System

Abstract

An object of a system according to an embodiment is to efficiently find a real estate property that best matches the desired conditions of a user.SOLUTION: A system includes a condition input part, a data collection part, a selection part, a recommendation part, and a business negotiation request part. The condition input unit inputs a desired condition of a user. The data collection part collects property information from a real estate list on the Internet on the basis of the desired conditions input by the condition input part. The selection unit analyzes the property information collected by the data collection unit and selects a property most suitable for the desired condition. The recommendation unit recommends the property selected by the selection unit to the user. A business negotiation request part transmits a business negotiation request to a real estate company about a property that a user wants to negotiate.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 that it is difficult for users to efficiently find real estate properties that best meet their desired conditions.

[0005] The system according to the embodiment aims to efficiently find a real estate property that best meets the user's desired conditions. [Means for solving the problem]

[0006] The system according to the embodiment includes a condition input unit, a data collection unit, a selection unit, a recommendation unit, and a negotiation request unit. The condition input unit inputs the user's desired conditions. The data collection unit collects property information from real estate listings on the Internet based on the desired conditions input by the condition input unit. The selection unit analyzes the property information collected by the data collection unit and selects the property that best meets the desired conditions. The recommendation unit recommends the property selected by the selection unit to the user. The negotiation request unit sends a negotiation request to a real estate company regarding the property with which the user wishes to negotiate. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently find a real estate property that best meets the user's desired conditions. [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 real estate recommendation system according to an embodiment of the present invention is a system in which a user inputs the conditions for a desired property, and a generation AI selects and recommends the most suitable property from real estate listings on the Internet. As a result, the real estate recommendation system can recommend the most suitable property based on the user's desired conditions and send a negotiation request.

[0029] A real estate recommendation system according to an embodiment includes a condition input unit, a data collection unit, a selection unit, a recommendation unit, and a negotiation request unit. The condition input unit inputs a user's desired conditions. For example, the user can input conditions such as budget, size, location, layout, and facilities. The data collection unit collects property information from online real estate listings based on the desired conditions input by the condition input unit. For example, the data collection unit collects information such as price, size, facilities, age, and location. The selection unit analyzes the property information collected by the data collection unit and selects properties that best meet the desired conditions. For example, the selection unit can understand the importance of each property and prioritize them by considering factors such as price, location, and facilities. The recommendation unit recommends the properties selected by the selection unit to the user. For example, the recommendation unit explains the features and advantages of the selected properties. The negotiation request unit sends a negotiation request to a real estate company for a property with which the user wishes to negotiate. For example, the negotiation request unit also enables online negotiations. As a result, the real estate recommendation system according to the embodiment can recommend the most suitable property based on the user's desired conditions and send a negotiation request.

[0030] The condition input unit can automatically predict and suggest desired conditions based on the user's past search history or browsing history. For example, the condition input unit analyzes the conditions of properties the user has searched for in the past and the characteristics of properties the user has viewed, and automatically suggests desired conditions the next time the user searches. For example, if the user has searched for many properties near train stations in the past, properties near train stations will be preferentially displayed the next time the user searches. The condition input unit also builds a system that automatically sets conditions such as budget, size, and layout based on the user's past search history. For example, the budget for the next search is automatically set based on the average budget of properties searched for in the past. The condition input unit also learns the characteristics of properties the user has viewed, and suggests conditions that reflect those characteristics the next time the user searches. For example, if the user has viewed many properties that allow pets in the past, properties that allow pets will be preferentially displayed the next time the user searches. This allows desired conditions to be automatically suggested based on the user's past history.

[0031] The condition input unit can analyze a user's lifestyle or hobbies and preferences and propose property conditions based on them. For example, the condition input unit collects the user's lifestyle and hobbies and preferences from questionnaires or profile information and proposes property conditions based on that. For example, for a user whose hobby is the outdoors, it proposes properties in areas with abundant natural environments. The condition input unit also analyzes public information on users' social media accounts, blogs, etc. to build a system that understands the user's lifestyle and hobbies and preferences. For example, for a user whose hobby is cooking, it proposes properties with well-equipped kitchens. The condition input unit also analyzes the user's purchase history and data on services used to identify the user's lifestyle and hobbies and preferences. For example, for a user who is a member of a fitness gym, it proposes properties that are close to a gym. In this way, property conditions can be proposed based on the user's lifestyle and hobbies and preferences.

[0032] The condition input unit can use voice input or gesture input to enable the user to input desired conditions more intuitively. The condition input unit, for example, builds a system that allows the user to input desired conditions by voice. For example, the user inputs by voice, "budget within 30 million yen, area at least 70 square meters." The condition input unit also provides an interface that allows the user to input desired conditions by gesture. For example, the user can set the budget and area by hand movements. The condition input unit also provides an interface that combines voice input and gesture input, allowing the user to input desired conditions more intuitively. For example, the user can specify conditions by voice and make fine adjustments by gestures. This allows the user to input desired conditions more intuitively.

[0033] The condition input unit can provide a multi-user interface that allows users to jointly input conditions with family or friends. The condition input unit provides a multi-user interface that allows users to jointly input desired conditions with family and friends. For example, multiple users can input and share conditions simultaneously. The condition input unit also uses the multi-user interface to build a system that allows users to adjust conditions in real time with family and friends. For example, conditions can be set while exchanging opinions using a chat function. The condition input unit also provides a system that integrates the desired conditions of each user when entering conditions jointly with family and friends and proposes optimal conditions. For example, the system proposes optimal properties based on the desired conditions of each user. This allows users to jointly input conditions with family and friends.

[0034] The data collection unit not only collects real estate listings but also information on the surrounding environment or local lifestyle, allowing for comprehensive property evaluations. For example, the data collection unit uses AI to collect not only real estate listings but also information on the surrounding environment and local lifestyle, building a system for comprehensive property evaluations. For example, it collects information on schools, hospitals, supermarkets, etc. The data collection unit also collects information on the surrounding environment and local lifestyle, and reflects this in property evaluations. For example, it makes evaluations based on information such as public safety, transportation access, and noise levels. The data collection unit also integrates real estate listings and information on the surrounding environment to provide a system for comprehensive property evaluations. For example, it comprehensively evaluates the convenience and livability of the property. This allows for comprehensive property evaluations that include information on the surrounding environment and local lifestyle.

