system

Abstract

The system according to this embodiment aims to provide optimal property information based on the user's desired conditions and lifestyle. [Solution] The system according to the embodiment comprises a hearing unit, a generation unit, a proposal unit, and a provision unit. The hearing unit conducts a detailed hearing of the user's desired conditions and lifestyle. The generation unit generates optimal property information based on the information gathered by the hearing unit. The proposal unit makes suggestions to relax the conditions based on the property information generated by the generation unit. The provision unit provides the property information proposed by the proposal unit to the user.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, optimal property information based on the user's desired conditions and lifestyle has not been sufficiently provided, and there is room for improvement.

[0005] The system according to the embodiment aims to provide optimal property information based on the user's desired conditions and lifestyle.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a hearing unit, a generation unit, a proposal unit, and a provision unit. The hearing unit conducts detailed interviews with the user regarding their desired conditions and lifestyle. The generation unit generates optimal property information based on the information gathered by the hearing unit. The proposal unit makes suggestions to relax the conditions based on the property information generated by the generation unit. The provision unit provides the property information proposed by the proposal unit to the user. [Effects of the Invention]

[0007] The system according to this embodiment can provide optimal property information based on the user's desired conditions and lifestyle. [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 applied 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] 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 Home Search System according to an embodiment of the present invention is a system that uses a conversational AI agent to conduct detailed interviews with users considering purchasing or renting a home, and utilizes a generative AI to provide personalized property recommendations. The Home Search System provides a supportive experience that allows users to relax their conditions based on their priorities, helping them find the optimal home. For example, the Home Search System allows users to input their desired conditions for purchasing or renting a home into the conversational AI agent. For example, they might input conditions such as "within a 10-minute walk from the station," "south-facing," and "budget under 5 million yen." The conversational AI agent conducts detailed interviews with the user about their desired conditions and lifestyle and provides this information to the generative AI. Next, the Home Search System allows the generative AI to generate optimal property information based on the user's desired conditions and lifestyle. The generative AI refers to a property information database and extracts properties that match the user's conditions. For example, it extracts properties that match the conditions "within a 10-minute walk from the station," "south-facing," and "budget under 5 million yen." Furthermore, the Home Search System allows the generative AI to suggest ways to relax the user's conditions. For example, by relaxing the requirement from "within a 10-minute walk from the station" to "within an 11-minute walk," the number of eligible properties increases. Also, by changing the requirement from "south-facing" to "southwest-facing" or "southeast-facing," the number of eligible properties can be increased even with a lower rent. Finally, the home search system uses a generating AI to provide users with the most suitable property information. Users can review the property information suggested by the generating AI and select properties that meet their criteria. Furthermore, when a new property matching the criteria is registered, the generating AI notifies the user through channels such as messaging apps. This system supports users in efficiently searching for their ideal home and finding the most suitable property within their budget. For example, relaxing the requirement from "within a 10-minute walk from the station" to "within an 11-minute walk" increases the number of eligible properties, giving users more options. Also, by including "southwest-facing" and "southeast-facing" in addition to "south-facing," the number of eligible properties can be increased even with a lower rent. This allows users to find the best property within their budget, increasing their satisfaction.This allows the home search system to provide optimal property information based on the user's desired conditions and lifestyle.

[0029] The home search system according to this embodiment comprises a hearing unit, a generation unit, a proposal unit, and a provision unit. The hearing unit conducts detailed interviews about the user's desired conditions and lifestyle. For example, when the user inputs their desired conditions for buying or renting a home, the hearing unit uses a conversational AI agent to ask detailed questions. For example, the hearing unit can ask the user questions such as, "How many minutes' walk from the station do you want?" or "What is your budget?" The hearing unit also asks questions about the user's lifestyle. For example, the hearing unit can ask questions such as, "Do you have pets?" or "Do you have children?" The generation unit generates optimal property information based on the information gathered by the hearing unit. For example, the generation unit uses a generation AI to extract properties that match the user's desired conditions and lifestyle. The generation unit refers to a property information database and searches for properties that match the user's conditions. For example, the generation unit can extract properties that meet the conditions "within a 10-minute walk from the station," "south-facing," and "budget under 5 million yen." The suggestion unit makes suggestions to relax the conditions based on the property information generated by the generation unit. For example, the suggestion unit may suggest increasing the number of target properties by relaxing the user's conditions. For example, the suggestion unit may suggest relaxing "within a 10-minute walk from the station" to "within an 11-minute walk." The suggestion unit may also suggest changing "south-facing" to "southwest-facing" or "southeast-facing." The provision unit provides the user with the property information suggested by the suggestion unit. For example, the provision unit may notify the user of the property information suggested by the generation AI. The provision unit can provide property information to the user through channels such as messaging apps. For example, the provision unit can notify the user via a messaging app when a new property matching the user's desired conditions is registered. As a result, the home search system according to this embodiment can provide optimal property information based on the user's desired conditions and lifestyle.

[0030] The interviewing unit conducts detailed interviews to understand the user's desired conditions and lifestyle. For example, when a user enters their desired conditions for buying or renting a home, the interviewing unit uses a conversational AI agent to ask detailed questions. Specifically, the interviewing unit can ask the user questions such as, "How many minutes' walk from the station do you want?" or "What is your budget?" This allows the unit to accurately understand the user's specific desired conditions. The interviewing unit also asks questions about the user's lifestyle. For example, by asking questions such as, "Do you have pets?" or "Do you have children?", the unit gathers information to suggest properties that suit the user's lifestyle. Furthermore, the interviewing unit can ask additional questions based on the user's answers. For example, if the user answers, "I have pets," the interviewing unit will ask detailed questions such as, "What kind and how big are your pets?" to gather information to suggest properties suitable for pets. The interviewing unit also analyzes the user's answers in real time and provides information to the generation unit to suggest the most suitable properties based on the user's desired conditions and lifestyle. This allows the interviewing department to thoroughly understand the user's desired conditions and lifestyle, and collect basic information to propose the most suitable properties. Furthermore, the interviewing department can store the user's responses in a database and use them for future property proposals and user support. As a result, the interviewing department can efficiently collect information to propose the most suitable properties based on the user's desired conditions and lifestyle, thereby improving the overall performance of the system.

[0031] The generation unit generates optimal property information based on the information gathered by the interviewing unit. For example, the generation unit uses generation AI to extract properties that match the user's desired conditions and lifestyle. Specifically, the generation unit refers to a property information database and searches for properties that match the user's conditions. For example, the generation unit can extract properties that meet the conditions of "within a 10-minute walk from the station," "south-facing," and "budget of 5 million yen or less." The generation AI analyzes the user's desired conditions and uses an algorithm to select the optimal property from the property information database. For example, the generation AI selects the optimal property by weighting the user's desired conditions and calculating a score for each property. The generation unit also considers the surrounding environment and facility information of the property based on the user's lifestyle. For example, if the user answers that they "own a pet," the generation unit will prioritize extracting properties that are close to parks or veterinary clinics suitable for pets. Furthermore, if the generation unit cannot find a property that matches the user's desired conditions, it can suggest relaxing the conditions. For example, relaxing the condition from "within a 10-minute walk from the station" to "within a 15-minute walk" can increase the number of eligible properties. This allows the generation unit to generate optimal property information based on the user's desired conditions and lifestyle and provide it to the proposal unit. Furthermore, the generation unit can store the generated property information in a database and utilize it for future property proposals and user support. This allows the generation unit to efficiently generate optimal property information based on the user's desired conditions and lifestyle, improving the overall performance of the system.

[0032] The proposal unit makes suggestions to relax the conditions based on the property information generated by the generation unit. For example, the proposal unit may suggest increasing the number of available properties by relaxing the user's conditions. Specifically, the proposal unit can suggest relaxing the condition from "within 10 minutes' walk from the station" to "within 11 minutes' walk." It can also suggest changing "south-facing" to "southwest-facing" or "southeast-facing." The proposal unit uses an algorithm to suggest the optimal relaxation conditions based on the user's desired conditions. For example, the proposal unit analyzes the user's desired conditions and calculates how much the number of available properties will increase with the relaxation conditions. This allows the proposal unit to suggest the optimal relaxation conditions to the user. The proposal unit can also suggest relaxation conditions based on the user's lifestyle. For example, if the user answers that they "own a pet," the proposal unit will suggest relaxation conditions to prioritize properties suitable for pets. Furthermore, the proposal unit can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, it can analyze how the user reacted to the suggested relaxation conditions and reflect this in future suggestions. This allows the proposal department to suggest optimal relaxation conditions based on the user's desired conditions and lifestyle, thereby increasing the number of available properties. Furthermore, the proposal department can save the suggested relaxation conditions in a database and utilize them for future property suggestions and user support. As a result, the proposal department can efficiently suggest optimal relaxation conditions based on the user's desired conditions and lifestyle, improving the overall performance of the system.

