System

Abstract

An object of a system according to an embodiment is to perform total coordination of an optimal property based on a lifestyle of a user.SOLUTION: A system includes a lifestyle information collection unit, a property information collection unit, a coordination unit, and a feedback unit. The lifestyle information collection unit collects lifestyle information of a user. The property information collection unit collects property information. The coordination unit performs total coordination of properties optimal for the user on the basis of the information collected by the lifestyle information collection unit and the property information collection unit. The feedback unit receives feedback from the user and customizes the proposal content.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 technologies do not adequately coordinate properties based on the user's lifestyle, and there is room for improvement.

[0005] The system according to the embodiment aims to provide total coordination of the most suitable property based on the user's lifestyle. [Means for solving the problem]

[0006] The system according to the embodiment includes a lifestyle information collection unit, a property information collection unit, a coordination unit, and a feedback unit. The lifestyle information collection unit collects lifestyle information about the user. The property information collection unit collects property information. The coordination unit coordinates a property that is optimal for the user based on the information collected by the lifestyle information collection unit and the property information collection unit. The feedback unit receives feedback from the user and customizes the proposed content. [Effects of the Invention]

[0007] The system according to the embodiment can provide total coordination of the most suitable property based on the user's lifestyle. [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) The AI ​​system according to the embodiment of the present invention collects lifestyle information of a user, analyzes property information, and proposes a total coordinated property that is optimal for the user. As a result, the AI ​​system proposes properties that are optimal for the user's lifestyle, thereby improving user satisfaction.

[0029] The AI ​​system according to the embodiment includes a lifestyle information collection unit, a property information collection unit, a coordination unit, and a feedback unit. The lifestyle information collection unit collects lifestyle information about the user. For example, the information includes information about the user's preferred holiday activities, work style (e.g., remote work, frequency of commuting), specific lifestyle preferences (e.g., eco-friendly living, living with pets), and hobbies (e.g., sports, art, cooking). The property information collection unit collects property information. For example, the information includes not only the property's layout and facilities, rent, and nearest train lines, but also surrounding facilities (e.g., parks, gyms, cafes, hospitals), local safety, transportation access, and educational environment. The coordination unit coordinates the optimal property for the user based on the information collected by the lifestyle information collection unit and the property information collection unit. For example, for a user who primarily works remotely, the coordination unit suggests a property with high-speed internet access in a quiet environment. For a user who enjoys the outdoors, the coordination unit suggests a property in an area with nearby parks and abundant nature. The feedback unit receives feedback from the user and customizes the suggestions. For example, if a user provides feedback on a proposed property such as "I want a larger living room" or "I prefer a property closer to the station," the feedback unit will perform further analysis based on that feedback and propose a more suitable property. This allows the AI ​​system according to the embodiment to propose properties that are optimal for the user's lifestyle and improve user satisfaction.

[0030] The lifestyle information collection unit can automatically collect lifestyle information from a user's social media or blog and analyze it using a generation AI. The lifestyle information collection unit, for example, analyzes the content of a user's social media posts to understand lifestyle trends. For example, it identifies a user's preferred lifestyle based on the locations and activities that the user frequently posts. The lifestyle information collection unit also automatically collects a user's blog posts and performs text analysis to extract detailed lifestyle information. For example, it identifies hobbies and interests from travelogues and daily events. The lifestyle information collection unit also analyzes image data from social media and blogs to collect visual information about the user's lifestyle. For example, it identifies outdoor activities and cooking hobbies from posted photos. This allows for more accurate lifestyle information to be obtained by automatically collecting and analyzing lifestyle information from a user's social media or blog.

[0031] The lifestyle information collection unit can analyze a user's past purchase history or search history to identify lifestyle information trends. The lifestyle information collection unit, for example, analyzes the user's online shopping purchase history to identify lifestyle trends. For example, it identifies hobbies and interests from the categories and brands of purchased products. The lifestyle information collection unit also analyzes the user's search history to identify topics and activities of interest. For example, it extracts lifestyle characteristics from frequently searched keywords and sites. The lifestyle information collection unit also integrates the user's purchase history and search history to identify more detailed lifestyle trends. For example, it analyzes the relevance between purchased products and searched information. In this way, lifestyle trends can be more accurately identified by analyzing the user's purchase history and search history.