[0035] The data collection unit collects past transaction history or price fluctuation data of a property and can provide future price predictions. For example, the data collection unit builds a system in which AI collects past transaction history and price fluctuation data of a property and provides future price predictions. For example, it predicts price trends based on past transaction data. The data collection unit also analyzes past transaction history of a property and makes future price predictions. For example, it predicts future prices based on past price fluctuation data. The data collection unit also collects price fluctuation data and provides a system that provides future price predictions. For example, it predicts future prices based on past price fluctuation data. This makes it possible to predict future prices based on past transaction history and price fluctuation data of a property.

[0036] The data collection unit can use drone or satellite images to collect the latest information on the appearance of a property and its surrounding environment. The data collection unit, for example, builds a system that uses drones to collect the latest information on the appearance of a property and its surrounding environment. For example, it photographs the appearance of a property and the surrounding scenery and stores the photographs in a database. The data collection unit also uses satellite images to collect the latest information on the appearance of a property and its surrounding environment. For example, it analyzes the location of the property and the surrounding terrain and stores the data in a database. The data collection unit also provides a system that combines drone and satellite images to collect the latest information on the appearance of a property and its surrounding environment. For example, it performs a detailed analysis of the appearance of a property and surrounding facilities. This allows the collection of the latest information on the appearance of a property and its surrounding environment.

[0037] The data collection unit provides an API for collecting information directly from real estate companies or owners, thereby maintaining the freshness of the data. The data collection unit, for example, provides an API for collecting information directly from real estate companies or owners, and builds a system for maintaining the freshness of the data. For example, the latest property information is obtained in real time. The data collection unit also collects information from real estate companies and owners through the API and stores it in a database. For example, the latest information on property prices and facilities is obtained. The data collection unit also collects information directly from real estate companies and owners using the API, and provides a system for maintaining the freshness of the data. For example, the latest property information is updated regularly. This makes it possible to collect information directly from real estate companies and owners and maintain the freshness of the data.

[0038] The selection unit can personalize the selection algorithm based on the user's past selection history or feedback. The selection unit, for example, builds a system that personalizes the selection algorithm based on the user's past selection history and feedback. For example, it learns the characteristics of properties selected in the past and reflects them in the next selection. The selection unit also analyzes the user's feedback and personalizes the selection algorithm. For example, it reflects them in the next selection based on the characteristics of properties that the user is satisfied with. The selection unit also provides a system that learns the user's preferences and tendencies based on the past selection history and feedback and personalizes the selection algorithm. For example, it prioritizes the location conditions and facilities that the user prefers. This makes it possible to personalize the selection algorithm based on the user's past selection history and feedback.

[0039] The selection unit can evaluate the energy efficiency or environmental impact of properties and prioritize eco-friendly properties. The selection unit, for example, builds a system that evaluates the energy efficiency and environmental impact of properties and prioritizes eco-friendly properties. For example, it prioritizes selecting properties with high energy efficiency or properties with environmentally friendly facilities. The selection unit also collects data on energy efficiency and environmental impact and reflects this in the evaluation of properties. For example, it gives a high rating to properties with solar power generation systems or energy-saving facilities. The selection unit also provides a system that sets evaluation standards for energy efficiency and environmental impact in order to prioritize eco-friendly properties. For example, it displays properties with high energy efficiency. This allows energy efficiency and environmental impact to be evaluated and eco-friendly properties to be prioritized.

[0040] The selection unit can provide a 3D model or virtual tour of the property, allowing the user to visually evaluate the property. For example, the selection unit provides a 3D model or virtual tour of the property, building a system that allows the user to visually evaluate the property. For example, the 3D model is used to check the interior of the property in detail. The selection unit also provides a virtual tour, allowing the user to visually evaluate the property as if they were actually visiting the property. For example, a 360-degree camera is used to photograph the interior of the property, and the virtual tour is provided. The selection unit also provides a system that allows the user to visually evaluate the property using the 3D model or virtual tour. For example, the interior of the property and the surrounding environment are checked in detail. This allows the user to visually evaluate the property.

[0041] The selection unit can evaluate the surrounding facilities or transportation access of properties and perform rankings that take into consideration convenience for daily life. The selection unit, for example, constructs a system that evaluates the surrounding facilities and transportation access of properties and performs rankings that take into consideration convenience for daily life. For example, the proximity of facilities such as schools, hospitals, and supermarkets is reflected in the evaluation. The selection unit also collects transportation access data and reflects it in the evaluation of properties. For example, the distance to the nearest station or bus stop and the convenience of transportation are reflected in the evaluation. The selection unit also provides a system that performs rankings that take into consideration convenience for daily life based on information on surrounding facilities and transportation access. For example, properties that are close to facilities necessary for daily life are given a high rating. This allows for rankings that take into consideration convenience for daily life.

[0042] The recommendation unit can reflect user feedback in real time and continuously improve the recommendation algorithm. The recommendation unit, for example, builds a system that reflects user feedback in real time and continuously improves the recommendation algorithm. For example, it adjusts the recommendation algorithm based on feedback provided by the user. The recommendation unit also analyzes user feedback and continuously improves the recommendation algorithm. For example, it reflects the features of properties that the user is satisfied with in the next recommendation. The recommendation unit also provides a system that collects feedback in real time and continuously improves the recommendation algorithm. For example, it adjusts the recommendation algorithm based on feedback provided by the user. This allows user feedback to be reflected in real time and the recommendation algorithm to continuously improve.

[0043] The recommendation unit can provide word-of-mouth or reviews of the surrounding area in addition to detailed information about the recommended property. The recommendation unit, for example, builds a system that provides word-of-mouth and reviews of the surrounding area in addition to detailed information about the recommended property. For example, the evaluation is based on the surrounding environment of the property and the opinions of residents. The recommendation unit also collects word-of-mouth and reviews of the surrounding area and reflects them in the detailed information about the recommended property. For example, the recommendation unit evaluates the property based on the opinions of residents and the reputation of the area. The recommendation unit also provides a system that provides word-of-mouth and reviews of the surrounding area in addition to detailed information about the recommended property. For example, the evaluation is based on the surrounding environment of the property and the opinions of residents. This makes it possible to provide word-of-mouth and reviews of the surrounding area in addition to detailed information about the recommended property.