[0033] The provisioning department provides users with property information proposed by the suggestion department. For example, the provisioning department notifies users of property information proposed by the generation AI. Specifically, the provisioning department can provide users with property information through channels such as messaging apps. For example, the provisioning department can notify users via messaging apps when a new property matching the user's desired conditions is registered. The provisioning department uses algorithms to provide optimal property information based on the user's desired conditions and lifestyle. For example, the provisioning department analyzes the user's desired conditions and selects the most suitable property information. The provisioning department also considers the surrounding environment and facility information of the property based on the user's lifestyle. For example, if a user answers that they "own a pet," the provisioning department will prioritize providing properties that are close to pet-friendly parks or veterinary clinics. Furthermore, the provisioning department can collect user feedback and continuously improve the accuracy and effectiveness of the provided content. For example, it can analyze how users reacted to the provided property information and reflect this in the next provision. In this way, the provisioning department can provide optimal property information based on the user's desired conditions and lifestyle, thereby improving user satisfaction. Furthermore, the service provider can store the provided property information in a database and utilize it for future property recommendations and user support. This allows the service provider to efficiently provide optimal property information based on the user's desired conditions and lifestyle, thereby improving the overall system performance.

[0034] The reception desk can receive user input. For example, when a user enters their desired conditions for buying or renting a home, the reception desk can provide methods such as text input, voice input, and selection from a list of options. For example, the reception desk can accept a user's text input of "within a 10-minute walk from the station." It can also accept a user's voice input of "facing south." Furthermore, the reception desk can accept a user's selection of "budget under 5 million yen" from a list of options. This allows the reception desk to conduct detailed interviews by receiving user input. 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 voice input into a generating AI and have the generating AI perform the conversion from voice data to text data.

[0035] The notification unit can notify the user. For example, the notification unit can provide methods such as email notifications, push notifications, and SMS notifications to quickly inform the user of suggestions to relax the conditions. For example, the notification unit can notify the user by email that "By relaxing the condition from within a 10-minute walk from the station to within an 11-minute walk, the number of eligible properties will increase." The notification unit can also notify the user by push notification that "By changing the orientation from south-facing to southwest-facing or southeast-facing, you can increase the number of eligible properties even while lowering the rent." Furthermore, the notification unit can notify the user by SMS notification that "By increasing the budget from 5 million yen to 5.5 million yen, you can consider more properties." In this way, the notification unit can quickly inform the user of suggestions to relax the conditions by notifying them. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input the user's suggestion to relax the conditions into a generating AI, the generating AI can generate the suggestion content, and the notification unit can notify the user.

[0036] The generation unit can refer to a property information database and extract properties that match the user's criteria. For example, the generation unit can search the property information database and extract properties that match the user's desired conditions. For example, the generation unit can extract properties that meet the conditions of "within a 10-minute walk from the station," "south-facing," and "budget of 5 million yen or less." The generation unit can also extract the latest property information based on the update frequency of the property information database. For example, if the property information database is updated daily, the generation unit will extract the latest property information. In this way, the generation unit can extract properties that match the user's criteria by referring to the property information database. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the property information database into a generation AI, and the generation AI can extract properties that match the user's criteria.

[0037] The proposal department can make suggestions to relax the user's conditions. For example, the proposal department can make suggestions to increase the number of eligible properties by relaxing the user's conditions. For example, the proposal department can make suggestions to relax the condition from "within 10 minutes' walk from the station" to "within 11 minutes' walk." The proposal department can also make suggestions to change "south-facing" to "southwest-facing" or "southeast-facing." Furthermore, the proposal department can make suggestions to "increase the budget from 5 million yen to 5.5 million yen." In this way, the proposal department can increase the number of eligible properties by making suggestions to relax the user's conditions. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input the user's conditions into a generating AI, and the generating AI can generate suggestions to relax the conditions.

[0038] The service provider can provide users with the most suitable property information. For example, the service provider can notify users of property information suggested by a generation AI. The service provider can provide users with property information through channels such as messaging apps. For example, the service provider can notify users via a messaging app when a new property matching the user's desired conditions is registered. The service provider can also notify users via email when a property matching the user's desired conditions is found. Furthermore, the service provider can notify users within the app when a property matching the user's desired conditions is found. In this way, the service provider can improve user satisfaction by providing users with the most suitable property information. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can notify users of property information suggested by a generation AI.

[0039] The interviewing unit can analyze the user's past interview history and select the optimal interviewing method. For example, the interviewing unit may prioritize using question formats that the user has preferred in the past. For example, the interviewing unit may refer to the types of questions that the user found easier to answer in the past. The interviewing unit can also conduct interviews at specific time periods based on the user's past interview history. For example, the interviewing unit may conduct interviews at times when the user was most relaxed in the past. In this way, the interviewing unit can select the optimal interviewing method by analyzing the user's past interview history. Some or all of the above processing in the interviewing unit may be performed using AI, for example, or not using AI. For example, the interviewing unit can input the user's past interview history data into a generating AI, which can then select the optimal interviewing method.

[0040] The interviewing unit can customize the questions asked during the interview based on the user's current living situation and areas of interest. For example, if the user is raising children, the interviewing unit will ask questions about a suitable environment for children. For example, if the user has pets, the interviewing unit will ask questions about properties suitable for pets. Also, if the user has hobbies, the interviewing unit can ask questions about properties near facilities related to those hobbies. In this way, the interviewing unit can collect more appropriate information by customizing the questions based on the user's current living situation and areas of interest. Some or all of the above processing in the interviewing unit may be performed using AI, for example, or not using AI. For example, the interviewing unit can input the user's living situation data into a generating AI, which can then customize the questions.

[0041] The interviewing unit can prioritize asking highly relevant questions during the interview, taking into account the user's geographical location. For example, if the user lives in a specific region, the interviewing unit will ask questions related to that region. For example, if the user is interested in a specific region, the interviewing unit will ask questions related to that region. Furthermore, if the user plans to move to a specific region, the interviewing unit can also ask questions related to that region. In this way, the interviewing unit can prioritize asking highly relevant questions by taking into account the user's geographical location. Some or all of the above processing in the interviewing unit may be performed using AI, for example, or not using AI. For example, the interviewing unit can input the user's geographical location information into a generating AI, which can then generate highly relevant questions.

[0042] The interviewing unit can analyze the user's social media activity during the interview and ask relevant questions. For example, the interviewing unit can ask questions about properties the user is interested in based on information the user has shared on social media. For example, the interviewing unit can ask questions about relevant properties based on information about accounts the user follows on social media. The interviewing unit can also ask questions about relevant properties based on information about groups the user participates in on social media. In this way, the interviewing unit can ask relevant questions by analyzing the user's social media activity. Some or all of the above processing in the interviewing unit may be performed using AI, for example, or not using AI. For example, the interviewing unit can input the user's social media activity data into a generating AI, which can then generate relevant questions.

[0043] The generation unit can adjust the level of detail in the generated information based on the importance of the property. For example, the generation unit provides detailed information for important properties. For example, it provides concise information for properties of low importance. It can also provide information of a moderate level of detail for properties of moderate importance. In this way, the generation unit can provide more appropriate information by adjusting the level of detail in the generated information based on the importance of the property. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input property importance data into a generation AI, and the generation AI can adjust the level of detail in the generated information.

[0044] The generation unit can apply different generation algorithms depending on the property category during generation. For example, for rental properties, the generation unit applies a generation algorithm specifically for rentals. For example, for purchase properties, the generation unit applies a generation algorithm specifically for purchases. Furthermore, the generation unit can also apply a generation algorithm specifically for commercial properties. In this way, the generation unit can provide more appropriate information by applying different generation algorithms depending on the property category. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input property category data into a generation AI, and the generation AI can apply different generation algorithms.

[0045] The generation unit can determine the generation priority based on the property registration date during generation. For example, the generation unit will prioritize the generation of newly registered properties. For example, the generation unit will lower the priority of properties that have been registered for a certain period of time. The generation unit can also adjust the generation priority according to the registration date. This allows the generation unit to provide more appropriate information by determining the generation priority based on the property registration date. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input property registration date data into a generation AI, and the generation AI can determine the generation priority.

[0046] The generation unit can adjust the generation order based on the relevance of properties during the generation process. For example, the generation unit may prioritize generating properties that are most relevant to the user's desired conditions. For example, the generation unit may postpone the generation of properties with low relevance. The generation unit can also adjust the generation order according to relevance. In this way, the generation unit can provide more appropriate information by adjusting the generation order based on the relevance of properties. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input property relevance data into a generation AI, and the generation AI can adjust the generation order.

[0047] The proposal unit can adjust the level of detail of its proposals based on the importance of the conditions. For example, the proposal unit will provide detailed proposals for important conditions. For example, it will provide concise proposals for less important conditions. It can also provide proposals with a moderate level of detail for conditions of moderate importance. In this way, the proposal unit can provide more appropriate proposals by adjusting the level of detail of its proposals based on the importance of the conditions. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal unit can input condition importance data into a generative AI, and the generative AI can adjust the level of detail of the proposals.