[0032] The lifestyle information collection unit can collect lifestyle information about a user using voice input or image analysis. The lifestyle information collection unit, for example, builds a system that allows a user to provide lifestyle information through voice input and performs voice analysis. For example, it converts what the user says into text and analyzes lifestyle trends. The lifestyle information collection unit also analyzes images uploaded by the user to collect visual information about the lifestyle. For example, it identifies hobbies and interests from travel photos or food photos. The lifestyle information collection unit also combines voice input and image analysis to develop a system that collects lifestyle information about a user from multiple angles. For example, it analyzes images related to content explained in voice. In this way, it is possible to collect lifestyle information about a user from multiple angles using voice input and image analysis.

[0033] The lifestyle information collection unit can collect lifestyle information from different cultural spheres or regions and analyze it from a global perspective. The lifestyle information collection unit, for example, builds a system that collects lifestyle information from different cultural spheres or regions and analyzes it from a global perspective. For example, it collects information from social media and blogs in various countries. The lifestyle information collection unit also compares lifestyle information from different cultural spheres and analyzes similarities and differences. For example, it identifies activities and hobbies that are popular in a particular cultural sphere. The lifestyle information collection unit also develops a system that analyzes lifestyle information from a global perspective and makes optimal suggestions to users. For example, it makes suggestions that combine lifestyle elements from different cultural spheres. This makes it possible to make more diverse suggestions by collecting lifestyle information from different cultural spheres and analyzing it from a global perspective.

[0034] The property information collection unit can collect reviews or ratings from past residents of a property and analyze them using a generation AI. For example, the property information collection unit builds a system that automatically collects reviews and ratings posted by past residents of a property and analyzes them using a generation AI. For example, it collects information from review sites and social media. The property information collection unit also performs text analysis of the reviews and ratings from past residents to extract the features and problems of the property. For example, it classifies positive and negative ratings. The property information collection unit also develops a system that uses a generation AI to calculate an overall rating of the property based on the collected reviews and ratings. For example, it creates a ranking of properties based on the rating score. This makes it easier to understand the features and problems of a property by analyzing the reviews and ratings from past residents.

[0035] The property information collection unit can collect and analyze changes in the surrounding environment of a property in real time. The property information collection unit, for example, builds a system that collects changes in the surrounding environment of a property in real time. For example, it automatically collects information on the opening of new facilities and changes in public safety. The property information collection unit also analyzes changes in the surrounding environment and evaluates the impact on the value and convenience of the property. For example, it analyzes whether the opening of a new facility will increase the attractiveness of the property. The property information collection unit also develops a system that updates the evaluation of a property in real time based on the collected information on changes in the surrounding environment. For example, it reflects the impact that changes in public safety have on the evaluation of a property. In this way, by collecting and analyzing changes in the surrounding environment in real time, it becomes easier to evaluate the value and convenience of a property.

[0036] The property information collection unit can collect detailed information about the surrounding environment of a property using drone or satellite images. The property information collection unit, for example, builds a system that uses drones to collect detailed information about the surrounding environment of a property. For example, it analyzes images taken by a drone to understand surrounding facilities and scenery. The property information collection unit also collects and analyzes information about the surrounding environment of a property using satellite images. For example, it understands the accessibility of transportation and the status of green spaces from satellite images. The property information collection unit also develops a system that combines drone and satellite images to collect detailed information about the surrounding environment of a property. For example, it integrates and analyzes images taken by a drone with satellite images. In this way, detailed information about the surrounding environment of a property can be collected using drones and satellite images.

[0037] The property information collection unit can compare property information from different cities or countries and analyze it from a global perspective. The property information collection unit, for example, builds a system that collects property information from different cities or countries and analyzes it from a global perspective. For example, it collects information from real estate databases in each country. The property information collection unit also compares property information from different cities or countries and analyzes similarities and differences. For example, it analyzes differences in rent and facilities. The property information collection unit also develops a system that analyzes property information from a global perspective and makes optimal suggestions to users. For example, it makes suggestions that combine property elements from different cities or countries. This makes it possible to compare property information from different cities or countries and analyze it from a global perspective, making more diverse suggestions possible.

[0038] The coordination department can predict future changes in a user's lifestyle and make property suggestions based on that. For example, the coordination department will build a system in which a generative AI analyzes a user's past data and predicts future lifestyle changes. For example, it will take into account changes in age and family composition. The coordination department will also predict future lifestyle changes and make property suggestions based on that. For example, it will suggest larger properties taking into account future increases in children. The coordination department will also develop a system that predicts changes in a user's lifestyle in real time and dynamically adjusts the content of suggestions. For example, it will update property suggestions according to life events. This makes it possible to predict future changes in a user's lifestyle and suggest properties that meet future needs.

