System

Abstract

An object of a system according to an embodiment is to efficiently collect and evaluate reliable information in a rental property search.SOLUTION: A system according to an embodiment includes a property information providing unit, a detailed information providing unit, a review providing unit, and an evaluation providing unit. A property information providing part remotely provides property information by utilizing the generated AI. The detailed information providing unit provides detailed information based on the property information provided by the property information providing unit. The review provider provides a review based on the detailed information provided by the detailed information provider. The evaluation provider provides an evaluation based on the review provided by the review provider.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem of making it difficult to efficiently collect and evaluate reliable information when searching for rental properties.

[0005] The system according to the embodiment aims to efficiently collect and evaluate reliable information when searching for rental properties. [Means for solving the problem]

[0006] The system according to the embodiment includes a property information providing unit, a detailed information providing unit, a review providing unit, and a rating providing unit. The property information providing unit utilizes a generation AI to remotely provide property information. The detailed information providing unit provides detailed information based on the property information provided by the property information providing unit. The review providing unit provides reviews based on the detailed information provided by the detailed information providing unit. The rating providing unit provides ratings based on the reviews provided by the review providing unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently collect and evaluate reliable information when searching for rental properties. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) A virtual real estate agent system according to an embodiment of the present invention utilizes generative AI to remotely provide property information and a platform including detailed information, reviews, and ratings, allowing users to obtain reliable information and effectively evaluate properties.

[0029] A virtual real estate agent system according to an embodiment includes a property information providing unit, a detailed information providing unit, a review providing unit, and an evaluation providing unit. The property information providing unit utilizes a generation AI to remotely provide property information. For example, when a user inputs a request such as "I'm looking for a 2LDK rental property in Tokyo," the generation AI analyzes the request and collects and provides the corresponding property information. The generation AI receives input in the form of prompts containing instructions from the user regarding what the generation AI wants the AI ​​to do, and the generation AI provides property information based on the prompts. The detailed information providing unit provides detailed information based on the property information provided by the property information providing unit. For example, it provides detailed information about the property's layout, facilities, and surrounding environment. The review providing unit provides reviews based on the detailed information provided by the detailed information providing unit. For example, it collects reviews and ratings from past tenants and provides them to the user. The evaluation providing unit provides evaluations based on the reviews provided by the review providing unit. For example, it analyzes the content of the reviews and calculates an overall evaluation. This allows the virtual real estate agent system to provide users with reliable information and effectively evaluate properties.

[0030] The property information provision unit analyzes a user's past search history and browsing history and prioritizes providing properties that best match the user's preferences. For example, the generation AI analyzes a user's past search history and extracts the characteristics of properties in which the user previously expressed interest. For example, it finds patterns such as specific areas, floor plans, and rent ranges, and suggests new properties based on those patterns. The property information provision unit also identifies the characteristics of properties frequently viewed by the user based on the user's browsing history and prioritizes displaying properties with similar characteristics. For example, it learns the characteristics of properties the user has spent a lot of time viewing and suggests similar properties. The generation AI also integrates the user's past search history and browsing history to comprehensively analyze the user's preferences. For example, if a user is interested in specific facilities or surrounding areas, it suggests the most suitable properties based on that information. This allows the system to efficiently provide properties that match the user's preferences.

[0031] The property information providing unit can include a future value prediction of a property when providing property information. For example, the generating AI analyzes past property data and develops an algorithm for predicting future value. For example, the generating AI predicts the future value of a property based on past rent fluctuations and local development plans. The property information providing unit also provides information including the future value prediction of a property, allowing users to evaluate properties from an investment perspective. For example, the generating AI predicts future rent increases and property value increases and provides that information to users. The property information providing unit also references external data such as local economic trends and infrastructure development plans when predicting the future value of a property. For example, the generating AI makes value predictions taking into account the planned opening of new transportation lines and construction plans for commercial facilities. This allows users to evaluate properties from an investment perspective.