[0048] The proposal unit can apply different proposal algorithms depending on the category of conditions when making a proposal. For example, for rental properties, the proposal unit applies a proposal algorithm specifically for rentals. For example, for properties to be purchased, the proposal unit applies a proposal algorithm specifically for purchases. Furthermore, the proposal unit can also apply a proposal algorithm specifically for commercial properties. In this way, the proposal unit can make more appropriate proposals by applying different proposal algorithms depending on the category of conditions. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal unit can input the category data of conditions into a generative AI, and the generative AI can apply different proposal algorithms.

[0049] The proposal department can determine the priority of proposals based on the submission timing of the conditions. For example, the proposal department will prioritize newly submitted conditions. For example, the proposal department will lower the priority of conditions that have been submitted for a certain period of time. The proposal department can also adjust the priority of proposals according to the submission timing. This allows the proposal department to make more appropriate proposals by determining the priority of proposals based on the submission timing of the conditions. Some or all of the above processing in the proposal department may be performed using, for example, a generation AI, or not using a generation AI. For example, the proposal department can input the submission timing data of the conditions into a generation AI, and the generation AI can determine the priority of proposals.

[0050] The proposal unit can adjust the order of proposals based on the relevance of the conditions when making a proposal. For example, the proposal unit will prioritize proposals that are most relevant to the user's desired conditions. For example, the proposal unit will postpone proposals with low relevance. The proposal unit can also adjust the order of proposals according to their relevance. In this way, the proposal unit can make more appropriate proposals by adjusting the order of proposals based on the relevance of the conditions. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal unit can input condition relevance data into a generative AI, and the generative AI can adjust the order of proposals.

[0051] The service provider can select the optimal display method by referring to the user's past selection history when providing information. For example, the service provider may refer to the display methods of properties previously selected by the user. For example, the service provider may infer the user's preferred display method from their past selection history. The service provider can also suggest the optimal display method based on the user's past selection history. In this way, the service provider can select the optimal display method by referring to the user's past selection history. Some or all of the above processing in the service provider may be performed using, for example, a generation AI, or without a generation AI. For example, the service provider can input the user's past selection history data into a generation AI, which can then select the optimal display method.

[0052] The information provider can customize the displayed content based on the user's current living situation at the time of delivery. For example, if the user is raising children, the information provider will prioritize displaying information about environments suitable for children. For example, if the user has pets, the information provider will prioritize displaying information about properties suitable for pets. Furthermore, if the user has hobbies, the information provider can prioritize displaying information about properties near facilities related to those hobbies. In this way, the information provider can provide more appropriate information by customizing the displayed content based on the user's current living situation. Some or all of the above processing in the information provider may be performed using, for example, a generating AI, or without using a generating AI. For example, the information provider can input the user's living situation data into a generating AI, which can then customize the displayed content.

[0053] The service provider can select the optimal display method at the time of delivery, taking into account the user's geographical location information. For example, if the user lives in a specific area, the service provider will prioritize displaying property information related to that area. For example, if the user is interested in a specific area, the service provider will prioritize displaying property information related to that area. Furthermore, if the user plans to move to a specific area, the service provider can prioritize displaying property information related to that area. In this way, the service provider can select the optimal display method by taking into account the user's geographical location information. Some or all of the above processing in the service provider may be performed using, for example, a generating AI, or without using a generating AI. For example, the service provider can input the user's geographical location information data into a generating AI, and the generating AI can select the optimal display method.

[0054] The service provider can analyze the user's social media activity and adjust the displayed content at the time of delivery. For example, the service provider can prioritize displaying information about properties the user is interested in based on information shared on social media. For example, the service provider can prioritize displaying information about related properties based on information about accounts the user follows on social media. The service provider can also prioritize displaying information about related properties based on information about groups the user participates in on social media. In this way, the service provider can provide more appropriate information by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input the user's social media activity data into a generative AI, which can then adjust the displayed content.

[0055] The reception department can analyze the user's past reception history and select the optimal reception method at the time of reception. For example, the reception department may prioritize suggesting reception methods that the user has frequently used in the past. For example, the reception department may predict and suggest a reception method to be used during a specific time period based on the user's past reception history. The reception department can also suggest the optimal reception method based on the user's past reception history. In this way, the reception department can select the optimal reception method by analyzing the user's past reception history. Some or all of the above processing in the reception department may be performed using, for example, a generative AI, or without a generative AI. For example, the reception department can input the user's past reception history data into a generative AI, which can then select the optimal reception method.

[0056] The reception desk can prioritize highly relevant requests by considering the user's geographical location information during the reception process. For example, if the user lives in a specific region, the reception desk will prioritize requests related to that region. For example, if the user is interested in a specific region, the reception desk will prioritize requests related to that region. Furthermore, if the user plans to move to a specific region, the reception desk can also prioritize requests related to that region. In this way, the reception desk can prioritize highly relevant requests by considering the user's geographical location information. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or without a generative AI. For example, the reception desk can input the user's geographical location data into a generative AI, which can then prioritize highly relevant requests.

[0057] The notification unit can analyze the user's past notification history and select the optimal notification method when sending a notification. For example, the notification unit may prioritize using notification methods that the user has previously preferred. For example, the notification unit may send notifications at specific time periods based on the user's past notification history. The notification unit can also suggest the optimal notification method based on the user's past notification history. In this way, the notification unit can select the optimal notification method by analyzing the user's past notification history. Some or all of the above processing in the notification unit may be performed using, for example, a generative AI, or without a generative AI. For example, the notification unit can input the user's past notification history data into a generative AI, which can then select the optimal notification method.

[0058] The notification unit can prioritize highly relevant notifications by considering the user's geographical location information when sending notifications. For example, if the user lives in a specific region, the notification unit will prioritize notifications related to that region. For example, if the user is interested in a specific region, the notification unit will prioritize notifications related to that region. Furthermore, if the user plans to move to a specific region, the notification unit can also prioritize notifications related to that region. In this way, the notification unit can prioritize highly relevant notifications by considering the user's geographical location information. Some or all of the above processing in the notification unit may be performed using, for example, a generative AI, or without a generative AI. For example, the notification unit can input the user's geographical location information data into a generative AI, which can then prioritize highly relevant notifications.

[0059] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0060] The home search system can also include a history analysis unit that analyzes the user's past search history. The history analysis unit analyzes the properties and conditions the user has searched for in the past to understand the user's preferences and tendencies. For example, the history analysis unit can extract the characteristics of properties the user has searched for in the past and preferentially suggest similar properties. The history analysis unit can also suggest relaxing the conditions based on the conditions the user has searched for in the past. Furthermore, the history analysis unit can refer to the ratings of properties the user has searched for in the past and preferentially suggest properties with high ratings. In this way, the history analysis unit can provide more appropriate property information by analyzing the user's past search history.

[0061] The home search system can also include a lifestyle analysis unit that understands the user's current living situation. This unit collects information such as the user's family structure, occupation, and hobbies, and then suggests properties that are best suited to the user. For example, if the user is raising children, the lifestyle analysis unit can prioritize suggesting properties with a child-friendly environment. If the user has pets, it can also suggest pet-friendly properties. Furthermore, if the user has hobbies, the lifestyle analysis unit can suggest properties near facilities related to those hobbies. In this way, the lifestyle analysis unit can provide more appropriate property information by understanding the user's current living situation.

[0062] The home search system may further include a geographic information analysis unit that provides property information while considering the user's geographic location. The geographic information analysis unit prioritizes providing property information relevant to a specific region if the user lives in that region. For example, if the user is interested in a particular region, the geographic information analysis unit can provide property information relevant to that region. Furthermore, if the user plans to move to a particular region, the geographic information analysis unit can provide property information relevant to that region. This allows the geographic information analysis unit to provide more appropriate property information by considering the user's geographic location. Some or all of the above processing in the geographic information analysis unit may be performed using, for example, AI, or without AI. For example, the geographic information analysis unit can input the user's geographic location data into a generating AI, which can then provide highly relevant property information.

[0063] The home search system may further include a social media analysis unit that analyzes the user's social media activity to provide property information. The social media analysis unit provides information about properties of interest based on information shared by the user on social media. For example, the social media analysis unit can provide information about relevant properties based on information about accounts the user follows on social media. It can also provide information about relevant properties based on information about groups the user participates in on social media. In this way, the social media analysis unit can provide more appropriate property information by analyzing the user's social media activity. Some or all of the above processing in the social media analysis unit may be performed using AI, for example, or not using AI. For example, the social media analysis unit can input the user's social media activity data into a generating AI, which can then provide relevant property information.