[0039] The coordination department can make property suggestions taking into account the user's health condition or fitness data. For example, the coordination department collects the user's health condition and fitness data, and builds a system in which the generation AI makes property suggestions based on that data. For example, the coordination department can suggest properties near gyms and parks to health-conscious users. The coordination department can also analyze fitness data to suggest properties that match the user's exercise habits and health goals. For example, it can suggest properties near running courses. The coordination department can also monitor the user's health condition in real time and develop a system that dynamically adjusts property suggestions based on that information. For example, it can update the suggestions according to changes in the user's health condition. This makes it possible to suggest properties that are suitable for health-conscious users by taking into account the user's health condition and fitness data.

[0040] The coordination unit can make property suggestions taking into account the user's family composition or pet information. For example, the coordination unit collects information about the user's family composition and pets, and builds a system in which the generation AI makes property suggestions based on that information. For example, it suggests properties with spacious living rooms and children's rooms for families with children. The coordination unit also takes pet information into account to suggest pet-friendly properties. For example, it suggests properties that allow pets or properties with nearby pet facilities. The coordination unit also monitors family composition and pet information in real time, and develops a system that dynamically adjusts property suggestions based on that information. For example, it updates the suggestions according to changes in family composition. This makes it possible to suggest properties that are suitable for families and pets by taking into account the user's family composition and pet information.

[0041] The coordination unit can match users with different lifestyles and propose joint property sharing. The coordination unit, for example, builds a system that matches users with different lifestyles and proposes joint property sharing. For example, it matches remote workers with outdoor enthusiasts. The coordination unit also matches compatible users based on users' lifestyle information and proposes shared properties. For example, it matches users with common hobbies and interests. The coordination unit also develops a system that analyzes the compatibility of users' lifestyles in real time when proposing shared properties and makes optimal matches. For example, it scores the degree of similarity of lifestyles. This makes it possible to propose joint property sharing by matching users with different lifestyles.

[0042] The feedback unit analyzes feedback from users, allowing the generation AI to automatically learn and improve the proposals. For example, the feedback unit collects feedback from users and builds a system in which the generation AI automatically learns and improves the proposals based on that data. For example, it performs text analysis of the feedback content. The feedback unit also analyzes the feedback data and identifies areas for improvement in the proposals. For example, it extracts the features and conditions of the property that the user is looking for. The feedback unit also develops a system in which the generation AI dynamically adjusts the proposals based on user feedback. For example, it updates the proposals in real time in response to the feedback. In this way, the generation AI analyzes user feedback and automatically learns and improves the proposals, thereby improving the accuracy of the proposals.

[0043] The feedback unit can develop a new algorithm for the generation AI to improve the accuracy of its suggestions based on user feedback. The feedback unit, for example, analyzes user feedback data and develops a new algorithm for the generation AI to improve the accuracy of its suggestions. For example, it improves the algorithm based on the feedback. The feedback unit also builds a system to evaluate the accuracy of the suggestions based on the feedback data. For example, it analyzes user satisfaction scores. The feedback unit also develops a new algorithm and builds a feedback loop to improve the accuracy of the suggestions. For example, it regularly evaluates and improves the performance of the algorithm. In this way, the accuracy of the suggestions is improved by developing a new algorithm based on user feedback.

[0044] The feedback department can also refer to the feedback of other users when the generation AI customizes the proposal content based on user feedback. For example, the feedback department collects feedback data from other users and builds a system in which the generation AI uses it as a reference to customize the proposal content. For example, similar feedback is grouped. The feedback department also analyzes the feedback of other users and extracts common improvements and requests. For example, it identifies the property features desired by multiple users. The feedback department also develops a system that dynamically adjusts the proposal content based on the feedback of other users. For example, it analyzes feedback trends in real time and reflects them in the proposal content. In this way, the accuracy of the proposal content is improved by also referring to the feedback of other users.

[0045] The feedback unit can also take feedback from different cultural spheres and regions into consideration when the generation AI customizes its proposals based on user feedback. For example, the feedback unit collects feedback from users in different cultural spheres and regions, and builds a system in which the generation AI uses that feedback to customize its proposals. For example, it takes into account the characteristics of each region. The feedback unit also analyzes feedback from different cultural spheres and extracts common improvements and requests. For example, it identifies the characteristics of properties that are popular in specific cultural spheres. The feedback unit also develops a system that dynamically adjusts the proposals based on feedback from different cultural spheres and regions. For example, it analyzes feedback trends by region in real time and reflects them in the proposals. This improves the accuracy of the proposals by taking feedback from different cultural spheres and regions into consideration.