[0032] When providing property information, the property information providing unit can also simultaneously provide information about events in the surrounding area and information about lifestyle convenience. For example, when the generation AI provides property information, the property information providing unit collects event information in the surrounding area and provides it to the user. For example, it displays information about nearby festivals and markets along with the property information. The property information providing unit also builds a system that provides information about lifestyle convenience in the surrounding area of ​​the property. For example, it displays information about nearby supermarkets, hospitals, schools, etc. along with the property information. When the generation AI provides property information, the property information providing unit also simultaneously provides information about transportation access and public safety in the surrounding area. For example, it displays information about the nearest station and bus stop, the local crime rate, etc. along with the property information. This allows the user to simultaneously obtain information about the surrounding area of ​​the property.

[0033] The property information providing unit can suggest customization options for a property desired by the user when providing property information. For example, the property information providing unit builds a system that suggests customization options such as renovations and furniture arrangement when the generation AI provides property information. For example, it proposes a renovation plan desired by the user. The property information providing unit also provides an interface that allows the user to input customization options for the property desired, and suggests the optimal property based on that information. For example, it simulates the furniture arrangement desired by the user. The property information providing unit also provides detailed information such as the cost and construction period of the customization options when the generation AI provides property information. For example, it displays a renovation estimate and construction schedule. This makes it possible to suggest the customization options desired by the user.

[0034] The detailed information providing unit can also provide information on the energy efficiency and environmental impact of a property. For example, when the generation AI provides detailed information about a property, the detailed information providing unit collects information on energy efficiency and provides it to the user. For example, it displays the insulation performance and energy consumption of the property. The detailed information providing unit also builds a system that provides information on the environmental impact of a property. For example, it evaluates the impact that the building materials and equipment of the property have on the environment and provides that information to the user. The detailed information providing unit also develops an algorithm that evaluates energy efficiency and environmental impact when the generation AI provides detailed information about a property. For example, it scores the energy efficiency of the property and provides the score to the user. This allows the user to obtain information on the energy efficiency and environmental impact of the property.

[0035] The review providing unit can provide detailed feedback based on the lifestyles and family compositions of past tenants. For example, the generation AI collects reviews based on the lifestyles and family compositions of past tenants and provides them to the user. For example, reviews of properties for families and properties for single people are displayed separately. The review providing unit also builds a system that provides detailed feedback based on the lifestyles of past tenants. For example, it collects reviews and ratings of tenants who have pets and provides that information to the user. The review providing unit also develops an algorithm that evaluates the suitability of properties according to family composition when the generation AI provides feedback based on the family compositions of past tenants. For example, it evaluates properties suitable for families with children. This allows the user to obtain detailed feedback based on the lifestyles and family compositions of past tenants.

[0036] The detailed information providing unit can also provide information about the property's history and past owners. For example, when the generation AI provides detailed information about a property, the detailed information providing unit collects information about the property's history and provides it to the user. For example, it displays the year the property was built and past renovation history. The detailed information providing unit also builds a system that provides information about the property's past owners. For example, it displays the number of past owners, the ownership period, and the owner's profile. The detailed information providing unit also develops an algorithm that analyzes information about the property's history and past owners when the generation AI provides detailed information about a property. For example, it evaluates reliability based on the property's ownership history and provides that information to the user. This allows the user to obtain information about the property's history and past owners.

[0037] When providing information on property vacancies and rent fluctuations, the property information providing unit can also include future rent predictions based on past data. For example, the generation AI in the property information providing unit analyzes past rent data and develops an algorithm for predicting future rents. For example, future rents are predicted based on past rent fluctuations and local economic trends. Furthermore, by including future rent predictions when providing information on property vacancies and rent fluctuations, the property information providing unit allows users to evaluate properties from a long-term perspective. For example, it predicts future rent increases and decreases and provides that information to users. Furthermore, when the generation AI provides information on property vacancies and rent fluctuations, the property information providing unit references external data such as local economic trends and infrastructure development plans. For example, rent predictions take into account the planned opening of new transportation lines and construction plans for commercial facilities. This allows users to evaluate properties from a long-term perspective.