[0064] The home search system may further include a selection history analysis unit that provides property information by referring to the user's past selection history. The selection history analysis unit analyzes information on properties the user has previously selected to understand the user's preferences and tendencies. For example, the selection history analysis unit can extract the characteristics of properties the user has previously selected and preferentially provide similar properties. The selection history analysis unit can also suggest relaxing the conditions based on the conditions the user has previously selected. Furthermore, the selection history analysis unit can refer to the ratings of properties the user has previously selected and preferentially provide properties with high ratings. In this way, the selection history analysis unit can provide more appropriate property information by referring to the user's past selection history. Some or all of the above processing in the selection history analysis unit may be performed using AI, for example, or without AI. For example, the selection history analysis unit can input the user's past selection history data into a generating AI, and the generating AI can provide property information.

[0065] The home search system may also include a hearing history analysis unit that analyzes the user's past hearing history and selects the optimal hearing method. The hearing history analysis unit prioritizes using question formats that the user has preferred in the past. For example, it may refer to questions that the user found easy to answer in the past. The hearing history analysis unit can also conduct hearings at specific time periods based on the user's past hearing history. For example, it may conduct hearings at times when the user was most relaxed in the past. In this way, the hearing history analysis unit can select the optimal hearing method by analyzing the user's past hearing history. Some or all of the above processing in the hearing history analysis unit may be performed using AI, for example, or without AI. For example, the hearing history analysis unit can input the user's past hearing history data into a generating AI, which can then select the optimal hearing method.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The interviewing department conducts detailed interviews to understand the user's desired conditions and lifestyle. For example, when a user enters their desired conditions for buying or renting a home, a conversational AI agent is used to ask detailed questions. Specifically, questions such as "How many minutes' walk from the station do you prefer?", "What is your budget?", "Do you have pets?", and "Do you have children?" can be asked. Step 2: The generation unit generates optimal property information based on the information gathered by the interviewing unit. The generation unit uses a generation AI to extract properties that match the user's desired conditions and lifestyle. For example, it searches for properties that meet the conditions of "within a 10-minute walk from the station," "south-facing," and "budget under 5 million yen." Step 3: The proposal unit makes suggestions to relax the conditions based on the property information generated by the generation unit. The proposal unit makes suggestions to increase the number of target properties by relaxing the user's conditions. For example, it can make suggestions to relax "within 10 minutes' walk from the station" to "within 11 minutes' walk," or to change "south-facing" to "southwest-facing" or "southeast-facing." Step 4: The provisioning unit provides the user with the property information proposed by the suggestioning unit. The provisioning unit notifies the user of the property information proposed by the generation AI. For example, the property information can be provided to the user through channels such as messaging apps.

[0068] (Example of form 2) The Home Search System according to an embodiment of the present invention is a system that uses a conversational AI agent to conduct detailed interviews with users considering purchasing or renting a home, and utilizes a generative AI to provide personalized property recommendations. The Home Search System provides a supportive experience that allows users to relax their conditions based on their priorities, helping them find the optimal home. For example, the Home Search System allows users to input their desired conditions for purchasing or renting a home into the conversational AI agent. For example, they might input conditions such as "within a 10-minute walk from the station," "south-facing," and "budget under 5 million yen." The conversational AI agent conducts detailed interviews with the user about their desired conditions and lifestyle and provides this information to the generative AI. Next, the Home Search System allows the generative AI to generate optimal property information based on the user's desired conditions and lifestyle. The generative AI refers to a property information database and extracts properties that match the user's conditions. For example, it extracts properties that match the conditions "within a 10-minute walk from the station," "south-facing," and "budget under 5 million yen." Furthermore, the Home Search System allows the generative AI to suggest ways to relax the user's conditions. For example, by relaxing the requirement from "within a 10-minute walk from the station" to "within an 11-minute walk," the number of eligible properties increases. Also, by changing the requirement from "south-facing" to "southwest-facing" or "southeast-facing," the number of eligible properties can be increased even with a lower rent. Finally, the home search system uses a generating AI to provide users with the most suitable property information. Users can review the property information suggested by the generating AI and select properties that meet their criteria. Furthermore, when a new property matching the criteria is registered, the generating AI notifies the user through channels such as messaging apps. This system supports users in efficiently searching for their ideal home and finding the most suitable property within their budget. For example, relaxing the requirement from "within a 10-minute walk from the station" to "within an 11-minute walk" increases the number of eligible properties, giving users more options. Also, by including "southwest-facing" and "southeast-facing" in addition to "south-facing," the number of eligible properties can be increased even with a lower rent. This allows users to find the best property within their budget, increasing their satisfaction.This allows the home search system to provide optimal property information based on the user's desired conditions and lifestyle.

[0069] The home search system according to this embodiment comprises a hearing unit, a generation unit, a proposal unit, and a provision unit. The hearing unit conducts detailed interviews about the user's desired conditions and lifestyle. For example, when the user inputs their desired conditions for buying or renting a home, the hearing unit uses a conversational AI agent to ask detailed questions. For example, the hearing unit can ask the user questions such as, "How many minutes' walk from the station do you want?" or "What is your budget?" The hearing unit also asks questions about the user's lifestyle. For example, the hearing unit can ask questions such as, "Do you have pets?" or "Do you have children?" The generation unit generates optimal property information based on the information gathered by the hearing unit. For example, the generation unit uses a generation AI to extract properties that match the user's desired conditions and lifestyle. The generation unit refers to a property information database and searches for properties that match the user's conditions. For example, the generation unit can extract properties that meet the conditions "within a 10-minute walk from the station," "south-facing," and "budget under 5 million yen." The suggestion unit makes suggestions to relax the conditions based on the property information generated by the generation unit. For example, the suggestion unit may suggest increasing the number of target properties by relaxing the user's conditions. For example, the suggestion unit may suggest relaxing "within a 10-minute walk from the station" to "within an 11-minute walk." The suggestion unit may also suggest changing "south-facing" to "southwest-facing" or "southeast-facing." The provision unit provides the user with the property information suggested by the suggestion unit. For example, the provision unit may notify the user of the property information suggested by the generation AI. The provision unit can provide property information to the user through channels such as messaging apps. For example, the provision unit can notify the user via a messaging app when a new property matching the user's desired conditions is registered. As a result, the home search system according to this embodiment can provide optimal property information based on the user's desired conditions and lifestyle.

[0070] The interviewing unit conducts detailed interviews to understand the user's desired conditions and lifestyle. For example, when a user enters their desired conditions for buying or renting a home, the interviewing unit uses a conversational AI agent to ask detailed questions. Specifically, the interviewing unit can ask the user questions such as, "How many minutes' walk from the station do you want?" or "What is your budget?" This allows the unit to accurately understand the user's specific desired conditions. The interviewing unit also asks questions about the user's lifestyle. For example, by asking questions such as, "Do you have pets?" or "Do you have children?", the unit gathers information to suggest properties that suit the user's lifestyle. Furthermore, the interviewing unit can ask additional questions based on the user's answers. For example, if the user answers, "I have pets," the interviewing unit will ask detailed questions such as, "What kind and how big are your pets?" to gather information to suggest properties suitable for pets. The interviewing unit also analyzes the user's answers in real time and provides information to the generation unit to suggest the most suitable properties based on the user's desired conditions and lifestyle. This allows the interviewing department to thoroughly understand the user's desired conditions and lifestyle, and collect basic information to propose the most suitable properties. Furthermore, the interviewing department can store the user's responses in a database and use them for future property proposals and user support. As a result, the interviewing department can efficiently collect information to propose the most suitable properties based on the user's desired conditions and lifestyle, thereby improving the overall performance of the system.

[0071] The generation unit generates optimal property information based on the information gathered by the interviewing unit. For example, the generation unit uses generation AI to extract properties that match the user's desired conditions and lifestyle. Specifically, the generation unit refers to a property information database and searches for properties that match the user's conditions. For example, the generation unit can extract properties that meet the conditions of "within a 10-minute walk from the station," "south-facing," and "budget of 5 million yen or less." The generation AI analyzes the user's desired conditions and uses an algorithm to select the optimal property from the property information database. For example, the generation AI selects the optimal property by weighting the user's desired conditions and calculating a score for each property. The generation unit also considers the surrounding environment and facility information of the property based on the user's lifestyle. For example, if the user answers that they "own a pet," the generation unit will prioritize extracting properties that are close to parks or veterinary clinics suitable for pets. Furthermore, if the generation unit cannot find a property that matches the user's desired conditions, it can suggest relaxing the conditions. For example, relaxing the condition from "within a 10-minute walk from the station" to "within a 15-minute walk" can increase the number of eligible properties. This allows the generation unit to generate optimal property information based on the user's desired conditions and lifestyle and provide it to the proposal unit. Furthermore, the generation unit can store the generated property information in a database and utilize it for future property proposals and user support. This allows the generation unit to efficiently generate optimal property information based on the user's desired conditions and lifestyle, improving the overall performance of the system.