[0046] The coordination unit can perform total coordination of living areas taking into account local event information or community activities. For example, the coordination unit collects local event information and community activities, and builds a system in which the generation AI performs total coordination of living areas based on that information. For example, it analyzes the local event calendar. The coordination unit also analyzes community activity information and suggests areas that suit the user's lifestyle. For example, it suggests areas where outdoor activities are popular. The coordination unit also collects local event information and community activities in real time, and develops a system that dynamically adjusts living area suggestions based on that information. For example, it updates the suggestions depending on the event status. This makes it possible to suggest living areas that are suitable for the user by taking into account local event information and community activities.

[0047] The coordination unit can coordinate living areas in a comprehensive manner, taking into account local climate or seasonal changes. For example, the coordination unit collects local climate data and seasonal changes, and builds a system in which the generation AI uses this data to coordinate living areas in a comprehensive manner. For example, it analyzes annual temperature and precipitation. The coordination unit also considers seasonal climate changes and suggests areas that suit the user's lifestyle. For example, it suggests areas where you can enjoy skiing in winter and areas close to the beach in summer. The coordination unit also collects local climate and seasonal changes in real time, and develops a system that dynamically adjusts living area suggestions based on that information. For example, it updates the suggestions by taking seasonal events and activities into account. This makes it possible to suggest living areas that are suitable for the user by taking into account local climate and seasonal changes.

[0048] The coordination unit can perform total coordination of living areas taking into account the user's commuting time or means of transportation. For example, the coordination unit collects the user's commuting time and means of transportation, and the generation AI builds a system that performs total coordination of living areas based on that information. For example, it suggests areas where commuting time can be shortened. The coordination unit also analyzes transportation information and suggests areas that suit the user's lifestyle. For example, it suggests areas with good public transportation or areas where bicycle commuting is possible. The coordination unit also collects the user's commuting time and means of transportation in real time, and develops a system that dynamically adjusts living area suggestions based on that information. For example, it updates the suggestions according to changes in traffic conditions. This makes it possible to suggest living areas that are convenient for commuting by taking into account the user's commuting time and means of transportation.

[0049] The coordination unit can compare the characteristics of different living areas and suggest the most suitable area to the user. For example, the coordination unit collects the characteristics of different living areas, and the generation AI builds a system that uses this information to suggest the most suitable area to the user. For example, it compares the facilities and environment of each area. The coordination unit also analyzes the characteristics of different areas and suggests areas that suit the user's lifestyle. For example, it suggests areas with good educational environments or areas with abundant shopping facilities. The coordination unit also collects the characteristics of different living areas in real time, and develops a system that dynamically suggests the most suitable area to the user based on this. For example, it updates the suggestions taking into account the development status of the area and information on the opening of new facilities. This makes it possible to suggest the most suitable area to the user by comparing the characteristics of different living areas.

[0050] The coordination unit can compare the characteristics of different living areas and suggest the most suitable area to the user. For example, the coordination unit collects the characteristics of different living areas, and the generation AI builds a system that uses this information to suggest the most suitable area to the user. For example, it compares the facilities and environment of each area. The coordination unit also analyzes the characteristics of different areas and suggests areas that suit the user's lifestyle. For example, it suggests areas with good educational environments or areas with abundant shopping facilities. The coordination unit also collects the characteristics of different living areas in real time, and develops a system that dynamically suggests the most suitable area to the user based on this. For example, it updates the suggestions taking into account the development status of the area and information on the opening of new facilities. This makes it possible to suggest the most suitable area to the user by comparing the characteristics of different living areas.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The lifestyle information collection unit collects the user's health and fitness data, and the generation AI can make property suggestions based on that. For example, it can suggest properties near gyms and parks to health-conscious users. It can also analyze fitness data and suggest properties that match the user's exercise habits and health goals. For example, it can suggest properties with nearby running courses. It will also develop a system that monitors the user's health condition in real time and dynamically adjusts property suggestions based on that. For example, it can update the suggestions according to changes in health condition. This will make it possible to suggest properties that are suitable for health-conscious users by taking into account the user's health and fitness data.