[0038] The property information providing unit can provide customizable comparison criteria based on the user's priorities when providing a property comparison function. For example, when the generation AI provides a property comparison function, the property information providing unit provides an interface that allows the user to set customizable comparison criteria based on the user's priorities. For example, the user can freely set criteria such as rent, floor plan, and location. The property information providing unit also builds a system that provides customizable comparison criteria based on the user's priorities. For example, the property information providing unit automatically adjusts the property comparison criteria according to the user's desired conditions. Furthermore, when the generation AI provides a property comparison function, the property information providing unit suggests optimal comparison criteria based on the user's past search history and browsing history. For example, the property information providing unit customizes the comparison criteria based on the user's frequently searched conditions. This allows the user to compare properties based on their own priorities.

[0039] When providing information on property vacancies and rent fluctuations, the property information providing unit can also provide information on economic trends and development plans in the surrounding area. For example, when the generation AI provides information on property vacancies and rent fluctuations, the property information providing unit collects information on economic trends in the surrounding area and provides it to the user. For example, it displays data such as the local economic growth rate and unemployment rate. The property information providing unit also builds a system that provides information on development plans in the surrounding area. For example, it displays construction plans for new commercial facilities and infrastructure along with property information. Furthermore, when the generation AI provides information on property vacancies and rent fluctuations, the property information providing unit provides information that takes into account local economic trends and development plans. For example, it evaluates the future development potential of the area and provides that information to the user. This allows the user to obtain information on economic trends and development plans in the surrounding area.

[0040] The property information providing unit can automatically generate a checklist for users to evaluate properties when providing a property comparison function. For example, the property information providing unit builds a system that automatically generates a checklist for users to evaluate properties when the generation AI provides the property comparison function. For example, it lists items related to the property's facilities and surrounding environment. The property information providing unit also customizes the property evaluation checklist based on the user's desired conditions. For example, it adjusts the checklist items according to the conditions that the user prioritizes. Furthermore, when the generation AI provides the property comparison function, the property information providing unit suggests an optimal checklist based on past user data. For example, it creates a checklist based on items that other users have highly rated. This allows the automatic generation of a checklist for users to evaluate properties.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The property information provider can also suggest properties based on the user's health condition. For example, for a user with allergies, properties with air purifiers or properties without pets can be suggested preferentially. For a user who likes to exercise, properties with nearby gyms or parks can be suggested. Furthermore, in order to make suggestions based on the user's health condition, it is also possible to collect and analyze the user's health data. This makes it possible to provide properties that are optimal for the user's health condition.

[0043] The property information provider can also suggest properties based on the user's lifestyle. For example, for a user who works remotely, properties with ample home office space can be suggested. For a user who enjoys outdoor activities, properties with nearby nature parks or hiking trails can be suggested. Furthermore, in order to make suggestions based on lifestyle, it is also possible to collect and analyze data on the user's hobbies and activities. This makes it possible to provide properties that are best suited to the user's lifestyle.

[0044] The property information providing unit can also suggest properties based on the user's family structure. For example, for a family with children, it can suggest properties with nearby schools and parks. For a family with elderly people, it can suggest properties with barrier-free designs. Furthermore, in order to make suggestions based on family structure, it is also possible to collect and analyze the user's family data. This makes it possible to provide properties that are optimal for the user's family structure.

[0045] The property information providing unit can also suggest properties based on the user's hobbies and interests. For example, a property with soundproofing can be suggested to a user who loves music. A property with a spacious kitchen and the latest cooking equipment can be suggested to a user who enjoys cooking. Furthermore, in order to make suggestions based on hobbies and interests, it is also possible to collect and analyze the user's hobby data. This makes it possible to provide properties that best suit the user's hobbies and interests.

[0046] The property information providing unit can also suggest properties based on the user's future plans. For example, a property with many rooms can be suggested to a user who plans to have a larger family in the future. Also, a property in a quiet environment can be suggested to a user who is considering retiring in the future. Furthermore, in order to make suggestions based on future plans, it is also possible to collect and analyze the user's future plan data. This makes it possible to provide properties that are best suited to the user's future plans.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The property information provider uses the generation AI to remotely provide property information. For example, if a user inputs a request such as "I'm looking for a 2LDK rental property in Tokyo," the generation AI analyzes the request and collects and provides the relevant property information. The input to the generation AI is a prompt containing instructions on what the user wants the generation AI to do, and the generation AI provides property information based on that prompt. Step 2: The detailed information providing unit provides detailed information based on the property information provided by the property information providing unit, such as detailed information about the property's layout, facilities, and surrounding environment. Step 3: The review providing unit provides reviews based on the detailed information provided by the detailed information providing unit. For example, reviews and ratings from past residents are collected and provided to the user. Step 4: The rating providing unit provides a rating based on the reviews provided by the review providing unit, for example, by analyzing the content of the reviews and calculating an overall rating.

