. It can also provide concise information for less important properties. Furthermore, it can provide detailed information for properties that match the user's desired conditions. In this way, by providing a level of detail according to the importance of the property, the service provider can provide the user with the information they need. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input property importance data into a generating AI and have the generating AI perform the adjustment of the level of detail.

[0079] The service provider can apply different display algorithms depending on the property category when providing property details. For example, it can apply different display algorithms for rental properties and properties for purchase. It can also apply different display algorithms for new properties and used properties. Furthermore, it can apply different display algorithms for commercial properties and residential properties. By applying a display algorithm according to the property category, it is possible to provide users with the most optimal information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input property category data into a generating AI and have the generating AI execute the application of the display algorithm.

[0080] The service provider can estimate the user's emotions and adjust the length of the property details based on the estimated emotions. For example, if the user is stressed, the service provider can provide concise property details. If the user is relaxed, the service provider can also provide detailed property information. Furthermore, if the user is in a hurry, the service provider can provide concise property details. This allows for a deeper understanding of the property information by providing detail lengths that match the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0081] The information provision unit can determine the priority of property details based on when the property was provided. For example, the information provision unit can prioritize providing detailed information for newly listed properties. It can also provide concise information for properties that have been listed for a long time. Furthermore, the information provision unit can prioritize providing detailed information for properties that match the user's desired conditions. By setting priorities based on when the property was provided, it is possible to provide users with important information. Some or all of the above processing in the information provision unit may be performed using AI, for example, or not using AI. For example, the information provision unit can input property provision date data into a generating AI and have the generating AI perform the priority determination.

[0082] The service provider can adjust the order of property details based on their relevance when providing them. For example, the service provider can prioritize displaying properties that best match the user's desired conditions. The service provider can also adjust the display order based on the property's price and conditions. Furthermore, the service provider can adjust the display order based on the property's rating and reviews. This allows the service provider to prioritize providing users with information that is important to them by setting an order based on the relevance of the properties. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input property relevance data into a generating AI and have the generating AI perform the order adjustment.

[0083] The support unit can estimate the user's emotions and adjust the viewing appointment and document preparation methods based on the estimated emotions. For example, if the user is feeling stressed, the support unit can provide a simple viewing appointment procedure. If the user is relaxed, the support unit can also provide detailed viewing appointment options. Furthermore, if the user is in a hurry, the support unit can provide a procedure for quickly making a viewing appointment. This improves user convenience by providing viewing appointment and document preparation methods that are tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support unit may be performed using AI or not using AI. For example, the support unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0084] The support department can customize support methods based on the user's current living situation when scheduling viewings or preparing documents. For example, if the user is busy with work, the support department will prioritize suggesting online viewing appointments. If the user lives with family, the support department can also suggest viewing appointments that the whole family can attend. Furthermore, if the user has pets, the support department can suggest pet-friendly viewing appointments. This improves user convenience by providing support methods tailored to the user's living situation. Some or all of the above processing in the support department may be performed using AI, for example, or not. For example, the support department can input user living situation data into a generating AI and have the generating AI perform the customization of support methods.

[0085] The support unit can estimate the user's emotions and determine the priority of support based on the estimated emotions. For example, if the user is stressed, the support unit will prioritize providing important support. It can also provide detailed support if the user is relaxed. Furthermore, if the user is in a hurry, the support unit can prioritize providing support that can be addressed quickly. This allows for efficient support by setting support priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support unit may be performed using AI, or not. For example, the support unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0086] The support department can select the most suitable support method by considering the user's geographical location when scheduling viewings or preparing documents. For example, if the user is in a specific area, the support department will prioritize suggesting viewings related to that area. Furthermore, if the user is traveling, the support department can prioritize suggesting viewings at their travel destination. Additionally, if the user is looking for a property within commuting distance, the support department can suggest viewings that take commuting time into consideration. This allows the support department to provide the most suitable support method by considering geographical location. Some or all of the above processing in the support department may be performed using AI, or not. For example, the support department can input the user's geographical location information into a generating AI and have the generating AI select the most suitable support method.

[0087] The support department can analyze a user's social media activity and suggest support measures when they are making a viewing appointment or preparing documents. For example, the support department can suggest a viewing appointment based on places the user has shared on social media. It can also suggest document preparation based on the activity of accounts the user follows on social media. Furthermore, the support department can suggest support measures based on posts the user has "liked" on social media. In this way, by analyzing social media activity, it is possible to provide support measures that are highly relevant to the user. Some or all of the above processing in the support department may be performed using AI, for example, or not using AI. For example, the support department can input the user's social media activity data into a generating AI and have the generating AI execute the suggestion of support measures.

[0088] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0089] The housing consultation system can also acquire user health data and incorporate it into property selection. For example, if a user has allergies, it can prioritize suggesting areas and properties with low allergen levels. If a user enjoys exercise, it can suggest properties with nearby gyms or parks. Furthermore, if a user is elderly, it can prioritize suggesting barrier-free properties. This allows the system to support users in choosing properties that suit their health condition.