[0053] The lifestyle information collection unit can analyze a user's past travel history and identify lifestyle information trends. For example, it can understand hobbies and interests from the places visited and the types of accommodations. It can also identify the user's preferred travel style (resort, adventure, cultural experience, etc.) based on the travel history. Furthermore, it can integrate the travel history with other lifestyle information to identify more detailed lifestyle trends. This allows for a more accurate understanding of lifestyle trends by analyzing the user's travel history.

[0054] The lifestyle information collection unit collects information about the user's lifestyle, and the generation AI can use that information to predict future changes in the user's lifestyle. For example, it takes into account changes in age and family composition. It can also predict future lifestyle changes and make property suggestions based on that. For example, it can suggest larger properties in consideration of future increases in children. It can also develop a system that predicts lifestyle changes in real time and dynamically adjusts the content of suggestions. For example, it can update property suggestions in response to life events. This makes it possible to predict future changes in the user's lifestyle and suggest properties that meet future needs.

[0055] The lifestyle information collection unit collects information about the user's lifestyle, and the generation AI can use that information to predict future changes in the user's lifestyle. For example, it takes into account changes in age and family composition. It can also predict future lifestyle changes and make property suggestions based on that. For example, it can suggest larger properties in consideration of future increases in children. It can also develop a system that predicts lifestyle changes in real time and dynamically adjusts the content of suggestions. For example, it can update property suggestions in response to life events. This makes it possible to predict future changes in the user's lifestyle and suggest properties that meet future needs.

[0056] The lifestyle information collection unit collects information about the user's lifestyle, and the generation AI can use that information to predict future changes in the user's lifestyle. For example, it takes into account changes in age and family composition. It can also predict future lifestyle changes and make property suggestions based on that. For example, it can suggest larger properties in consideration of future increases in children. It can also develop a system that predicts lifestyle changes in real time and dynamically adjusts the content of suggestions. For example, it can update property suggestions in response to life events. This makes it possible to predict future changes in the user's lifestyle and suggest properties that meet future needs.

[0057] The lifestyle information collection unit collects information about the user's lifestyle, and the generation AI can use that information to predict future changes in the user's lifestyle. For example, it takes into account changes in age and family composition. It can also predict future lifestyle changes and make property suggestions based on that. For example, it can suggest larger properties in consideration of future increases in children. It can also develop a system that predicts lifestyle changes in real time and dynamically adjusts the content of suggestions. For example, it can update property suggestions in response to life events. This makes it possible to predict future changes in the user's lifestyle and suggest properties that meet future needs.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The lifestyle information collection unit collects information about the user's lifestyle, such as their preferred holiday activities, work style (such as remote work or frequency of commuting), specific lifestyle preferences (such as eco-friendly living or living with pets), and hobbies (such as sports, art, cooking). Step 2: The property information collection department collects property information, such as the property's layout, facilities, rent, nearest train line, surrounding facilities (parks, gyms, cafes, hospitals, etc.), local safety, transportation access, and educational environment. Step 3: The Coordination Department coordinates the optimal property for the user based on the information collected by the Lifestyle Information Collection Department and the Property Information Collection Department. For example, for a user who primarily works remotely, it will suggest a property with high-speed internet access in a quiet environment. For a user who enjoys the outdoors, it will suggest a property in an area with a nearby park or abundant nature. Step 4: The feedback unit receives feedback from the user and customizes the proposed content. For example, if a user provides feedback such as "I want a larger living room" or "I prefer a property closer to the station," the feedback unit will re-analyze the feedback and propose a more suitable property.

[0060] (Example 2) The AI ​​system according to the embodiment of the present invention collects lifestyle information of a user, analyzes property information, and proposes a total coordinated property that is optimal for the user. As a result, the AI ​​system proposes properties that are optimal for the user's lifestyle, thereby improving user satisfaction.