[0049] (Example 2) A virtual real estate agent system according to an embodiment of the present invention utilizes generative AI to remotely provide property information and a platform including detailed information, reviews, and ratings, allowing users to obtain reliable information and effectively evaluate properties.

[0050] A virtual real estate agent system according to an embodiment includes a property information providing unit, a detailed information providing unit, a review providing unit, and an evaluation providing unit. The property information providing unit utilizes a generation AI to remotely provide property information. For example, when a user inputs a request such as "I'm looking for a 2LDK rental property in Tokyo," the generation AI analyzes the request and collects and provides the corresponding property information. The generation AI receives input in the form of prompts containing instructions from the user regarding what the generation AI wants the AI ​​to do, and the generation AI provides property information based on the prompts. The detailed information providing unit provides detailed information based on the property information provided by the property information providing unit. For example, it provides detailed information about the property's layout, facilities, and surrounding environment. The review providing unit provides reviews based on the detailed information provided by the detailed information providing unit. For example, it collects reviews and ratings from past tenants and provides them to the user. The evaluation providing unit provides evaluations based on the reviews provided by the review providing unit. For example, it analyzes the content of the reviews and calculates an overall evaluation. This allows the virtual real estate agent system to provide users with reliable information and effectively evaluate properties.

[0051] The property information provision unit analyzes a user's past search history and browsing history and prioritizes providing properties that best match the user's preferences. For example, the generation AI analyzes a user's past search history and extracts the characteristics of properties in which the user previously expressed interest. For example, it finds patterns such as specific areas, floor plans, and rent ranges, and suggests new properties based on those patterns. The property information provision unit also identifies the characteristics of properties frequently viewed by the user based on the user's browsing history and prioritizes displaying properties with similar characteristics. For example, it learns the characteristics of properties the user has spent a lot of time viewing and suggests similar properties. The generation AI also integrates the user's past search history and browsing history to comprehensively analyze the user's preferences. For example, if a user is interested in specific facilities or surrounding areas, it suggests the most suitable properties based on that information. This allows the system to efficiently provide properties that match the user's preferences.

[0052] The property information providing unit can include a future value prediction of a property when providing property information. For example, the generating AI analyzes past property data and develops an algorithm for predicting future value. For example, the generating AI predicts the future value of a property based on past rent fluctuations and local development plans. The property information providing unit also provides information including the future value prediction of a property, allowing users to evaluate properties from an investment perspective. For example, the generating AI predicts future rent increases and property value increases and provides that information to users. The property information providing unit also references external data such as local economic trends and infrastructure development plans when predicting the future value of a property. For example, the generating AI makes value predictions taking into account the planned opening of new transportation lines and construction plans for commercial facilities. This allows users to evaluate properties from an investment perspective.

[0053] The property information providing unit uses the emotion estimation function to analyze the user's emotions in real time when browsing property information, and can preferentially display properties that elicit positive emotions. For example, the property information providing unit uses the emotion estimation function to analyze the user's facial expressions and voice when browsing property information and estimate emotions in real time. For example, properties that elicit a smile from the user are preferentially displayed. The property information providing unit also identifies properties that elicit positive emotions based on the user's emotional response, and builds a system that preferentially displays those properties. For example, properties that elicit an excited expression from the user are preferentially suggested. The property information providing unit also uses the emotion estimation function to collect emotional data when the user browses property information and learn the characteristics of properties that elicit positive emotions. For example, common features of properties to which the user has shown a favorable response are analyzed, and properties that have those characteristics are preferentially displayed. This allows properties to be preferentially displayed to which the user has positive emotions.