[0072] The proposal unit makes suggestions to relax the conditions based on the property information generated by the generation unit. For example, the proposal unit may suggest increasing the number of available properties by relaxing the user's conditions. Specifically, the proposal unit can suggest relaxing the condition from "within 10 minutes' walk from the station" to "within 11 minutes' walk." It can also suggest changing "south-facing" to "southwest-facing" or "southeast-facing." The proposal unit uses an algorithm to suggest the optimal relaxation conditions based on the user's desired conditions. For example, the proposal unit analyzes the user's desired conditions and calculates how much the number of available properties will increase with the relaxation conditions. This allows the proposal unit to suggest the optimal relaxation conditions to the user. The proposal unit can also suggest relaxation conditions based on the user's lifestyle. For example, if the user answers that they "own a pet," the proposal unit will suggest relaxation conditions to prioritize properties suitable for pets. Furthermore, the proposal unit can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, it can analyze how the user reacted to the suggested relaxation conditions and reflect this in future suggestions. This allows the proposal department to suggest optimal relaxation conditions based on the user's desired conditions and lifestyle, thereby increasing the number of available properties. Furthermore, the proposal department can save the suggested relaxation conditions in a database and utilize them for future property suggestions and user support. As a result, the proposal department can efficiently suggest optimal relaxation conditions based on the user's desired conditions and lifestyle, improving the overall performance of the system.

[0073] The provisioning department provides users with property information proposed by the suggestion department. For example, the provisioning department notifies users of property information proposed by the generation AI. Specifically, the provisioning department can provide users with property information through channels such as messaging apps. For example, the provisioning department can notify users via messaging apps when a new property matching the user's desired conditions is registered. The provisioning department uses algorithms to provide optimal property information based on the user's desired conditions and lifestyle. For example, the provisioning department analyzes the user's desired conditions and selects the most suitable property information. The provisioning department also considers the surrounding environment and facility information of the property based on the user's lifestyle. For example, if a user answers that they "own a pet," the provisioning department will prioritize providing properties that are close to pet-friendly parks or veterinary clinics. Furthermore, the provisioning department can collect user feedback and continuously improve the accuracy and effectiveness of the provided content. For example, it can analyze how users reacted to the provided property information and reflect this in the next provision. In this way, the provisioning department can provide optimal property information based on the user's desired conditions and lifestyle, thereby improving user satisfaction. Furthermore, the service provider can store the provided property information in a database and utilize it for future property recommendations and user support. This allows the service provider to efficiently provide optimal property information based on the user's desired conditions and lifestyle, thereby improving the overall system performance.

[0074] The reception desk can receive user input. For example, when a user enters their desired conditions for buying or renting a home, the reception desk can provide methods such as text input, voice input, and selection from a list of options. For example, the reception desk can accept a user's text input of "within a 10-minute walk from the station." It can also accept a user's voice input of "facing south." Furthermore, the reception desk can accept a user's selection of "budget under 5 million yen" from a list of options. This allows the reception desk to conduct detailed interviews by receiving user input. 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 voice input into a generating AI and have the generating AI perform the conversion from voice data to text data.

[0075] The notification unit can notify the user. For example, the notification unit can provide methods such as email notifications, push notifications, and SMS notifications to quickly inform the user of suggestions to relax the conditions. For example, the notification unit can notify the user by email that "By relaxing the condition from within a 10-minute walk from the station to within an 11-minute walk, the number of eligible properties will increase." The notification unit can also notify the user by push notification that "By changing the orientation from south-facing to southwest-facing or southeast-facing, you can increase the number of eligible properties even while lowering the rent." Furthermore, the notification unit can notify the user by SMS notification that "By increasing the budget from 5 million yen to 5.5 million yen, you can consider more properties." In this way, the notification unit can quickly inform the user of suggestions to relax the conditions by notifying them. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input the user's suggestion to relax the conditions into a generating AI, the generating AI can generate the suggestion content, and the notification unit can notify the user.

[0076] The generation unit can refer to a property information database and extract properties that match the user's criteria. For example, the generation unit can search the property information database and extract properties that match the user's desired conditions. For example, the generation unit can extract properties that meet the conditions of "within a 10-minute walk from the station," "south-facing," and "budget of 5 million yen or less." The generation unit can also extract the latest property information based on the update frequency of the property information database. For example, if the property information database is updated daily, the generation unit will extract the latest property information. In this way, the generation unit can extract properties that match the user's criteria by referring to the property information database. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the property information database into a generation AI, and the generation AI can extract properties that match the user's criteria.

[0077] The proposal department can make suggestions to relax the user's conditions. For example, the proposal department can make suggestions to increase the number of eligible properties by relaxing the user's conditions. For example, the proposal department can make suggestions to relax the condition from "within 10 minutes' walk from the station" to "within 11 minutes' walk." The proposal department can also make suggestions to change "south-facing" to "southwest-facing" or "southeast-facing." Furthermore, the proposal department can make suggestions to "increase the budget from 5 million yen to 5.5 million yen." In this way, the proposal department can increase the number of eligible properties by making suggestions to relax the user's conditions. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input the user's conditions into a generating AI, and the generating AI can generate suggestions to relax the conditions.

[0078] The service provider can provide users with the most suitable property information. For example, the service provider can notify users of property information suggested by a generation AI. The service provider can provide users with property information through channels such as messaging apps. For example, the service provider can notify users via a messaging app when a new property matching the user's desired conditions is registered. The service provider can also notify users via email when a property matching the user's desired conditions is found. Furthermore, the service provider can notify users within the app when a property matching the user's desired conditions is found. In this way, the service provider can improve user satisfaction by providing users with the most suitable property information. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can notify users of property information suggested by a generation AI.

[0079] The interviewing unit can estimate the user's emotions and adjust the timing and method of the interview based on the estimated emotions. For example, if the user is stressed, the interviewing unit may delay the interview and ask questions in a relaxed state. For example, if the user is relaxed, the interviewing unit may ask detailed questions and collect more information. Also, if the user is in a hurry, the interviewing unit may ask concise questions and collect information quickly. In this way, the interviewing unit can conduct more effective interviews by adjusting the timing and method of the interview based on 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 interviewing unit may be performed using AI, for example, or without AI. For example, the interviewing unit can input the user's facial expression data into a generating AI, which can then estimate the user's emotions and adjust the timing and method of the interview.

[0080] The interviewing unit can analyze the user's past interview history and select the optimal interviewing method. For example, the interviewing unit may prioritize using question formats that the user has preferred in the past. For example, the interviewing unit may refer to the types of questions that the user found easier to answer in the past. The interviewing unit can also conduct interviews at specific time periods based on the user's past interview history. For example, the interviewing unit may conduct interviews at times when the user was most relaxed in the past. In this way, the interviewing unit can select the optimal interviewing method by analyzing the user's past interview history. Some or all of the above processing in the interviewing unit may be performed using AI, for example, or not using AI. For example, the interviewing unit can input the user's past interview history data into a generating AI, which can then select the optimal interviewing method.

[0081] The interviewing unit can customize the questions asked during the interview based on the user's current living situation and areas of interest. For example, if the user is raising children, the interviewing unit will ask questions about a suitable environment for children. For example, if the user has pets, the interviewing unit will ask questions about properties suitable for pets. Also, if the user has hobbies, the interviewing unit can ask questions about properties near facilities related to those hobbies. In this way, the interviewing unit can collect more appropriate information by customizing the questions based on the user's current living situation and areas of interest. Some or all of the above processing in the interviewing unit may be performed using AI, for example, or not using AI. For example, the interviewing unit can input the user's living situation data into a generating AI, which can then customize the questions.

[0082] The interview unit can estimate the user's emotions and determine the priority of the interview based on the estimated emotions. For example, if the user is feeling anxious, the interview unit will prioritize asking questions that provide reassurance. For example, if the user is excited, the interview unit will prioritize asking questions that help the user think calmly. Also, if the user is tired, the interview unit can prioritize asking simple questions. In this way, the interview unit can conduct more effective interviews by determining the priority of the interview based on 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 interview unit may be performed using AI, or not using AI. For example, the interview unit can input user facial expression data into a generative AI, which can estimate the user's emotions and determine the priority of the interview.

[0083] The interviewing unit can prioritize asking highly relevant questions during the interview, taking into account the user's geographical location. For example, if the user lives in a specific region, the interviewing unit will ask questions related to that region. For example, if the user is interested in a specific region, the interviewing unit will ask questions related to that region. Furthermore, if the user plans to move to a specific region, the interviewing unit can also ask questions related to that region. In this way, the interviewing unit can prioritize asking highly relevant questions by taking into account the user's geographical location. Some or all of the above processing in the interviewing unit may be performed using AI, for example, or not using AI. For example, the interviewing unit can input the user's geographical location information into a generating AI, which can then generate highly relevant questions.

[0084] The interviewing unit can analyze the user's social media activity during the interview and ask relevant questions. For example, the interviewing unit can ask questions about properties the user is interested in based on information the user has shared on social media. For example, the interviewing unit can ask questions about relevant properties based on information about accounts the user follows on social media. The interviewing unit can also ask questions about relevant properties based on information about groups the user participates in on social media. In this way, the interviewing unit can ask relevant questions by analyzing the user's social media activity. Some or all of the above processing in the interviewing unit may be performed using AI, for example, or not using AI. For example, the interviewing unit can input the user's social media activity data into a generating AI, which can then generate relevant questions.