[0044] The recommendation unit can provide a comparison function for recommended properties, allowing users to compare multiple properties at a glance. The recommendation unit, for example, provides a comparison function for recommended properties, and builds a system that allows users to compare multiple properties at a glance. For example, it displays a list of property prices, size, facilities, etc. The recommendation unit also provides an interface for comparing multiple properties, allowing users to easily compare properties. For example, it displays property features in a table format. The recommendation unit also provides a comparison function for recommended properties, and builds a system that allows users to compare multiple properties at a glance. For example, it displays a list of property prices, size, facilities, etc. This allows users to compare multiple properties at a glance.

[0045] The recommendation unit can provide video introductions or live streaming of recommended properties, allowing users to feel the atmosphere of the properties in real time. The recommendation unit, for example, provides video introductions or live streaming of recommended properties, building a system that allows users to feel the atmosphere of the properties in real time. For example, the interior of the properties and the surrounding environment are introduced through video. The recommendation unit also allows users to feel the atmosphere of the properties in real time through live streaming. For example, the interior of the properties are introduced through live streaming. The recommendation unit also provides a system that provides video introductions or live streaming of recommended properties, allowing users to feel the atmosphere of the properties in real time. For example, the interior of the properties and the surrounding environment are introduced through video. This allows users to feel the atmosphere of the properties in real time.

[0046] The business negotiation request unit can propose the optimal business negotiation method based on the user's past business negotiation history or feedback when a business negotiation request is made. For example, the business negotiation request unit builds a system that proposes the optimal business negotiation method based on the user's past business negotiation history and feedback when a business negotiation request is made. For example, the proposal is made based on business negotiation methods that have been successful in the past. The business negotiation request unit also analyzes the user's past business negotiation history and proposes the optimal business negotiation method. For example, the proposal is made based on the characteristics of business negotiations that have been successful in the past. The business negotiation request unit also provides a system that proposes the optimal business negotiation method to the user based on feedback. For example, the proposal is made based on a business negotiation method that satisfied the user. This makes it possible to propose the optimal business negotiation method based on the user's past business negotiation history and feedback.

[0047] The business negotiation request unit provides additional or related information about the property in real time during the business negotiation, allowing the business negotiation to proceed smoothly. The business negotiation request unit, for example, provides additional or related information about the property in real time during the business negotiation, building a system that allows the business negotiation to proceed smoothly. For example, it provides detailed information about the property and information about the surrounding environment in real time. The business negotiation request unit also provides information requested by the user in real time during the business negotiation, allowing the business negotiation to proceed smoothly. For example, it provides detailed information about the property and facilities in real time. The business negotiation request unit also provides a system that provides additional or related information about the property in real time, allowing the business negotiation to proceed smoothly. For example, it provides detailed information about the property and information about the surrounding environment in real time. This allows the business negotiation to proceed smoothly by providing additional or related information about the property in real time during the business negotiation.

[0048] The business negotiation request unit can provide a function that allows a user to flexibly set the desired business negotiation date and time or method when making a business negotiation request. The business negotiation request unit, for example, builds a system that provides a function that allows a user to flexibly set the desired business negotiation date and time and method when making a business negotiation request. For example, the user can select the desired date and time and the method of online business negotiation. The business negotiation request unit also provides an interface that allows a user to flexibly set the desired business negotiation date and time and method. For example, the business negotiation date and time is selected using a calendar function. The business negotiation request unit also provides a system that allows a user to flexibly set the desired business negotiation date and time and method when making a business negotiation request. For example, the user can select the desired date and time and the method of online business negotiation. This allows a user to flexibly set the desired business negotiation date and method.

[0049] The business negotiation request unit can share a 3D model or virtual tour of a property during an online business negotiation, allowing the user to visually check the property. The business negotiation request unit, for example, builds a system that allows the user to share a 3D model or virtual tour of a property during an online business negotiation, allowing the user to visually check the property. For example, the 3D model is used to check the interior of the property in detail. The business negotiation request unit also provides a virtual tour, allowing the user to visually check the property during the online business negotiation as if they were actually visiting the property. For example, a 360-degree camera is used to photograph the interior of the property, and the virtual tour is provided. The business negotiation request unit also provides a system that allows the user to visually check the property during the online business negotiation, using the 3D model or virtual tour. For example, the interior of the property and the surrounding environment are checked in detail. This allows the user to share a 3D model or virtual tour of the property during the online business negotiation, allowing the user to visually check the property.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The condition input unit can also suggest optimal property conditions based on the user's health and fitness data. For example, if the user uses a fitness app, the data can be analyzed to suggest properties with nearby gyms or parks. In addition, properties with elevators or barrier-free access can be prioritized based on the user's health condition. Furthermore, it can suggest properties with quiet environments based on the user's sleep data. This allows the system to suggest property conditions based on the user's health and fitness data.

[0052] The data collection unit can collect data on the energy efficiency and environmental impact of properties, and prioritize eco-friendly properties. For example, properties equipped with solar power generation systems and energy-saving equipment can be given a higher rating. It is also possible to collect data on the building materials and insulation performance of properties to select environmentally friendly properties. Furthermore, the presence of green spaces and parks in the surrounding area of ​​a property can be reflected in the evaluation. This allows eco-friendly properties to be prioritized.

[0053] The selection unit can also evaluate the security level of properties and prioritize properties with high security. For example, properties equipped with 24-hour surveillance cameras and auto-lock systems can be given a high rating. It is also possible to collect security data on the surrounding area of ​​a property and select properties in areas with high security. Furthermore, it can collect data on the property's security equipment and security services and perform a comprehensive security evaluation. This allows properties with high security to be prioritized.

[0054] The recommendation unit can also recommend properties based on the user's life events. For example, it can suggest the most suitable property based on life events such as marriage, childbirth, or job change. It can also predict future needs based on the user's life events and recommend properties that are comfortable to live in over the long term. Furthermore, it can also prioritize the recommendation of properties that are close to services and facilities related to the life events. This makes it possible to recommend properties based on the user's life events.