[0054] When providing property information, the property information providing unit can also simultaneously provide information about events in the surrounding area and information about lifestyle convenience. For example, when the generation AI provides property information, the property information providing unit collects event information in the surrounding area and provides it to the user. For example, it displays information about nearby festivals and markets along with the property information. The property information providing unit also builds a system that provides information about lifestyle convenience in the surrounding area of ​​the property. For example, it displays information about nearby supermarkets, hospitals, schools, etc. along with the property information. When the generation AI provides property information, the property information providing unit also simultaneously provides information about transportation access and public safety in the surrounding area. For example, it displays information about the nearest station and bus stop, the local crime rate, etc. along with the property information. This allows the user to simultaneously obtain information about the surrounding area of ​​the property.

[0055] The property information providing unit can suggest customization options for a property desired by the user when providing property information. For example, the property information providing unit builds a system that suggests customization options such as renovations and furniture arrangement when the generation AI provides property information. For example, it proposes a renovation plan desired by the user. The property information providing unit also provides an interface that allows the user to input customization options for the property desired, and suggests the optimal property based on that information. For example, it simulates the furniture arrangement desired by the user. The property information providing unit also provides detailed information such as the cost and construction period of the customization options when the generation AI provides property information. For example, it displays a renovation estimate and construction schedule. This makes it possible to suggest the customization options desired by the user.

[0056] The property information providing unit uses the emotion estimation function to analyze the emotion of the user when entering property information and can suggest properties that the user is most interested in. For example, the property information providing unit uses the emotion estimation function to analyze the facial expressions and voice of the user when entering property information and estimate the emotion in real time. For example, it prioritizes suggesting properties for which the user has an excited expression. The property information providing unit also identifies properties that the user is most interested in based on the user's emotional response and builds a system that prioritizes suggesting those properties. For example, it learns the characteristics of properties to which the user has shown a favorable response and suggests properties that have those characteristics. The property information providing unit also uses the emotion estimation function to collect emotional data when the user enters property information and analyze the characteristics of properties that the user is most interested in. For example, it identifies common features of properties for which the user has shown positive emotions and suggests properties that have those characteristics. This makes it possible to suggest properties that the user is most interested in.

[0057] The detailed information providing unit can also provide information on the energy efficiency and environmental impact of a property. For example, when the generation AI provides detailed information about a property, the detailed information providing unit collects information on energy efficiency and provides it to the user. For example, it displays the insulation performance and energy consumption of the property. The detailed information providing unit also builds a system that provides information on the environmental impact of a property. For example, it evaluates the impact that the building materials and equipment of the property have on the environment and provides that information to the user. The detailed information providing unit also develops an algorithm that evaluates energy efficiency and environmental impact when the generation AI provides detailed information about a property. For example, it scores the energy efficiency of the property and provides the score to the user. This allows the user to obtain information on the energy efficiency and environmental impact of the property.

[0058] The review providing unit can provide detailed feedback based on the lifestyles and family compositions of past tenants. For example, the generation AI collects reviews based on the lifestyles and family compositions of past tenants and provides them to the user. For example, reviews of properties for families and properties for single people are displayed separately. The review providing unit also builds a system that provides detailed feedback based on the lifestyles of past tenants. For example, it collects reviews and ratings of tenants who have pets and provides that information to the user. The review providing unit also develops an algorithm that evaluates the suitability of properties according to family composition when the generation AI provides feedback based on the family compositions of past tenants. For example, it evaluates properties suitable for families with children. This allows the user to obtain detailed feedback based on the lifestyles and family compositions of past tenants.

[0059] The detailed information providing unit can also provide information about the property's history and past owners. For example, when the generation AI provides detailed information about a property, the detailed information providing unit collects information about the property's history and provides it to the user. For example, it displays the year the property was built and past renovation history. The detailed information providing unit also builds a system that provides information about the property's past owners. For example, it displays the number of past owners, the ownership period, and the owner's profile. The detailed information providing unit also develops an algorithm that analyzes information about the property's history and past owners when the generation AI provides detailed information about a property. For example, it evaluates reliability based on the property's ownership history and provides that information to the user. This allows the user to obtain information about the property's history and past owners.