[0061] The AI ​​system according to the embodiment includes a lifestyle information collection unit, a property information collection unit, a coordination unit, and a feedback unit. The lifestyle information collection unit collects lifestyle information about the user. For example, the information includes information about the user's preferred holiday activities, work style (e.g., remote work, frequency of commuting), specific lifestyle preferences (e.g., eco-friendly living, living with pets), and hobbies (e.g., sports, art, cooking). The property information collection unit collects property information. For example, the information includes not only the property's layout and facilities, rent, and nearest train lines, but also surrounding facilities (e.g., parks, gyms, cafes, hospitals), local safety, transportation access, and educational environment. The coordination unit coordinates the optimal property for the user based on the information collected by the lifestyle information collection unit and the property information collection unit. For example, for a user who primarily works remotely, the coordination unit suggests a property with high-speed internet access in a quiet environment. For a user who enjoys the outdoors, the coordination unit suggests a property in an area with nearby parks and abundant nature. The feedback unit receives feedback from the user and customizes the suggestions. For example, if a user provides feedback on a proposed property such as "I want a larger living room" or "I prefer a property closer to the station," the feedback unit will perform further analysis based on that feedback and propose a more suitable property. This allows the AI ​​system according to the embodiment to propose properties that are optimal for the user's lifestyle and improve user satisfaction.

[0062] The lifestyle information collection unit can automatically collect lifestyle information from a user's social media or blog and analyze it using a generation AI. The lifestyle information collection unit, for example, analyzes the content of a user's social media posts to understand lifestyle trends. For example, it identifies a user's preferred lifestyle based on the locations and activities that the user frequently posts. The lifestyle information collection unit also automatically collects a user's blog posts and performs text analysis to extract detailed lifestyle information. For example, it identifies hobbies and interests from travelogues and daily events. The lifestyle information collection unit also analyzes image data from social media and blogs to collect visual information about the user's lifestyle. For example, it identifies outdoor activities and cooking hobbies from posted photos. This allows for more accurate lifestyle information to be obtained by automatically collecting and analyzing lifestyle information from a user's social media or blog.

[0063] The lifestyle information collection unit can analyze a user's past purchase history or search history to identify lifestyle information trends. The lifestyle information collection unit, for example, analyzes the user's online shopping purchase history to identify lifestyle trends. For example, it identifies hobbies and interests from the categories and brands of purchased products. The lifestyle information collection unit also analyzes the user's search history to identify topics and activities of interest. For example, it extracts lifestyle characteristics from frequently searched keywords and sites. The lifestyle information collection unit also integrates the user's purchase history and search history to identify more detailed lifestyle trends. For example, it analyzes the relevance between purchased products and searched information. In this way, lifestyle trends can be more accurately identified by analyzing the user's purchase history and search history.

[0064] The lifestyle information collection unit can use the emotion estimation function to analyze the emotions a user has toward a specific lifestyle and identify lifestyle elements that elicit positive emotions. The lifestyle information collection unit, for example, collects information about the user's lifestyle and uses the emotion estimation function to identify elements that elicit positive emotions. For example, it analyzes emotion scores for specific activities and places. The lifestyle information collection unit also analyzes the user's emotional response in real time when entering lifestyle information and makes suggestions that elicit positive emotions. For example, it analyzes facial expressions and voices at the time of entry. The lifestyle information collection unit also identifies lifestyle elements that elicit positive emotions for the user based on the emotion estimation data and makes suggestions that emphasize those elements. For example, it prioritizes suggestions for activities and places with high emotion scores. In this way, the emotion estimation function can be used to identify lifestyle elements that elicit positive emotions for the user and make more appropriate suggestions.

[0065] The lifestyle information collection unit can collect lifestyle information about a user using voice input or image analysis. The lifestyle information collection unit, for example, builds a system that allows a user to provide lifestyle information through voice input and performs voice analysis. For example, it converts what the user says into text and analyzes lifestyle trends. The lifestyle information collection unit also analyzes images uploaded by the user to collect visual information about the lifestyle. For example, it identifies hobbies and interests from travel photos or food photos. The lifestyle information collection unit also combines voice input and image analysis to develop a system that collects lifestyle information about a user from multiple angles. For example, it analyzes images related to content explained in voice. In this way, it is possible to collect lifestyle information about a user from multiple angles using voice input and image analysis.

[0066] The lifestyle information collection unit can collect lifestyle information from different cultural spheres or regions and analyze it from a global perspective. The lifestyle information collection unit, for example, builds a system that collects lifestyle information from different cultural spheres or regions and analyzes it from a global perspective. For example, it collects information from social media and blogs in various countries. The lifestyle information collection unit also compares lifestyle information from different cultural spheres and analyzes similarities and differences. For example, it identifies activities and hobbies that are popular in a particular cultural sphere. The lifestyle information collection unit also develops a system that analyzes lifestyle information from a global perspective and makes optimal suggestions to users. For example, it makes suggestions that combine lifestyle elements from different cultural spheres. This makes it possible to make more diverse suggestions by collecting lifestyle information from different cultural spheres and analyzing it from a global perspective.