[0055] The business negotiation request unit provides additional information and related information about the property in real time during the business negotiation, allowing the business negotiation to proceed smoothly. For example, it provides detailed information about the property and information about the surrounding environment in real time. It also provides information that the user requests in real time during the business negotiation, allowing the business negotiation to proceed smoothly. For example, it provides detailed information about the property's price and facilities in real time. The business negotiation request unit also provides a system that provides additional information and related information about the property in real time, allowing the business negotiation to proceed smoothly. For example, it provides detailed information about the property and information about the surrounding environment in real time. This allows the system to provide additional information and related information about the property in real time during the business negotiation, allowing the business negotiation to proceed smoothly.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The condition input unit inputs the user's desired conditions. For example, the user can input conditions such as budget, size, location, layout, and facilities. Step 2: The data collection unit collects property information from real estate listings on the Internet based on the desired conditions entered by the condition input unit. For example, the data collection unit collects information such as price, area, facilities, age of the building, and location. Step 3: The selection department analyzes the property information collected by the data collection department and selects the property that best meets the desired conditions. For example, the selection department considers factors such as price, location, and facilities, and can understand the importance of each property and assign priorities. Step 4: The recommendation unit recommends the properties selected by the selection unit to the user. For example, the recommendation unit explains the features and advantages of the selected properties. Step 5: The negotiation request unit sends a negotiation request to a real estate company regarding the property that the user wants to negotiate. For example, the negotiation request unit may enable online negotiations.

[0058] (Example 2) A real estate recommendation system according to an embodiment of the present invention is a system in which a user inputs the conditions for a desired property, and a generation AI selects and recommends the most suitable property from real estate listings on the Internet. As a result, the real estate recommendation system can recommend the most suitable property based on the user's desired conditions and send a negotiation request.

[0059] A real estate recommendation system according to an embodiment includes a condition input unit, a data collection unit, a selection unit, a recommendation unit, and a negotiation request unit. The condition input unit inputs a user's desired conditions. For example, the user can input conditions such as budget, size, location, layout, and facilities. The data collection unit collects property information from online real estate listings based on the desired conditions input by the condition input unit. For example, the data collection unit collects information such as price, size, facilities, age, and location. The selection unit analyzes the property information collected by the data collection unit and selects properties that best meet the desired conditions. For example, the selection unit can understand the importance of each property and prioritize them by considering factors such as price, location, and facilities. The recommendation unit recommends the properties selected by the selection unit to the user. For example, the recommendation unit explains the features and advantages of the selected properties. The negotiation request unit sends a negotiation request to a real estate company for a property with which the user wishes to negotiate. For example, the negotiation request unit also enables online negotiations. As a result, the real estate recommendation system according to the embodiment can recommend the most suitable property based on the user's desired conditions and send a negotiation request.

[0060] The condition input unit can automatically predict and suggest desired conditions based on the user's past search history or browsing history. For example, the condition input unit analyzes the conditions of properties the user has searched for in the past and the characteristics of properties the user has viewed, and automatically suggests desired conditions the next time the user searches. For example, if the user has searched for many properties near train stations in the past, properties near train stations will be preferentially displayed the next time the user searches. The condition input unit also builds a system that automatically sets conditions such as budget, size, and layout based on the user's past search history. For example, the budget for the next search is automatically set based on the average budget of properties searched for in the past. The condition input unit also learns the characteristics of properties the user has viewed, and suggests conditions that reflect those characteristics the next time the user searches. For example, if the user has viewed many properties that allow pets in the past, properties that allow pets will be preferentially displayed the next time the user searches. This allows desired conditions to be automatically suggested based on the user's past history.

[0061] The condition input unit can analyze a user's lifestyle or hobbies and preferences and propose property conditions based on them. For example, the condition input unit collects the user's lifestyle and hobbies and preferences from questionnaires or profile information and proposes property conditions based on that. For example, for a user whose hobby is the outdoors, it proposes properties in areas with abundant natural environments. The condition input unit also analyzes public information on users' social media accounts, blogs, etc. to build a system that understands the user's lifestyle and hobbies and preferences. For example, for a user whose hobby is cooking, it proposes properties with well-equipped kitchens. The condition input unit also analyzes the user's purchase history and data on services used to identify the user's lifestyle and hobbies and preferences. For example, for a user who is a member of a fitness gym, it proposes properties that are close to a gym. In this way, property conditions can be proposed based on the user's lifestyle and hobbies and preferences.

[0062] The condition input unit can use the emotion estimation function to provide an interface for reducing stress or anxiety felt by the user while inputting. For example, when the user inputs desired conditions, the condition input unit uses the emotion estimation function to detect stress or anxiety and provide a relaxing interface. For example, relaxing music is played while the user is inputting. The condition input unit also uses the emotion estimation function to dynamically change the color or design of the interface to reduce stress or anxiety felt by the user while inputting. For example, if the user is feeling stressed, the color is changed to a calming color. The condition input unit also uses the emotion estimation function to provide an assistant function for reducing stress or anxiety felt by the user while inputting. For example, an auto-completion function is provided when inputting is difficult. This reduces the stress and anxiety felt by the user while inputting.

[0063] The condition input unit can use voice input or gesture input to enable the user to input desired conditions more intuitively. The condition input unit, for example, builds a system that allows the user to input desired conditions by voice. For example, the user inputs by voice, "budget within 30 million yen, area at least 70 square meters." The condition input unit also provides an interface that allows the user to input desired conditions by gesture. For example, the user can set the budget and area by hand movements. The condition input unit also provides an interface that combines voice input and gesture input, allowing the user to input desired conditions more intuitively. For example, the user can specify conditions by voice and make fine adjustments by gestures. This allows the user to input desired conditions more intuitively.

[0064] The condition input unit can provide a multi-user interface that allows users to jointly input conditions with family or friends. The condition input unit provides a multi-user interface that allows users to jointly input desired conditions with family and friends. For example, multiple users can input and share conditions simultaneously. The condition input unit also uses the multi-user interface to build a system that allows users to adjust conditions in real time with family and friends. For example, conditions can be set while exchanging opinions using a chat function. The condition input unit also provides a system that integrates the desired conditions of each user when entering conditions jointly with family and friends and proposes optimal conditions. For example, the system proposes optimal properties based on the desired conditions of each user. This allows users to jointly input conditions with family and friends.

[0065] The condition input unit uses the emotion estimation function to analyze the emotional response to the conditions entered by the user in real time and make suggestions that will elicit a positive response. For example, when the user enters desired conditions, the condition input unit uses the emotion estimation function to analyze the emotional response in real time and make suggestions that will elicit a positive response. For example, conditions that satisfy the user are displayed preferentially. The condition input unit also uses the emotion estimation function to analyze the emotional response to the conditions entered by the user and provide an interface for eliciting a positive response. For example, conditions that satisfy the user are highlighted. The condition input unit also analyzes the emotional response of the user in real time and provides an assistant function for eliciting a positive response. For example, conditions that satisfy the user are automatically completed. This allows the emotional response of the user to be analyzed in real time and make suggestions that will elicit a positive response.