[0060] The review providing unit can analyze the emotions of users when they post reviews and encourage positive reviews. The review providing unit, for example, uses an emotion estimation function to analyze the emotions of users when they post reviews and build a system that encourages positive reviews. For example, it encourages users to post reviews when they show positive emotions. The review providing unit also provides incentives to encourage positive reviews based on the users' emotional reactions. For example, it awards points or benefits to users who post positive reviews. The review providing unit also uses the emotion estimation function to collect emotional data when users post reviews and learn the characteristics of positive reviews. For example, it analyzes commonalities in reviews in which users show positive emotions and encourages reviews that have those characteristics. This makes it possible to encourage users to post positive reviews.

[0061] The review providing unit analyzes the emotions of users when viewing reviews and ratings, and can prioritize displaying information that interests the user most. The review providing unit, for example, uses an emotion estimation function to analyze facial expressions and voices of users when viewing reviews and ratings, and estimate emotions in real time. For example, reviews that the user is interested in are prioritized for display. The review providing unit also identifies information that the user is most interested in based on the user's emotional response, and builds a system that prioritizes displaying that information. For example, reviews and ratings to which the user has a favorable response are prioritized for display. The review providing unit also uses the emotion estimation function to collect emotional data when the user views reviews and ratings, and analyze the characteristics of the information that the user is most interested in. For example, common features of reviews in which the user has expressed positive emotions are identified, and information having those characteristics is prioritized for display. This makes it possible to prioritize displaying information that the user is most interested in.

[0062] When providing information on property vacancies and rent fluctuations, the property information providing unit can also include future rent predictions based on past data. For example, the generation AI in the property information providing unit analyzes past rent data and develops an algorithm for predicting future rents. For example, future rents are predicted based on past rent fluctuations and local economic trends. Furthermore, by including future rent predictions when providing information on property vacancies and rent fluctuations, the property information providing unit allows users to evaluate properties from a long-term perspective. For example, it predicts future rent increases and decreases and provides that information to users. Furthermore, when the generation AI provides information on property vacancies and rent fluctuations, the property information providing unit references external data such as local economic trends and infrastructure development plans. For example, rent predictions take into account the planned opening of new transportation lines and construction plans for commercial facilities. This allows users to evaluate properties from a long-term perspective.

[0063] The property information providing unit can provide customizable comparison criteria based on the user's priorities when providing a property comparison function. For example, when the generation AI provides a property comparison function, the property information providing unit provides an interface that allows the user to set customizable comparison criteria based on the user's priorities. For example, the user can freely set criteria such as rent, floor plan, and location. The property information providing unit also builds a system that provides customizable comparison criteria based on the user's priorities. For example, the property information providing unit automatically adjusts the property comparison criteria according to the user's desired conditions. Furthermore, when the generation AI provides a property comparison function, the property information providing unit suggests optimal comparison criteria based on the user's past search history and browsing history. For example, the property information providing unit customizes the comparison criteria based on the user's frequently searched conditions. This allows the user to compare properties based on their own priorities.

[0064] The property information providing unit uses the emotion estimation function to analyze the user's emotions when comparing properties and can preferentially suggest properties that will satisfy the user. For example, the property information providing unit uses the emotion estimation function to analyze the user's facial expressions and voice when comparing properties and estimate emotions in real time. For example, it preferentially suggests properties for which the user has a satisfied expression. The property information providing unit also identifies the most satisfying property based on the user's emotional response and builds a system that preferentially suggests that property. For example, it learns the characteristics of properties to which the user has a favorable response and suggests properties that have those characteristics. The property information providing unit also uses the emotion estimation function to collect emotional data when the user compares properties and analyzes the characteristics of the most satisfying property. For example, it identifies common features of properties for which the user has a positive emotion and suggests properties that have those characteristics. This allows it to preferentially suggest properties that will satisfy the user.

[0065] When providing information on property vacancies and rent fluctuations, the property information providing unit can also provide information on economic trends and development plans in the surrounding area. For example, when the generation AI provides information on property vacancies and rent fluctuations, the property information providing unit collects information on economic trends in the surrounding area and provides it to the user. For example, it displays data such as the local economic growth rate and unemployment rate. The property information providing unit also builds a system that provides information on development plans in the surrounding area. For example, it displays construction plans for new commercial facilities and infrastructure along with property information. Furthermore, when the generation AI provides information on property vacancies and rent fluctuations, the property information providing unit provides information that takes into account local economic trends and development plans. For example, it evaluates the future development potential of the area and provides that information to the user. This allows the user to obtain information on economic trends and development plans in the surrounding area.