[0067] The lifestyle information collection unit uses the emotion estimation function to analyze the emotions of a user when entering lifestyle information in real time and make suggestions that will elicit positive emotions. The lifestyle information collection unit, for example, uses the emotion estimation function to build a system that analyzes emotions in real time when a user enters lifestyle information. For example, it analyzes facial expressions and voices at the time of entry. The lifestyle information collection unit also makes suggestions that will make the user feel positive emotions based on the emotion estimation data. For example, it presents encouraging messages or success stories depending on the input content. The lifestyle information collection unit also analyzes the user's emotional responses in real time and provides an interface for eliciting positive emotions. For example, it makes positive suggestions when the emotion score is low. In this way, the emotion estimation function can be used to make suggestions that will make the user feel positive emotions.

[0068] The property information collection unit can collect reviews or ratings from past residents of a property and analyze them using a generation AI. For example, the property information collection unit builds a system that automatically collects reviews and ratings posted by past residents of a property and analyzes them using a generation AI. For example, it collects information from review sites and social media. The property information collection unit also performs text analysis of the reviews and ratings from past residents to extract the features and problems of the property. For example, it classifies positive and negative ratings. The property information collection unit also develops a system that uses a generation AI to calculate an overall rating of the property based on the collected reviews and ratings. For example, it creates a ranking of properties based on the rating score. This makes it easier to understand the features and problems of a property by analyzing the reviews and ratings from past residents.

[0069] The property information collection unit can collect and analyze changes in the surrounding environment of a property in real time. The property information collection unit, for example, builds a system that collects changes in the surrounding environment of a property in real time. For example, it automatically collects information on the opening of new facilities and changes in public safety. The property information collection unit also analyzes changes in the surrounding environment and evaluates the impact on the value and convenience of the property. For example, it analyzes whether the opening of a new facility will increase the attractiveness of the property. The property information collection unit also develops a system that updates the evaluation of a property in real time based on the collected information on changes in the surrounding environment. For example, it reflects the impact that changes in public safety have on the evaluation of a property. In this way, by collecting and analyzing changes in the surrounding environment in real time, it becomes easier to evaluate the value and convenience of a property.

[0070] The property information collection unit can use the emotion estimation function to analyze a user's emotions toward a property and identify property elements that elicit positive emotions. The property information collection unit, for example, builds a system that analyzes a user's emotions toward a property and identifies elements that elicit positive emotions. For example, it analyzes emotional reactions to property photos and descriptions. The property information collection unit also identifies property elements that evoke positive emotions in users based on emotion estimation data and makes suggestions that emphasize those elements. For example, it prioritizes suggestions of facilities and floor plans with high emotion scores. The property information collection unit also develops a system that analyzes a user's emotional reactions in real time and identifies property elements that elicit positive emotions. For example, it analyzes emotional reactions during a property viewing. As a result, the emotion estimation function can be used to identify property elements that evoke positive emotions in users, enabling more appropriate suggestions.

[0071] The property information collection unit can collect detailed information about the surrounding environment of a property using drone or satellite images. The property information collection unit, for example, builds a system that uses drones to collect detailed information about the surrounding environment of a property. For example, it analyzes images taken by a drone to understand surrounding facilities and scenery. The property information collection unit also collects and analyzes information about the surrounding environment of a property using satellite images. For example, it understands the accessibility of transportation and the status of green spaces from satellite images. The property information collection unit also develops a system that combines drone and satellite images to collect detailed information about the surrounding environment of a property. For example, it integrates and analyzes images taken by a drone with satellite images. In this way, detailed information about the surrounding environment of a property can be collected using drones and satellite images.

[0072] The property information collection unit can compare property information from different cities or countries and analyze it from a global perspective. The property information collection unit, for example, builds a system that collects property information from different cities or countries and analyzes it from a global perspective. For example, it collects information from real estate databases in each country. The property information collection unit also compares property information from different cities or countries and analyzes similarities and differences. For example, it analyzes differences in rent and facilities. The property information collection unit also develops a system that analyzes property information from a global perspective and makes optimal suggestions to users. For example, it makes suggestions that combine property elements from different cities or countries. This makes it possible to compare property information from different cities or countries and analyze it from a global perspective, making more diverse suggestions possible.