[0066] The data collection unit not only collects real estate listings but also information on the surrounding environment or local lifestyle, allowing for comprehensive property evaluations. For example, the data collection unit uses AI to collect not only real estate listings but also information on the surrounding environment and local lifestyle, building a system for comprehensive property evaluations. For example, it collects information on schools, hospitals, supermarkets, etc. The data collection unit also collects information on the surrounding environment and local lifestyle, and reflects this in property evaluations. For example, it makes evaluations based on information such as public safety, transportation access, and noise levels. The data collection unit also integrates real estate listings and information on the surrounding environment to provide a system for comprehensive property evaluations. For example, it comprehensively evaluates the convenience and livability of the property. This allows for comprehensive property evaluations that include information on the surrounding environment and local lifestyle.

[0067] The data collection unit collects past transaction history or price fluctuation data of a property and can provide future price predictions. For example, the data collection unit builds a system in which AI collects past transaction history and price fluctuation data of a property and provides future price predictions. For example, it predicts price trends based on past transaction data. The data collection unit also analyzes past transaction history of a property and makes future price predictions. For example, it predicts future prices based on past price fluctuation data. The data collection unit also collects price fluctuation data and provides a system that provides future price predictions. For example, it predicts future prices based on past price fluctuation data. This makes it possible to predict future prices based on past transaction history and price fluctuation data of a property.

[0068] The data collection unit can use the emotion estimation function to prioritize collecting information that the user is most interested in when collecting property information. The data collection unit, for example, uses the emotion estimation function to build a system that prioritizes collecting information that the user is most interested in when collecting property information. For example, it prioritizes collecting facilities and location conditions that the user is interested in. The data collection unit also analyzes the user's emotional response in real time and prioritizes collecting information that the user is most interested in. For example, it prioritizes collecting property features that the user is interested in. The data collection unit also uses the emotion estimation function to provide a system that prioritizes collecting information that the user is most interested in. For example, it prioritizes collecting detailed information about properties that the user is interested in. This makes it possible to prioritize collecting information that the user is most interested in.

[0069] The data collection unit can use drone or satellite images to collect the latest information on the appearance of a property and its surrounding environment. The data collection unit, for example, builds a system that uses drones to collect the latest information on the appearance of a property and its surrounding environment. For example, it photographs the appearance of a property and the surrounding scenery and stores the photographs in a database. The data collection unit also uses satellite images to collect the latest information on the appearance of a property and its surrounding environment. For example, it analyzes the location of the property and the surrounding terrain and stores the data in a database. The data collection unit also provides a system that combines drone and satellite images to collect the latest information on the appearance of a property and its surrounding environment. For example, it performs a detailed analysis of the appearance of a property and surrounding facilities. This allows the collection of the latest information on the appearance of a property and its surrounding environment.

[0070] The data collection unit provides an API for collecting information directly from real estate companies or owners, thereby maintaining the freshness of the data. The data collection unit, for example, provides an API for collecting information directly from real estate companies or owners, and builds a system for maintaining the freshness of the data. For example, the latest property information is obtained in real time. The data collection unit also collects information from real estate companies and owners through the API and stores it in a database. For example, the latest information on property prices and facilities is obtained. The data collection unit also collects information directly from real estate companies and owners using the API, and provides a system for maintaining the freshness of the data. For example, the latest property information is updated regularly. This makes it possible to collect information directly from real estate companies and owners and maintain the freshness of the data.

[0071] The data collection unit can use the emotion estimation function to analyze the user's emotional response to the collected property information and preferentially display information that elicits a positive response. The data collection unit, for example, uses the emotion estimation function to analyze the user's emotional response to the collected property information and build a system that preferentially displays information that elicits a positive response. For example, it highlights property features that interest the user. The data collection unit also analyzes the user's emotional response in real time and preferentially displays information that elicits a positive response. For example, it preferentially displays detailed information about properties that satisfy the user. The data collection unit also uses the emotion estimation function to provide a system that analyzes the user's emotional response to the collected property information and preferentially displays information that elicits a positive response. For example, it highlights property features that interest the user. This makes it possible to analyze the user's emotional response and preferentially display information that elicits a positive response.

[0072] The selection unit can personalize the selection algorithm based on the user's past selection history or feedback. The selection unit, for example, builds a system that personalizes the selection algorithm based on the user's past selection history and feedback. For example, it learns the characteristics of properties selected in the past and reflects them in the next selection. The selection unit also analyzes the user's feedback and personalizes the selection algorithm. For example, it reflects them in the next selection based on the characteristics of properties that the user is satisfied with. The selection unit also provides a system that learns the user's preferences and tendencies based on the past selection history and feedback and personalizes the selection algorithm. For example, it prioritizes the location conditions and facilities that the user prefers. This makes it possible to personalize the selection algorithm based on the user's past selection history and feedback.

[0073] The selection unit can evaluate the energy efficiency or environmental impact of properties and prioritize eco-friendly properties. The selection unit, for example, builds a system that evaluates the energy efficiency and environmental impact of properties and prioritizes eco-friendly properties. For example, it prioritizes selecting properties with high energy efficiency or properties with environmentally friendly facilities. The selection unit also collects data on energy efficiency and environmental impact and reflects this in the evaluation of properties. For example, it gives a high rating to properties with solar power generation systems or energy-saving facilities. The selection unit also provides a system that sets evaluation standards for energy efficiency and environmental impact in order to prioritize eco-friendly properties. For example, it displays properties with high energy efficiency. This allows energy efficiency and environmental impact to be evaluated and eco-friendly properties to be prioritized.

[0074] The selection unit can use the emotion estimation function to prioritize ranking properties for which the user feels the most positive emotions. The selection unit, for example, uses the emotion estimation function to build a system that prioritizes ranking properties for which the user feels the most positive emotions. For example, the ranking is performed based on the characteristics of properties that satisfy the user. The selection unit also analyzes the user's emotional responses in real time and prioritizes ranking properties that elicit positive emotions. For example, the ranking is performed based on the characteristics of properties that interest the user. The selection unit also uses the emotion estimation function to provide a system that prioritizes ranking properties for which the user feels the most positive emotions. For example, the ranking is performed based on detailed information about properties that satisfy the user. This makes it possible to prioritize ranking properties for which the user feels the most positive emotions.