[0066] The property information providing unit can automatically generate a checklist for users to evaluate properties when providing a property comparison function. For example, the property information providing unit builds a system that automatically generates a checklist for users to evaluate properties when the generation AI provides the property comparison function. For example, it lists items related to the property's facilities and surrounding environment. The property information providing unit also customizes the property evaluation checklist based on the user's desired conditions. For example, it adjusts the checklist items according to the conditions that the user prioritizes. Furthermore, when the generation AI provides the property comparison function, the property information providing unit suggests an optimal checklist based on past user data. For example, it creates a checklist based on items that other users have highly rated. This allows the automatic generation of a checklist for users to evaluate properties.

[0067] The property information providing unit uses the emotion estimation function to analyze the user's emotions when selecting a property and can suggest properties that the user feels most positive about. For example, the property information providing unit uses the emotion estimation function to analyze the user's facial expressions and voice when selecting a property and estimate emotions in real time. For example, it prioritizes suggesting properties for which the user feels positive about. The property information providing unit also builds a system that identifies properties for which the user feels most positive about based on the user's emotional response and prioritizes suggesting those properties. For example, it learns the characteristics of properties for which the user felt favorably and suggests properties that have those characteristics. The property information providing unit also uses the emotion estimation function to collect emotional data when the user selects a property and analyzes the characteristics of properties for which the user feels most positive about. For example, it identifies common features of properties for which the user felt positive about, and suggests properties that have those characteristics. This makes it possible to suggest properties for which the user feels most positive about.

[0068] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0069] The property information provider can also suggest properties based on the user's health condition. For example, for a user with allergies, properties with air purifiers or properties without pets can be suggested preferentially. For a user who likes to exercise, properties with nearby gyms or parks can be suggested. Furthermore, in order to make suggestions based on the user's health condition, it is also possible to collect and analyze the user's health data. This makes it possible to provide properties that are optimal for the user's health condition.

[0070] The property information provider can also suggest properties based on the user's lifestyle. For example, for a user who works remotely, properties with ample home office space can be suggested. For a user who enjoys outdoor activities, properties with nearby nature parks or hiking trails can be suggested. Furthermore, in order to make suggestions based on lifestyle, it is also possible to collect and analyze data on the user's hobbies and activities. This makes it possible to provide properties that are best suited to the user's lifestyle.

[0071] The property information providing unit can also suggest properties based on the user's family structure. For example, for a family with children, it can suggest properties with nearby schools and parks. For a family with elderly people, it can suggest properties with barrier-free designs. Furthermore, in order to make suggestions based on family structure, it is also possible to collect and analyze the user's family data. This makes it possible to provide properties that are optimal for the user's family structure.

[0072] The property information providing unit can estimate the user's emotions and avoid properties that cause stress to the user. For example, if the user is sensitive to noise, properties in quiet environments can be preferentially suggested. Also, if the user feels stressed in small spaces, spacious properties can be suggested. Furthermore, by collecting and analyzing the user's emotional data, it is possible to identify factors that cause stress and suggest properties that avoid those factors. This makes it possible to provide properties where the user can live comfortably.

[0073] The property information providing unit can estimate the user's emotions and suggest properties where the user can relax. For example, if the user wants to relax in a natural environment, properties in lush green areas can be suggested preferentially. If the user wants to relax near water, properties near rivers or lakes can be suggested. Furthermore, by collecting and analyzing the user's emotional data, it is possible to identify factors that contribute to relaxation and suggest properties that have those factors. This makes it possible to provide properties where the user can relax.

[0074] The property information providing unit can estimate the user's emotions and suggest properties that excite the user. For example, if the user is excited by city night views, properties on high floors can be suggested preferentially. Also, if the user is excited by historical buildings, properties in historical areas can be suggested. Furthermore, by collecting and analyzing the user's emotional data, it is possible to identify factors that excite the user and suggest properties that have those factors. This makes it possible to provide properties that excite the user.