[0085] The generation unit can estimate the user's emotions and adjust the way the property information is presented based on the estimated emotions. For example, if the user is relaxed, the generation unit can provide detailed property information. For example, if the user is in a hurry, the generation unit can provide concise property information. The generation unit can also provide visually appealing property information if the user is excited. In this way, the generation unit can provide more appropriate information by adjusting the way the property information is presented based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not using AI. For example, the generation unit can input user emotion data into the generation AI, which can then adjust the way the property information is presented.

[0086] The generation unit can adjust the level of detail in the generated information based on the importance of the property. For example, the generation unit provides detailed information for important properties. For example, it provides concise information for properties of low importance. It can also provide information of a moderate level of detail for properties of moderate importance. In this way, the generation unit can provide more appropriate information by adjusting the level of detail in the generated information based on the importance of the property. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input property importance data into a generation AI, and the generation AI can adjust the level of detail in the generated information.

[0087] The generation unit can apply different generation algorithms depending on the property category during generation. For example, for rental properties, the generation unit applies a generation algorithm specifically for rentals. For example, for purchase properties, the generation unit applies a generation algorithm specifically for purchases. Furthermore, the generation unit can also apply a generation algorithm specifically for commercial properties. In this way, the generation unit can provide more appropriate information by applying different generation algorithms depending on the property category. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input property category data into a generation AI, and the generation AI can apply different generation algorithms.

[0088] The generation unit can estimate the user's emotions and adjust the length of the property information it generates based on the estimated emotions. For example, if the user is relaxed, the generation unit will provide detailed property information. For example, if the user is in a hurry, the generation unit will provide concise property information. The generation unit can also provide visually appealing property information if the user is excited. In this way, the generation unit can provide more appropriate information by adjusting the length of the property information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not using AI. For example, the generation unit can input user emotion data into the generation AI, which can then adjust the length of the property information.

[0089] The generation unit can determine the generation priority based on the property registration date during generation. For example, the generation unit will prioritize the generation of newly registered properties. For example, the generation unit will lower the priority of properties that have been registered for a certain period of time. The generation unit can also adjust the generation priority according to the registration date. This allows the generation unit to provide more appropriate information by determining the generation priority based on the property registration date. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input property registration date data into a generation AI, and the generation AI can determine the generation priority.

[0090] The generation unit can adjust the generation order based on the relevance of properties during the generation process. For example, the generation unit may prioritize generating properties that are most relevant to the user's desired conditions. For example, the generation unit may postpone the generation of properties with low relevance. The generation unit can also adjust the generation order according to relevance. In this way, the generation unit can provide more appropriate information by adjusting the generation order based on the relevance of properties. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input property relevance data into a generation AI, and the generation AI can adjust the generation order.

[0091] The suggestion unit can estimate the user's emotions and adjust the way it presents its suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit will provide detailed suggestions. If the user is in a hurry, the suggestion unit will provide concise suggestions. Furthermore, if the user is excited, the suggestion unit may provide visually appealing suggestions. This allows the suggestion unit to provide more appropriate suggestions by adjusting the presentation based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI 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 suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI, which can then adjust the presentation of its suggestions.

[0092] The proposal unit can adjust the level of detail of its proposals based on the importance of the conditions. For example, the proposal unit will provide detailed proposals for important conditions. For example, it will provide concise proposals for less important conditions. It can also provide proposals with a moderate level of detail for conditions of moderate importance. In this way, the proposal unit can provide more appropriate proposals by adjusting the level of detail of its proposals based on the importance of the conditions. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal unit can input condition importance data into a generative AI, and the generative AI can adjust the level of detail of the proposals.

[0093] The proposal unit can apply different proposal algorithms depending on the category of conditions when making a proposal. For example, for rental properties, the proposal unit applies a proposal algorithm specifically for rentals. For example, for properties to be purchased, the proposal unit applies a proposal algorithm specifically for purchases. Furthermore, the proposal unit can also apply a proposal algorithm specifically for commercial properties. In this way, the proposal unit can make more appropriate proposals by applying different proposal algorithms depending on the category of conditions. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal unit can input the category data of conditions into a generative AI, and the generative AI can apply different proposal algorithms.

[0094] The suggestion unit can estimate the user's emotions and adjust the length of its suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit will provide detailed suggestions. For example, if the user is in a hurry, the suggestion unit will provide concise suggestions. The suggestion unit can also provide visually appealing suggestions if the user is excited. This allows the suggestion unit to provide more appropriate suggestions by adjusting the length of its suggestions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI, which can then adjust the length of its suggestions.

[0095] The proposal department can determine the priority of proposals based on the submission timing of the conditions. For example, the proposal department will prioritize newly submitted conditions. For example, the proposal department will lower the priority of conditions that have been submitted for a certain period of time. The proposal department can also adjust the priority of proposals according to the submission timing. This allows the proposal department to make more appropriate proposals by determining the priority of proposals based on the submission timing of the conditions. Some or all of the above processing in the proposal department may be performed using, for example, a generation AI, or not using a generation AI. For example, the proposal department can input the submission timing data of the conditions into a generation AI, and the generation AI can determine the priority of proposals.

[0096] The proposal unit can adjust the order of proposals based on the relevance of the conditions when making a proposal. For example, the proposal unit will prioritize proposals that are most relevant to the user's desired conditions. For example, the proposal unit will postpone proposals with low relevance. The proposal unit can also adjust the order of proposals according to their relevance. In this way, the proposal unit can make more appropriate proposals by adjusting the order of proposals based on the relevance of the conditions. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal unit can input condition relevance data into a generative AI, and the generative AI can adjust the order of proposals.

[0097] The service provider can estimate the user's emotions and adjust how property information is displayed based on the estimated emotions. For example, if the user is relaxed, the service provider can provide detailed property information. For example, if the user is in a hurry, the service provider can provide concise property information. The service provider can also provide visually appealing property information if the user is excited. In this way, the service provider can provide more appropriate information by adjusting how property information is displayed based on 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 emotion data into a generative AI, which can then adjust how property information is displayed.

[0098] The service provider can select the optimal display method by referring to the user's past selection history when providing information. For example, the service provider may refer to the display methods of properties previously selected by the user. For example, the service provider may infer the user's preferred display method from their past selection history. The service provider can also suggest the optimal display method based on the user's past selection history. In this way, the service provider can select the optimal display method by referring to the user's past selection history. Some or all of the above processing in the service provider may be performed using, for example, a generation AI, or without a generation AI. For example, the service provider can input the user's past selection history data into a generation AI, which can then select the optimal display method.

[0099] The information provider can customize the displayed content based on the user's current living situation at the time of delivery. For example, if the user is raising children, the information provider will prioritize displaying information about environments suitable for children. For example, if the user has pets, the information provider will prioritize displaying information about properties suitable for pets. Furthermore, if the user has hobbies, the information provider can prioritize displaying information about properties near facilities related to those hobbies. In this way, the information provider can provide more appropriate information by customizing the displayed content based on the user's current living situation. Some or all of the above processing in the information provider may be performed using, for example, a generating AI, or without using a generating AI. For example, the information provider can input the user's living situation data into a generating AI, which can then customize the displayed content.

[0100] The service provider can estimate the user's emotions and prioritize the property information to be provided based on the estimated emotions. For example, if the user is relaxed, the service provider will prioritize detailed property information. For example, if the user is in a hurry, the service provider will prioritize concise property information. The service provider can also prioritize visually appealing property information if the user is excited. In this way, the service provider can provide more appropriate information by prioritizing property information based on 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 emotion data into a generative AI, which can then determine the priority of property information.

[0101] The service provider can select the optimal display method at the time of delivery, taking into account the user's geographical location information. For example, if the user lives in a specific area, the service provider will prioritize displaying property information related to that area. For example, if the user is interested in a specific area, the service provider will prioritize displaying property information related to that area. Furthermore, if the user plans to move to a specific area, the service provider can prioritize displaying property information related to that area. In this way, the service provider can select the optimal display method by taking into account the user's geographical location information. Some or all of the above processing in the service provider may be performed using, for example, a generating AI, or without using a generating AI. For example, the service provider can input the user's geographical location information data into a generating AI, and the generating AI can select the optimal display method.

[0102] The service provider can analyze the user's social media activity and adjust the displayed content at the time of delivery. For example, the service provider can prioritize displaying information about properties the user is interested in based on information shared on social media. For example, the service provider can prioritize displaying information about related properties based on information about accounts the user follows on social media. The service provider can also prioritize displaying information about related properties based on information about groups the user participates in on social media. In this way, the service provider can provide more appropriate information by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input the user's social media activity data into a generative AI, which can then adjust the displayed content.