[0075] The selection unit can provide a 3D model or virtual tour of the property, allowing the user to visually evaluate the property. For example, the selection unit provides a 3D model or virtual tour of the property, building a system that allows the user to visually evaluate the property. For example, the 3D model is used to check the interior of the property in detail. The selection unit also provides a virtual tour, allowing the user to visually evaluate the property as if they were actually visiting the property. For example, a 360-degree camera is used to photograph the interior of the property, and the virtual tour is provided. The selection unit also provides a system that allows the user to visually evaluate the property using the 3D model or virtual tour. For example, the interior of the property and the surrounding environment are checked in detail. This allows the user to visually evaluate the property.

[0076] The selection unit can evaluate the surrounding facilities or transportation access of properties and perform rankings that take into consideration convenience for daily life. The selection unit, for example, constructs a system that evaluates the surrounding facilities and transportation access of properties and performs rankings that take into consideration convenience for daily life. For example, the proximity of facilities such as schools, hospitals, and supermarkets is reflected in the evaluation. The selection unit also collects transportation access data and reflects it in the evaluation of properties. For example, the distance to the nearest station or bus stop and the convenience of transportation are reflected in the evaluation. The selection unit also provides a system that performs rankings that take into consideration convenience for daily life based on information on surrounding facilities and transportation access. For example, properties that are close to facilities necessary for daily life are given a high rating. This allows for rankings that take into consideration convenience for daily life.

[0077] The selection unit uses the emotion estimation function to analyze the emotional response of the user when viewing a property in real time, and can preferentially display properties that elicit a positive response. The selection unit, for example, uses the emotion estimation function to build a system that analyzes the emotional response of the user when viewing a property in real time, and preferentially displays properties that elicit a positive response. For example, it highlights features of properties that interest the user. The selection unit also analyzes the emotional response of the user in real time, and preferentially displays properties that elicit a positive response. For example, it preferentially displays detailed information about properties that satisfy the user. The selection unit also provides a system that uses the emotion estimation function to analyze the emotional response of the user when viewing a property in real time, and preferentially displays properties that elicit a positive response. For example, it highlights features of properties that interest the user. This makes it possible to analyze the emotional response of the user in real time, and preferentially display properties that elicit a positive response.

[0078] The recommendation unit can reflect user feedback in real time and continuously improve the recommendation algorithm. The recommendation unit, for example, builds a system that reflects user feedback in real time and continuously improves the recommendation algorithm. For example, it adjusts the recommendation algorithm based on feedback provided by the user. The recommendation unit also analyzes user feedback and continuously improves the recommendation algorithm. For example, it reflects the features of properties that the user is satisfied with in the next recommendation. The recommendation unit also provides a system that collects feedback in real time and continuously improves the recommendation algorithm. For example, it adjusts the recommendation algorithm based on feedback provided by the user. This allows user feedback to be reflected in real time and the recommendation algorithm to continuously improve.

[0079] The recommendation unit can provide word-of-mouth or reviews of the surrounding area in addition to detailed information about the recommended property. The recommendation unit, for example, builds a system that provides word-of-mouth and reviews of the surrounding area in addition to detailed information about the recommended property. For example, the evaluation is based on the surrounding environment of the property and the opinions of residents. The recommendation unit also collects word-of-mouth and reviews of the surrounding area and reflects them in the detailed information about the recommended property. For example, the recommendation unit evaluates the property based on the opinions of residents and the reputation of the area. The recommendation unit also provides a system that provides word-of-mouth and reviews of the surrounding area in addition to detailed information about the recommended property. For example, the evaluation is based on the surrounding environment of the property and the opinions of residents. This makes it possible to provide word-of-mouth and reviews of the surrounding area in addition to detailed information about the recommended property.

[0080] The recommendation unit can use the emotion estimation function to provide a recommendation method that will evoke the most positive emotions in the user. The recommendation unit, for example, uses the emotion estimation function to build a system that provides a recommendation method that will evoke the most positive emotions in the user. For example, recommendations are made based on the features of properties that satisfy the user. The recommendation unit also analyzes the user's emotional responses in real time to provide a recommendation method that elicits positive emotions. For example, recommendations are made based on the features of properties that interest the user. The recommendation unit also uses the emotion estimation function to provide a system that provides a recommendation method that will evoke the most positive emotions in the user. For example, recommendations are made based on detailed information about properties that satisfy the user. This makes it possible to provide a recommendation method that will evoke the most positive emotions in the user.

[0081] The recommendation unit can provide a comparison function for recommended properties, allowing users to compare multiple properties at a glance. The recommendation unit, for example, provides a comparison function for recommended properties, and builds a system that allows users to compare multiple properties at a glance. For example, it displays a list of property prices, size, facilities, etc. The recommendation unit also provides an interface for comparing multiple properties, allowing users to easily compare properties. For example, it displays property features in a table format. The recommendation unit also provides a comparison function for recommended properties, and builds a system that allows users to compare multiple properties at a glance. For example, it displays a list of property prices, size, facilities, etc. This allows users to compare multiple properties at a glance.

[0082] The recommendation unit can provide video introductions or live streaming of recommended properties, allowing users to feel the atmosphere of the properties in real time. The recommendation unit, for example, provides video introductions or live streaming of recommended properties, building a system that allows users to feel the atmosphere of the properties in real time. For example, the interior of the properties and the surrounding environment are introduced through video. The recommendation unit also allows users to feel the atmosphere of the properties in real time through live streaming. For example, the interior of the properties are introduced through live streaming. The recommendation unit also provides a system that provides video introductions or live streaming of recommended properties, allowing users to feel the atmosphere of the properties in real time. For example, the interior of the properties and the surrounding environment are introduced through video. This allows users to feel the atmosphere of the properties in real time.

[0083] The recommendation unit uses the emotion estimation function to analyze the emotional response of the user when viewing recommended properties in real time, and can prioritize displaying properties that elicit a positive response. The recommendation unit, for example, uses the emotion estimation function to analyze the emotional response of the user when viewing recommended properties in real time, and builds a system that prioritizes displaying properties that elicit a positive response. For example, it highlights features of properties that interest the user. The recommendation unit also analyzes the user's emotional response in real time, and prioritizes displaying properties that elicit a positive response. For example, it prioritizes displaying detailed information about properties that satisfy the user. The recommendation unit also uses the emotion estimation function to provide a system that analyzes the emotional response of the user when viewing recommended properties in real time, and prioritizes displaying properties that elicit a positive response. For example, it highlights features of properties that interest the user. This makes it possible to analyze the user's emotional response in real time, and prioritize displaying properties that elicit a positive response.