[0090] The housing consultation system can estimate the user's emotions and adjust the information provided during property viewings based on those emotions. For example, if the user is feeling stressed, it can provide simple, concise information during the viewing. If the user is relaxed, it can provide detailed property information and information about the surrounding environment. Furthermore, if the user is in a hurry, it can provide information that can be quickly reviewed during the viewing. In this way, by providing information tailored to the user's emotions, the efficiency of property viewings can be improved.

[0091] The housing consultation system can analyze a user's past property selection history and propose an optimal property selection algorithm. For example, it can analyze the characteristics of properties the user has previously selected and prioritize suggesting properties with similar characteristics. It can also consider the characteristics of properties the user has avoided in the past and exclude properties with those characteristics. Furthermore, it can suggest properties related to specific seasons or events based on the user's past selection history. In this way, it can support the selection of the optimal property based on past history.

[0092] The housing consultation system can adjust property selection criteria based on the user's current living situation and future plans. For example, if a user plans to change their family structure, it can suggest properties that suit that plan. If a user plans to change jobs in the future, it can suggest properties that take commuting time into consideration. Furthermore, if a user plans to own a pet, it can prioritize suggesting pet-friendly properties. This allows the system to support users in choosing a property that suits their living situation and future plans.

[0093] The housing consultation system can estimate the user's emotions and adjust the property selection criteria based on those emotions. For example, if the user is feeling stressed, simple selection criteria can be applied to quickly select a property. If the user is relaxed, detailed selection criteria can be applied to allow for comparison of more properties. Furthermore, if the user is in a hurry, minimal selection criteria can be applied to quickly select a property. In this way, by applying selection criteria tailored to the user's emotions, the system can support efficient property selection.

[0094] The housing consultation system can adjust property selection criteria based on the user's geographical location. For example, if a user is in a specific area, it will prioritize suggesting properties related to that area. If a user is traveling, it can also prioritize suggesting properties in their travel destination. Furthermore, if a user is looking for a property within commuting distance, it can prioritize suggesting properties that take commuting time into consideration. This allows the system to suggest properties that are highly relevant to the user by taking geographical location into account.

[0095] The housing consultation system can analyze users' social media activity and reflect this in property selection. For example, it can suggest properties based on places users have shared on social media. It can also suggest properties based on the activity of accounts users follow. Furthermore, it can suggest properties based on posts users have "liked." In this way, by analyzing social media activity, it can suggest properties that are highly relevant to the user.

[0096] The housing consultation system can estimate the user's emotions and adjust the display order of properties based on those emotions. For example, if the user is feeling stressed, important properties will be displayed first. If the user is relaxed, properties with detailed information can be displayed first. Furthermore, if the user is in a hurry, properties that can be quickly reviewed can be displayed first. In this way, by setting the display order according to the user's emotions, it can support efficient property selection.

[0097] The housing consultation system can improve the accuracy of property selection by considering the interrelationships between properties. For example, it can select the optimal property by comparing the prices and conditions of nearby properties. It can also select properties by considering information about surrounding facilities (schools, hospitals, supermarkets, etc.). Furthermore, it can improve selection accuracy by analyzing the past transaction history of properties. In this way, the accuracy of selection can be improved by considering the interrelationships between properties.

[0098] The housing consultation system can select properties by considering the attribute information of the property provider. For example, it can evaluate the reliability of the property provider and prioritize the selection of highly reliable properties. It can also analyze the past transaction history of the property provider to select appropriate properties. Furthermore, it can select properties by referring to the ratings and reviews of the property provider. In this way, by considering the attribute information of the property provider, it is possible to select highly reliable properties.

[0099] The following briefly describes the processing flow for example form 2.

[0100] Step 1: The reception desk receives information such as the user's preferences, lifestyle, and budget. The reception desk stores the information entered by the user in a database, which can then be used for analysis. The reception desk can also analyze the information entered by the user in real time and provide immediate feedback. For example, it can instantly display relevant property information based on the preferences entered by the user. Step 2: The selection unit uses a generation AI to analyze the information received by the reception unit and select the most suitable property. The selection unit's generation AI can pick out the most suitable property from a vast amount of property data based on information such as the user's desired conditions, lifestyle, and budget. The generation AI can also analyze the property data and display the most suitable properties to the user in a ranking format. For example, it can analyze information such as property price, location, and facilities to suggest the most suitable property to the user. Step 3: The provisioning department provides details of the properties selected by the selection department. The provisioning department can display detailed property information to the user and provide support for scheduling viewings, preparing necessary documents, and negotiations. They can also provide additional information about the properties selected by the user, such as photos and videos of the property and information about the surrounding environment. Step 4: The support department assists with scheduling viewings, preparing necessary documents, and negotiating for properties provided by the offering department. The support department can assist users in preparing necessary documents when scheduling viewings. They can also assist users in negotiating when purchasing or leasing a property. For example, they can provide appropriate advice when users are negotiating the price of a property.

[0101] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0102] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0103] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0104] Each of the multiple elements described above, including the reception unit, selection unit, provision unit, and support unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and receives information such as the user's desired conditions, lifestyle, and budget. The selection unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and selects the most suitable property using generating AI. The provision unit is implemented by, for example, the output device 40 of the smart device 14 and provides details of the selected property. The support unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides support for viewing reservations, preparation of necessary documents, and negotiations. The correspondence between each unit and the devices and control units is not limited to the example described above and can be changed in various ways.