[0103] The reception desk can estimate the user's emotions and adjust the timing and method of reception based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. For example, if the user is relaxed, the reception desk can provide detailed input options and suggest a customizable input method. The reception desk can also prioritize voice input and process the call quickly if the user is in a hurry. This allows the reception desk to provide more appropriate reception by adjusting the timing and method of reception based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into a generative AI, which can then adjust the timing and method of reception.

[0104] The reception department can analyze the user's past reception history and select the optimal reception method at the time of reception. For example, the reception department may prioritize suggesting reception methods that the user has frequently used in the past. For example, the reception department may predict and suggest a reception method to be used during a specific time period based on the user's past reception history. The reception department can also suggest the optimal reception method based on the user's past reception history. In this way, the reception department can select the optimal reception method by analyzing the user's past reception history. Some or all of the above processing in the reception department may be performed using, for example, a generative AI, or without a generative AI. For example, the reception department can input the user's past reception history data into a generative AI, which can then select the optimal reception method.

[0105] The reception desk can estimate the user's emotions and determine the priority of the reception process based on the estimated emotions. For example, if the user is feeling anxious, the reception desk will prioritize a reassuring reception method. For example, if the user is excited, the reception desk will prioritize a reception method that allows for calm thinking. The reception desk can also prioritize a simple reception method if the user is tired. In this way, the reception desk can provide more appropriate reception by determining the priority of the reception process based on 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 can input user emotion data into a generative AI, which can then determine the priority of the reception process.

[0106] The reception desk can prioritize highly relevant requests by considering the user's geographical location information during the reception process. For example, if the user lives in a specific region, the reception desk will prioritize requests related to that region. For example, if the user is interested in a specific region, the reception desk will prioritize requests related to that region. Furthermore, if the user plans to move to a specific region, the reception desk can also prioritize requests related to that region. In this way, the reception desk can prioritize highly relevant requests by considering the user's geographical location information. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or without a generative AI. For example, the reception desk can input the user's geographical location data into a generative AI, which can then prioritize highly relevant requests.

[0107] The notification unit can estimate the user's emotions and adjust the timing and method of notifications based on the estimated emotions. For example, if the user is stressed, the notification unit may reduce the frequency of notifications and only send important information. For example, if the user is relaxed, the notification unit may provide detailed notifications and information. Also, if the user is in a hurry, the notification unit may provide concise notifications and information quickly. In this way, the notification unit can provide more appropriate notifications by adjusting the timing and method of notifications based on 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 notification unit may be performed using AI or not using AI. For example, the notification unit can input user emotion data into the generative AI, which can then adjust the timing and method of notifications.

[0108] The notification unit can analyze the user's past notification history and select the optimal notification method when sending a notification. For example, the notification unit may prioritize using notification methods that the user has previously preferred. For example, the notification unit may send notifications at specific time periods based on the user's past notification history. The notification unit can also suggest the optimal notification method based on the user's past notification history. In this way, the notification unit can select the optimal notification method by analyzing the user's past notification history. Some or all of the above processing in the notification unit may be performed using, for example, a generative AI, or without a generative AI. For example, the notification unit can input the user's past notification history data into a generative AI, which can then select the optimal notification method.

[0109] The notification unit can estimate the user's emotions and determine the priority of notifications based on the estimated emotions. For example, if the user is feeling anxious, the notification unit will prioritize reassuring notifications. For example, if the user is excited, the notification unit will prioritize notifications that help the user think calmly. The notification unit can also prioritize simple notifications if the user is tired. In this way, the notification unit can provide more appropriate notifications by determining the priority of notifications based on 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 notification unit may be performed using AI, or not using AI. For example, the notification unit can input user emotion data into a generative AI, and the generative AI can determine the priority of notifications.

[0110] The notification unit can prioritize highly relevant notifications by considering the user's geographical location information when sending notifications. For example, if the user lives in a specific region, the notification unit will prioritize notifications related to that region. For example, if the user is interested in a specific region, the notification unit will prioritize notifications related to that region. Furthermore, if the user plans to move to a specific region, the notification unit can also prioritize notifications related to that region. In this way, the notification unit can prioritize highly relevant notifications by considering the user's geographical location information. Some or all of the above processing in the notification unit may be performed using, for example, a generative AI, or without a generative AI. For example, the notification unit can input the user's geographical location information data into a generative AI, which can then prioritize highly relevant notifications.

[0111] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0112] The home search system can also include a history analysis unit that analyzes the user's past search history. The history analysis unit analyzes the properties and conditions the user has searched for in the past to understand the user's preferences and tendencies. For example, the history analysis unit can extract the characteristics of properties the user has searched for in the past and preferentially suggest similar properties. The history analysis unit can also suggest relaxing the conditions based on the conditions the user has searched for in the past. Furthermore, the history analysis unit can refer to the ratings of properties the user has searched for in the past and preferentially suggest properties with high ratings. In this way, the history analysis unit can provide more appropriate property information by analyzing the user's past search history.

[0113] The home search system can also include a lifestyle analysis unit that understands the user's current living situation. This unit collects information such as the user's family structure, occupation, and hobbies, and then suggests properties that are best suited to the user. For example, if the user is raising children, the lifestyle analysis unit can prioritize suggesting properties with a child-friendly environment. If the user has pets, it can also suggest pet-friendly properties. Furthermore, if the user has hobbies, the lifestyle analysis unit can suggest properties near facilities related to those hobbies. In this way, the lifestyle analysis unit can provide more appropriate property information by understanding the user's current living situation.

[0114] The home search system may further include a display adjustment unit that estimates the user's emotions and adjusts how property information is displayed based on the estimated emotions. For example, the display adjustment unit may display detailed property information when the user is relaxed. For example, it may display concise property information when the user is in a hurry. It may also display visually appealing property information when the user is excited. In this way, the display adjustment unit can provide more appropriate information by adjusting how property information is displayed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The 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 display adjustment unit may be performed using AI or not using AI. For example, the display adjustment unit can input user emotion data into the generative AI, which can then adjust how property information is displayed.

[0115] The home search system may further include a geographic information analysis unit that provides property information while considering the user's geographic location. The geographic information analysis unit prioritizes providing property information relevant to a specific region if the user lives in that region. For example, if the user is interested in a particular region, the geographic information analysis unit can provide property information relevant to that region. Furthermore, if the user plans to move to a particular region, the geographic information analysis unit can provide property information relevant to that region. This allows the geographic information analysis unit to provide more appropriate property information by considering the user's geographic location. Some or all of the above processing in the geographic information analysis unit may be performed using, for example, AI, or without AI. For example, the geographic information analysis unit can input the user's geographic location data into a generating AI, which can then provide highly relevant property information.

[0116] The home search system may further include a social media analysis unit that analyzes the user's social media activity to provide property information. The social media analysis unit provides information about properties of interest based on information shared by the user on social media. For example, the social media analysis unit can provide information about relevant properties based on information about accounts the user follows on social media. It can also provide information about relevant properties based on information about groups the user participates in on social media. In this way, the social media analysis unit can provide more appropriate property information by analyzing the user's social media activity. Some or all of the above processing in the social media analysis unit may be performed using AI, for example, or not using AI. For example, the social media analysis unit can input the user's social media activity data into a generating AI, which can then provide relevant property information.

[0117] The home search system may further include a priority determination unit that estimates the user's emotions and determines the priority of property information based on the estimated emotions. For example, if the user is relaxed, the priority determination unit may prioritize providing detailed property information. For example, if the user is in a hurry, the priority determination unit may prioritize providing concise property information. The priority determination unit may also prioritize providing visually appealing property information if the user is excited. In this way, the priority determination unit can provide more appropriate information by prioritizing property information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The 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 priority determination unit may be performed using AI or not using AI. For example, the priority determination unit can input user emotion data into the generative AI, and the generative AI can determine the priority of property information.

[0118] The home search system may further include a selection history analysis unit that provides property information by referring to the user's past selection history. The selection history analysis unit analyzes information on properties the user has previously selected to understand the user's preferences and tendencies. For example, the selection history analysis unit can extract the characteristics of properties the user has previously selected and preferentially provide similar properties. The selection history analysis unit can also suggest relaxing the conditions based on the conditions the user has previously selected. Furthermore, the selection history analysis unit can refer to the ratings of properties the user has previously selected and preferentially provide properties with high ratings. In this way, the selection history analysis unit can provide more appropriate property information by referring to the user's past selection history. Some or all of the above processing in the selection history analysis unit may be performed using AI, for example, or without AI. For example, the selection history analysis unit can input the user's past selection history data into a generating AI, and the generating AI can provide property information.

[0119] The home search system may further include a suggestion expression adjustment unit that estimates the user's emotions and adjusts the way suggestions are presented based on the estimated emotions. For example, the suggestion expression adjustment unit may provide detailed suggestions if the user is relaxed. For example, it may provide concise suggestions if the user is in a hurry. It may also provide visually appealing suggestions if the user is excited. In this way, the suggestion expression adjustment unit can provide more appropriate suggestions by adjusting the way suggestions are presented based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion expression adjustment unit may be performed using AI or not using AI. For example, the suggestion expression adjustment unit can input user emotion data into the generative AI, which can then adjust the way suggestions are presented.