[0084] The business negotiation request unit can propose the optimal business negotiation method based on the user's past business negotiation history or feedback when a business negotiation request is made. For example, the business negotiation request unit builds a system that proposes the optimal business negotiation method based on the user's past business negotiation history and feedback when a business negotiation request is made. For example, the proposal is made based on business negotiation methods that have been successful in the past. The business negotiation request unit also analyzes the user's past business negotiation history and proposes the optimal business negotiation method. For example, the proposal is made based on the characteristics of business negotiations that have been successful in the past. The business negotiation request unit also provides a system that proposes the optimal business negotiation method to the user based on feedback. For example, the proposal is made based on a business negotiation method that satisfied the user. This makes it possible to propose the optimal business negotiation method based on the user's past business negotiation history and feedback.

[0085] The business negotiation request unit provides additional or related information about the property in real time during the business negotiation, allowing the business negotiation to proceed smoothly. The business negotiation request unit, for example, provides additional or related information about the property in real time during the business negotiation, building a system that allows the business negotiation to proceed smoothly. For example, it provides detailed information about the property and information about the surrounding environment in real time. The business negotiation request unit also provides information requested by the user in real time during the business negotiation, allowing the business negotiation to proceed smoothly. For example, it provides detailed information about the property and facilities in real time. The business negotiation request unit also provides a system that provides additional or related information about the property in real time, allowing the business negotiation to proceed smoothly. For example, it provides detailed information about the property and information about the surrounding environment in real time. This allows the business negotiation to proceed smoothly by providing additional or related information about the property in real time during the business negotiation.

[0086] The business negotiation request unit uses the emotion estimation function to analyze the emotional response of a user during business negotiations and can provide a business negotiation method that elicits a positive response. The business negotiation request unit, for example, uses the emotion estimation function to analyze the emotional response of a user during business negotiations and builds a system that provides a business negotiation method that elicits a positive response. For example, a proposal is made based on a business negotiation method that satisfies the user. The business negotiation request unit also analyzes the emotional response of a user in real time and provides a business negotiation method that elicits a positive response. For example, the business negotiation progresses based on the characteristics of a property in which the user is interested. The business negotiation request unit also uses the emotion estimation function to analyze the emotional response of a user during business negotiations and provides a system that provides a business negotiation method that elicits a positive response. For example, a proposal is made based on a business negotiation method that satisfies the user. This makes it possible to analyze the emotional response of a user during business negotiations and provide a business negotiation method that elicits a positive response.

[0087] The business negotiation request unit can provide a function that allows a user to flexibly set the desired business negotiation date and time or method when making a business negotiation request. The business negotiation request unit, for example, builds a system that provides a function that allows a user to flexibly set the desired business negotiation date and time and method when making a business negotiation request. For example, the user can select the desired date and time and the method of online business negotiation. The business negotiation request unit also provides an interface that allows a user to flexibly set the desired business negotiation date and time and method. For example, the business negotiation date and time is selected using a calendar function. The business negotiation request unit also provides a system that allows a user to flexibly set the desired business negotiation date and time and method when making a business negotiation request. For example, the user can select the desired date and time and the method of online business negotiation. This allows a user to flexibly set the desired business negotiation date and method.

[0088] The business negotiation request unit can share a 3D model or virtual tour of a property during an online business negotiation, allowing the user to visually check the property. The business negotiation request unit, for example, builds a system that allows the user to share a 3D model or virtual tour of a property during an online business negotiation, allowing the user to visually check the property. For example, the 3D model is used to check the interior of the property in detail. The business negotiation request unit also provides a virtual tour, allowing the user to visually check the property during the online business negotiation as if they were actually visiting the property. For example, a 360-degree camera is used to photograph the interior of the property, and the virtual tour is provided. The business negotiation request unit also provides a system that allows the user to visually check the property during the online business negotiation, using the 3D model or virtual tour. For example, the interior of the property and the surrounding environment are checked in detail. This allows the user to share a 3D model or virtual tour of the property during the online business negotiation, allowing the user to visually check the property.

[0089] The business negotiation request unit uses the emotion estimation function to analyze the emotional responses of a user during business negotiations in real time, and can provide a business negotiation method that elicits a positive response. The business negotiation request unit, for example, uses the emotion estimation function to build a system that analyzes the emotional responses of a user during business negotiations in real time, and provides a business negotiation method that elicits a positive response. For example, a proposal is made based on a business negotiation method that satisfies the user. The business negotiation request unit also analyzes the emotional responses of a user in real time, and provides a business negotiation method that elicits a positive response. For example, the business negotiation request unit progresses the business negotiation based on the characteristics of a property in which the user is interested. The business negotiation request unit also uses the emotion estimation function to analyze the emotional responses of a user during business negotiations in real time, and provides a system that provides a business negotiation method that elicits a positive response. For example, a proposal is made based on a business negotiation method that satisfies the user. This makes it possible to provide a business negotiation method that analyzes the emotional responses of a user during business negotiations in real time, and

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The condition input unit can also suggest optimal property conditions based on the user's health and fitness data. For example, if the user uses a fitness app, the data can be analyzed to suggest properties with nearby gyms or parks. In addition, properties with elevators or barrier-free access can be prioritized based on the user's health condition. Furthermore, it can suggest properties with quiet environments based on the user's sleep data. This allows the system to suggest property conditions based on the user's health and fitness data.

[0092] The data collection unit can collect data on the energy efficiency and environmental impact of properties, and prioritize eco-friendly properties. For example, properties equipped with solar power generation systems and energy-saving equipment can be given a higher rating. It is also possible to collect data on the building materials and insulation performance of properties to select environmentally friendly properties. Furthermore, the presence of green spaces and parks in the surrounding area of ​​a property can be reflected in the evaluation. This allows eco-friendly properties to be prioritized.

[0093] The selection unit can also evaluate the security level of properties and prioritize properties with high security. For example, properties equipped with 24-hour surveillance cameras and auto-lock systems can be given a high rating. It is also possible to collect security data on the surrounding area of ​​a property and select properties in areas with high security. Furthermore, it can collect data on the property's security equipment and security services and perform a comprehensive security evaluation. This allows properties with high security to be prioritized.