[0105] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0106] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0107] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0108] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0109] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0110] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0111] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0112] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0113] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0114] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0115] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0116] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0118] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0119] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0120] Each of the multiple elements described above, including the reception unit, selection unit, provision unit, and support unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and receives information such as the user's desired conditions, lifestyle, and budget. The selection unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and selects the most suitable property using generating AI. The provision unit is implemented by, for example, the speaker 240 of the smart glasses 214 and provides details of the selected property. The support unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides support for making viewing reservations, preparing necessary documents, and negotiations. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0121] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0122] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0123] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0124] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0125] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0127] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0128] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0129] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0130] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0131] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0132] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0135] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0136] Each of the multiple elements described above, including the reception unit, selection unit, provision unit, and support unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and receives information such as the user's desired conditions, lifestyle, and budget. The selection unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and selects the most suitable property using a generation AI. The provision unit is implemented by, for example, the display 343 of the headset terminal 314 and provides details of the selected property. The support unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides support for viewing reservations, preparation of necessary documents, and negotiations. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0137] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0138] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0139] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0140] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0141] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0143] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0144] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0145] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0146] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0147] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0148] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0149] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0150] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0151] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0152] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0153] Each of the multiple elements described above, including the reception unit, selection unit, provision unit, and support unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and receives information such as the user's desired conditions, lifestyle, and budget. The selection unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and selects the most suitable property using generating AI. The provision unit is implemented by, for example, the speaker 240 of the robot 414 and provides details of the selected property. The support unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides support for making viewing reservations, preparing necessary documents, and negotiations. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0154] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0155] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0156] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0157] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0158] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0159] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0160] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0161] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0162] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0163] 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.

[0164] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0165] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0166] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0167] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0168] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0169] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0170] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0171] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0172] (Note 1) The reception desk receives information such as the user's desired conditions, lifestyle, and budget, A selection unit analyzes the information received by the reception unit and selects the most suitable property, A provision unit that provides details of the property selected by the aforementioned selection unit, The system includes a support department that handles tasks such as scheduling viewings of properties provided by the aforementioned provision department, preparing necessary documents, and providing support for negotiations. A system characterized by the following features. (Note 2) The aforementioned reception unit is It estimates the user's emotions and adjusts the input interface for desired conditions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned reception unit is We analyze the user's past property selection history and suggest the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is When users enter their desired criteria, filtering is performed based on their current living situation and future plans. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is It estimates the user's emotions and determines the priority of the input preferences based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is When users enter their desired conditions, the system prioritizes highly relevant conditions by considering their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is When you enter your desired criteria, the system analyzes your social media activity and suggests relevant criteria. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned selection unit is The system estimates user sentiment and adjusts property selection criteria based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned selection unit is When selecting properties, consider the interrelationships between properties to improve the accuracy of the selection process. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned selection unit is When selecting a property, we take into consideration the attributes of the property provider. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned selection unit is The system estimates the user's emotions and adjusts the display order of selected properties based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned selection unit is When selecting properties, the geographical distribution of the properties should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned selection unit is When selecting a property, refer to relevant literature to improve the accuracy of the selection. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned supply unit is, The system estimates the user's emotions and adjusts how property details are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned supply unit is, When providing property details, adjust the level of detail based on the importance of the property. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned supply unit is, When providing property details, different display algorithms are applied depending on the property category. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned supply unit is, The system estimates the user's emotions and adjusts the length of the property details based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, When providing property details, we will prioritize the details based on when the property was made available. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When providing property details, the order of the details will be adjusted based on the relevance of the properties. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned support unit is The system estimates the user's emotions and adjusts the viewing reservation and document preparation methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned support unit is When scheduling viewings or preparing documents, the support methods are customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned support unit is The system estimates the user's emotions and determines support priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned support unit is When scheduling viewings or preparing documents, the system selects the most suitable support method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned support unit is We analyze users' social media activity and suggest support methods when they are making viewing appointments or preparing documents. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0173] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The reception desk receives information such as the user's desired conditions, lifestyle, and budget, A selection unit analyzes the information received by the reception unit and selects the most suitable property, A provision unit that provides details of the property selected by the aforementioned selection unit, The system includes a support department that handles property viewing appointments, preparation of necessary documents, and negotiation support for properties provided by the aforementioned provision department. A system characterized by the following features.

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

3. The aforementioned reception unit is We analyze the user's past property selection history and suggest the optimal input method. The system according to feature 1.

4. The aforementioned reception unit is When users enter their desired criteria, filtering is performed based on their current living situation and future plans. The system according to feature 1.

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

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

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

8. The aforementioned selection unit is The system estimates user sentiment and adjusts property selection criteria based on the estimated sentiment. The system according to feature 1.

9. The aforementioned selection unit is When selecting properties, consider the interrelationships between properties to improve the accuracy of the selection process. The system according to feature 1.

10. The aforementioned selection unit is When selecting a property, we take into consideration the attributes of the property provider. The system according to feature 1.

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