[0075] The property information provider can estimate the user's emotions and suggest properties that the user can feel safe in. For example, if the user feels safe in areas with good public safety, properties in areas with low crime rates can be suggested first. Also, if the user feels safe in nearby communities, properties in areas with active community activities can be suggested. Furthermore, by collecting and analyzing the user's emotional data, it is possible to identify factors that make a user feel safe and suggest properties that have those factors. This makes it possible to provide properties that the user can feel safe in.

[0076] The property information providing unit can estimate the user's emotions and suggest properties that will most satisfy the user. For example, if the user is satisfied with a particular design or interior, properties with that design or interior can be suggested preferentially. Also, if the user is satisfied with particular facilities or services, properties that have those facilities or services can be suggested. Furthermore, by collecting and analyzing the user's emotional data, it is possible to identify factors that cause satisfaction and suggest properties that have those factors. This makes it possible to provide properties that will most satisfy the user.

[0077] The property information providing unit can also suggest properties based on the user's hobbies and interests. For example, a property with soundproofing can be suggested to a user who loves music. A property with a spacious kitchen and the latest cooking equipment can be suggested to a user who enjoys cooking. Furthermore, in order to make suggestions based on hobbies and interests, it is also possible to collect and analyze the user's hobby data. This makes it possible to provide properties that best suit the user's hobbies and interests.

[0078] The property information providing unit can also suggest properties based on the user's future plans. For example, a property with many rooms can be suggested to a user who plans to have a larger family in the future. Also, a property in a quiet environment can be suggested to a user who is considering retiring in the future. Furthermore, in order to make suggestions based on future plans, it is also possible to collect and analyze the user's future plan data. This makes it possible to provide properties that are best suited to the user's future plans.

[0079] The processing flow of the second embodiment will be briefly explained below.

[0080] Step 1: The property information provider uses the generation AI to remotely provide property information. For example, if a user inputs a request such as "I'm looking for a 2LDK rental property in Tokyo," the generation AI analyzes the request and collects and provides the relevant property information. The input to the generation AI is a prompt containing instructions on what the user wants the generation AI to do, and the generation AI provides property information based on that prompt. Step 2: The detailed information providing unit provides detailed information based on the property information provided by the property information providing unit, such as detailed information about the property's layout, facilities, and surrounding environment. Step 3: The review providing unit provides reviews based on the detailed information provided by the detailed information providing unit. For example, reviews and ratings from past residents are collected and provided to the user. Step 4: The rating providing unit provides a rating based on the reviews provided by the review providing unit, for example, by analyzing the content of the reviews and calculating an overall rating.

[0081] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0082] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0083] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0084] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0085] 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.

[0086] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0087] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0088] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0089] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0090] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0091] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0092] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0093] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0094] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0095] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0096] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0097] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0098] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0099] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0100] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0101] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0102] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0103] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0104] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0105] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0106] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0107] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0108] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0109] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0110] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0111] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0112] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0113] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0114] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0115] 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.

[0116] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0117] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0118] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0120] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0121] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0122] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0123] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0124] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0125] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0126] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0127] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0128] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0129] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0130] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0131] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0132] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0133] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0134] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0135] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0136] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0137] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0138] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0139] 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.

[0140] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0141] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0142] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0143] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0144] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0145] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0146] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0147] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0148] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A property information provider that uses AI generation to provide property information remotely; a detailed information providing unit that provides detailed information based on the property information provided by the property information providing unit; a review providing unit that provides reviews based on the detailed information provided by the detailed information providing unit; a rating providing unit that provides a rating based on the review provided by the review providing unit. A system characterized by:

2. The property information department When providing property information, include a forecast of the property's future value.

2. The system of claim 1.

3. The detailed information section is It also provides information on the energy efficiency and environmental impact of properties.

2. The system of claim 1.

4. The review provider is Provide detailed feedback based on past residents' lifestyles and family structures 2. The system of claim 1.

5. The property information department When providing information on property availability and rent fluctuations, include future rent forecasts based on past data.

2. The system of claim 1.

6. The property information department Analyzes emotions in real time when users browse property information, and prioritizes displaying properties that evoke positive emotions 2. The system of claim 1.

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