[0094] The recommendation unit can also recommend properties based on the user's life events. For example, it can suggest the most suitable property based on life events such as marriage, childbirth, or job change. It can also predict future needs based on the user's life events and recommend properties that are comfortable to live in over the long term. Furthermore, it can also prioritize the recommendation of properties that are close to services and facilities related to the life events. This makes it possible to recommend properties based on the user's life events.

[0095] The business negotiation request unit provides additional information and related information about the property in real time during the business negotiation, allowing the business negotiation to proceed smoothly. For example, it provides detailed information about the property and information about the surrounding environment in real time. It also provides information that the user requests in real time during the business negotiation, allowing the business negotiation to proceed smoothly. For example, it provides detailed information about the property's price and facilities in real time. The business negotiation request unit also provides a system that provides additional information and related information about the property in real time, allowing the business negotiation to proceed smoothly. For example, it provides detailed information about the property and information about the surrounding environment in real time. This allows the system to provide additional information and related information about the property in real time during the business negotiation, allowing the business negotiation to proceed smoothly.

[0096] The condition input unit can use the emotion estimation function to provide an interface for reducing stress or anxiety felt by the user while inputting. For example, when the user inputs desired conditions, the emotion estimation function can be used to detect stress or anxiety, and a relaxing interface can be provided. For example, relaxing music can be played while the user is inputting. The condition input unit can also use the emotion estimation function to dynamically change the color or design of the interface to reduce stress or anxiety felt by the user while inputting. For example, if the user is feeling stressed, the color can be changed to a calming color. The condition input unit can also use the emotion estimation function to provide an assistant function for reducing stress or anxiety felt by the user while inputting. For example, an auto-completion function can be provided when inputting is difficult. This can reduce the stress and anxiety felt by the user while inputting.

[0097] The data collection unit can use the emotion estimation function to prioritize collecting information that the user is most interested in when collecting property information. For example, it prioritizes collecting facilities and location conditions that the user is interested in. The data collection unit also analyzes the user's emotional reactions in real time and prioritizes collecting information that the user is most interested in. For example, it prioritizes collecting property features that the user is interested in. The data collection unit also provides a system that uses the emotion estimation function to prioritize collecting information that the user is most interested in. For example, it prioritizes collecting detailed information about properties that the user is interested in. This allows the information that the user is most interested in to be prioritized.

[0098] The selection unit can use the emotion estimation function to prioritize ranking properties that evoke the most positive emotions in the user. For example, the ranking is performed based on the characteristics of properties that satisfy the user. The selection unit also analyzes the user's emotional responses in real time and prioritizes ranking properties that elicit positive emotions. For example, the ranking is performed based on the characteristics of properties that interest the user. The selection unit also provides a system that uses the emotion estimation function to prioritize ranking properties that evoke the most positive emotions in the user. For example, the ranking is performed based on detailed information about properties that satisfy the user. This allows the system to prioritize ranking properties that evoke the most positive emotions in the user.

[0099] The recommendation unit can use the emotion estimation function to provide a recommendation method that will evoke the most positive emotions in the user. For example, recommendations are made based on the characteristics of properties that satisfy the user. The recommendation unit also analyzes the user's emotional responses in real time to provide a recommendation method that will elicit positive emotions. For example, recommendations are made based on the characteristics of properties that interest the user. The recommendation unit also uses the emotion estimation function to provide a system that provides a recommendation method that will evoke the most positive emotions in the user. For example, recommendations are made based on detailed information about properties that satisfy the user. This makes it possible to provide a recommendation method that will evoke the most positive emotions in the user.

[0100] The business negotiation request unit uses the emotion estimation function to analyze the emotional responses of the user during business negotiations in real time, and can provide a business negotiation method that elicits a positive response. For example, a proposal is made based on a business negotiation method that satisfies the user. The business negotiation request unit also analyzes the emotional responses of the user in real time, and provides a business negotiation method that elicits a positive response. For example, the business negotiation progresses based on the characteristics of a property in which the user is interested. The business negotiation request unit also uses the emotion estimation function to provide a system that analyzes the emotional responses of the user during business negotiations in real time, and provides a business negotiation method that elicits a positive response. For example, a proposal is made based on a business negotiation method that satisfies the user. This makes it possible to analyze the emotional responses of the user during business negotiations in real time, and provide a business negotiation method that elicits a positive response.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The condition input unit inputs the user's desired conditions. For example, the user can input conditions such as budget, size, location, layout, and facilities. Step 2: The data collection unit collects property information from real estate listings on the Internet based on the desired conditions entered by the condition input unit. For example, the data collection unit collects information such as price, area, facilities, age of the building, and location. Step 3: The selection department analyzes the property information collected by the data collection department and selects the property that best meets the desired conditions. For example, the selection department considers factors such as price, location, and facilities, and can understand the importance of each property and assign priorities. Step 4: The recommendation unit recommends the properties selected by the selection unit to the user. For example, the recommendation unit explains the features and advantages of the selected properties. Step 5: The negotiation request unit sends a negotiation request to a real estate company regarding the property that the user wants to negotiate. For example, the negotiation request unit may enable online negotiations.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

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

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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]

[0170] 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 condition input section for inputting desired conditions of the user; a data collection unit that collects property information from real estate lists on the Internet based on the desired conditions input by the condition input unit; a selection unit that analyzes the property information collected by the data collection unit and selects a property that best meets the desired conditions; a recommendation unit that recommends the property selected by the selection unit to a user; a negotiation request unit that transmits a negotiation request to a real estate company regarding a property that the user wants to negotiate on; A system characterized by:

2. The condition input unit To provide an interface for reducing stress or anxiety felt by the user while inputting data 2. The system of claim 1.

3. The data collection unit When collecting the property information, information that the user is most interested in is collected preferentially.

2. The system of claim 1.

4. The sorting unit is The property for which the user has the most positive feelings is ranked preferentially.

2. The system of claim 1.

5. The recommendation unit To provide a recommendation method that gives the user the most positive feelings 2. The system of claim 1.

6. The business negotiation request unit To provide a business negotiation method that analyzes the emotional reaction of the user during the business negotiation and elicits a positive reaction.

2. The system of claim 1.

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