[0120] The home search system may also include a hearing history analysis unit that analyzes the user's past hearing history and selects the optimal hearing method. The hearing history analysis unit prioritizes using question formats that the user has preferred in the past. For example, it may refer to questions that the user found easy to answer in the past. The hearing history analysis unit can also conduct hearings at specific time periods based on the user's past hearing history. For example, it may conduct hearings at times when the user was most relaxed in the past. In this way, the hearing history analysis unit can select the optimal hearing method by analyzing the user's past hearing history. Some or all of the above processing in the hearing history analysis unit may be performed using AI, for example, or without AI. For example, the hearing history analysis unit can input the user's past hearing history data into a generating AI, which can then select the optimal hearing method.

[0121] The home search system may further include a notification adjustment unit that estimates the user's emotions and adjusts the timing and method of notifications based on the estimated emotions. For example, if the user is stressed, the notification adjustment unit may reduce the frequency of notifications and only notify important information. For example, if the user is relaxed, the notification adjustment unit may provide detailed notifications and information. Also, if the user is in a hurry, the notification adjustment unit may provide concise notifications and information quickly. In this way, the notification adjustment unit can provide more appropriate notifications by adjusting the timing and method of notifications based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The 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 notification adjustment unit may be performed using AI or not using AI. For example, the notification adjustment unit can input user emotion data into the generative AI, which can then adjust the timing and method of notifications.

[0122] The following briefly describes the processing flow for example form 2.

[0123] Step 1: The interviewing department conducts detailed interviews to understand the user's desired conditions and lifestyle. For example, when a user enters their desired conditions for buying or renting a home, a conversational AI agent is used to ask detailed questions. Specifically, questions such as "How many minutes' walk from the station do you prefer?", "What is your budget?", "Do you have pets?", and "Do you have children?" can be asked. Step 2: The generation unit generates optimal property information based on the information gathered by the interviewing unit. The generation unit uses a generation AI to extract properties that match the user's desired conditions and lifestyle. For example, it searches for properties that meet the conditions of "within a 10-minute walk from the station," "south-facing," and "budget under 5 million yen." Step 3: The proposal unit makes suggestions to relax the conditions based on the property information generated by the generation unit. The proposal unit makes suggestions to increase the number of target properties by relaxing the user's conditions. For example, it can make suggestions to relax "within 10 minutes' walk from the station" to "within 11 minutes' walk," or to change "south-facing" to "southwest-facing" or "southeast-facing." Step 4: The provisioning unit provides the user with the property information proposed by the suggestioning unit. The provisioning unit notifies the user of the property information proposed by the generation AI. For example, the property information can be provided to the user through channels such as messaging apps.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] Each of the multiple elements described above, including the hearing unit, generation unit, proposal unit, provision unit, reception unit, and notification unit, is implemented by at least one of the smart device 14 and the data processing unit 12. For example, the hearing unit is implemented by the control unit 46A of the smart device 14 and conducts detailed interviews about the user's desired conditions and lifestyle. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates optimal property information. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and makes suggestions to relax the conditions. The provision unit is implemented by the control unit 46A of the smart device 14 and provides property information to the user. The reception unit is implemented by the control unit 46A of the smart device 14 and receives user input. The notification unit is implemented by the control unit 46A of the smart device 14 and notifies the user. 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.

[0128] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] Each of the multiple elements described above, including the hearing unit, generation unit, proposal unit, provision unit, reception unit, and notification unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the hearing unit is implemented by the control unit 46A of the smart glasses 214 and conducts detailed interviews about the user's desired conditions and lifestyle. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates optimal property information. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and makes suggestions to relax the conditions. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides property information to the user. The reception unit is implemented by the control unit 46A of the smart glasses 214 and receives user input. The notification unit is implemented by the control unit 46A of the smart glasses 214 and notifies the user. 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.

[0144] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0153] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0154] In 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.

[0155] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0156] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0157] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0158] The data processing system 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.

[0159] Each of the multiple elements described above, including the hearing unit, generation unit, proposal unit, provision unit, reception unit, and notification unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the hearing unit is implemented by the control unit 46A of the headset terminal 314 and conducts detailed interviews about the user's desired conditions and lifestyle. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates optimal property information. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and makes suggestions to relax the conditions. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides property information to the user. The reception unit is implemented by the control unit 46A of the headset terminal 314 and receives user input. The notification unit is implemented by the control unit 46A of the headset terminal 314 and notifies the user. 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.

[0160] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.).

[0173] 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.

[0174] 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.

[0175] 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.

[0176] Each of the multiple elements described above, including the hearing unit, generation unit, proposal unit, provision unit, reception unit, and notification unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the hearing unit is implemented by the control unit 46A of the robot 414 and conducts a detailed hearing of the user's desired conditions and lifestyle. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates optimal property information. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and makes a proposal to relax the conditions. The provision unit is implemented by, for example, the control unit 46A of the robot 414 and provides property information to the user. The reception unit is implemented by, for example, the control unit 46A of the robot 414 and receives user input. The notification unit is implemented by, for example, the control unit 46A of the robot 414 and notifies the user. 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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."

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] (Note 1) The interview department conducts detailed interviews to understand the user's desired conditions and lifestyle, Based on the information gathered by the aforementioned hearing unit, a generation unit generates optimal property information. Based on the property information generated by the generation unit, a proposal unit makes suggestions to relax the conditions, The system comprises a provisioning unit that provides the user with property information proposed by the proposal unit. A system characterized by the following features. (Note 2) It is equipped with a reception unit that accepts user input. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a notification unit that notifies the user. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Refer to the property information database and extract properties that match the user's criteria. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, We propose relaxing the user requirements. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, Providing users with the most suitable property information. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned hearing section is, We estimate the user's emotions and adjust the timing and method of interviews based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned hearing section is, Analyze the user's past interview history and select the most suitable interview method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned hearing section is, During the interview, the questions are customized based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned hearing section is, The system estimates the user's emotions and determines the priority of interviews based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned hearing section is, During the interview, we prioritize asking highly relevant questions, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned hearing section is, During the interview, we analyze the user's social media activity and ask relevant questions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is We estimate the user's emotions and adjust the way property information is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is During generation, 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 15) The generating unit is During generation, different generation algorithms are applied depending on the property category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is It estimates the user's emotions and adjusts the length of the property information generated based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is During generation, the generation priority is determined based on the property registration date. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is During generation, the generation order is adjusted based on the relevance of the properties. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the conditions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the category of conditions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When submitting a proposal, the priority of the proposals will be determined based on when the conditions were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the conditions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, The system estimates the user's emotions and adjusts how property information is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing the service, the system will refer to the user's past selection history to select the most suitable display method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing the service, the displayed content will be customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, The system estimates the user's emotions and prioritizes the property information provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing the service, the optimal display method will be selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing the service, we analyze the user's social media activity and adjust the displayed content accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing and method of reception based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned reception unit is At the time of registration, the system analyzes the user's past registration history and selects the most suitable registration method. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned reception unit is The system estimates the user's emotions and determines the priority of the reception process based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned reception unit is During registration, the system prioritizes registrations that are highly relevant to the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned notification unit, It estimates the user's emotions and adjusts the timing and method of notifications based on the estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned notification unit, When sending a notification, the system analyzes the user's past notification history and selects the most suitable notification method. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned notification unit, It estimates the user's emotions and prioritizes notifications based on those emotions. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned notification unit, When sending notifications, the system prioritizes relevant notifications by considering the user's geographical location. The system described in Appendix 3, characterized by the features described herein. [Explanation of Symbols]

[0196] 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 interview department conducts detailed interviews to understand the user's desired conditions and lifestyle, Based on the information gathered by the aforementioned hearing unit, a generation unit generates optimal property information. Based on the property information generated by the generation unit, a proposal unit makes suggestions to relax the conditions, The system comprises a provisioning unit that provides the user with property information proposed by the proposal unit. A system characterized by the following features.

2. It is equipped with a reception unit that accepts user input. The system according to feature 1.

3. It includes a notification unit that notifies the user. The system according to feature 1.

4. The generating unit is Refer to the property information database and extract properties that match the user's criteria. The system according to feature 1.

5. The aforementioned proposal section is, We propose relaxing the user requirements. The system according to feature 1.

6. The aforementioned supply unit is, Providing users with the most suitable property information. The system according to feature 1.

7. The aforementioned hearing section is, We estimate the user's emotions and adjust the timing and method of interviews based on those estimated emotions. The system according to feature 1.

8. The aforementioned hearing section is, Analyze the user's past interview history and select the most suitable interview method. The system according to feature 1.

9. The aforementioned hearing section is, During the interview, the questions are customized based on the user's current lifestyle and areas of interest. The system according to feature 1.

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