[0073] The property information collection unit uses the emotion estimation function to analyze the user's emotions in real time when entering property information, and can make suggestions that elicit positive emotions. For example, the property information collection unit builds a system that uses the emotion estimation function to analyze emotions in real time when a user enters property information. For example, it analyzes facial expressions and voices at the time of entry. The property information collection unit also makes suggestions that will make the user feel positive emotions based on the emotion estimation data. For example, it presents encouraging messages or success stories depending on the input content. The property information collection unit also analyzes the user's emotional responses in real time and provides an interface for eliciting positive emotions. For example, it makes positive suggestions when the emotion score is low. In this way, the emotion estimation function can be used to make suggestions that will make the user feel positive emotions.

[0074] The coordination department can predict future changes in a user's lifestyle and make property suggestions based on that. For example, the coordination department will build a system in which a generative AI analyzes a user's past data and predicts future lifestyle changes. For example, it will take into account changes in age and family composition. The coordination department will also predict future lifestyle changes and make property suggestions based on that. For example, it will suggest larger properties taking into account future increases in children. The coordination department will also develop a system that predicts changes in a user's lifestyle in real time and dynamically adjusts the content of suggestions. For example, it will update property suggestions according to life events. This makes it possible to predict future changes in a user's lifestyle and suggest properties that meet future needs.

[0075] The coordination department can make property suggestions taking into account the user's health condition or fitness data. For example, the coordination department collects the user's health condition and fitness data, and builds a system in which the generation AI makes property suggestions based on that data. For example, the coordination department can suggest properties near gyms and parks to health-conscious users. The coordination department can also analyze fitness data to suggest properties that match the user's exercise habits and health goals. For example, it can suggest properties near running courses. The coordination department can also monitor the user's health condition in real time and develop a system that dynamically adjusts property suggestions based on that information. For example, it can update the suggestions according to changes in the user's health condition. This makes it possible to suggest properties that are suitable for health-conscious users by taking into account the user's health condition and fitness data.

[0076] The coordination unit uses the emotion estimation function to analyze the emotions a user has toward a proposed property and can prioritize suggest properties that elicit positive emotions. The coordination unit, for example, builds a system that analyzes the emotions a user has toward a proposed property in real time and prioritizes suggesting properties that elicit positive emotions. For example, it analyzes emotional reactions to photos and descriptions of the property. The coordination unit also identifies properties for which the user has positive emotions based on the emotion estimation data and prioritizes suggesting those properties. For example, it displays properties with high emotion scores at the top of the list. The coordination unit also develops a system that analyzes the user's emotional reactions in real time and dynamically suggests properties that elicit positive emotions. For example, it adjusts the content of the proposal based on the emotional reactions during the viewing. In this way, the emotion estimation function can be used to prioritize suggesting properties for which the user has positive emotions.

[0077] The coordination unit can make property suggestions taking into account the user's family composition or pet information. For example, the coordination unit collects information about the user's family composition and pets, and builds a system in which the generation AI makes property suggestions based on that information. For example, it suggests properties with spacious living rooms and children's rooms for families with children. The coordination unit also takes pet information into account to suggest pet-friendly properties. For example, it suggests properties that allow pets or properties with nearby pet facilities. The coordination unit also monitors family composition and pet information in real time, and develops a system that dynamically adjusts property suggestions based on that information. For example, it updates the suggestions according to changes in family composition. This makes it possible to suggest properties that are suitable for families and pets by taking into account the user's family composition and pet information.

[0078] The coordination unit can match users with different lifestyles and propose joint property sharing. The coordination unit, for example, builds a system that matches users with different lifestyles and proposes joint property sharing. For example, it matches remote workers with outdoor enthusiasts. The coordination unit also matches compatible users based on users' lifestyle information and proposes shared properties. For example, it matches users with common hobbies and interests. The coordination unit also develops a system that analyzes the compatibility of users' lifestyles in real time when proposing shared properties and makes optimal matches. For example, it scores the degree of similarity of lifestyles. This makes it possible to propose joint property sharing by matching users with different lifestyles.

[0079] The coordination unit uses the emotion estimation function to analyze the emotions of a user when entering lifestyle information in real time and make suggestions that elicit positive emotions. The coordination unit, for example, builds a system that uses the emotion estimation function to analyze emotions in real time when a user enters lifestyle information. For example, it analyzes facial expressions and voices at the time of entry. The coordination unit also makes suggestions that will make the user feel positive emotions based on the emotion estimation data. For example, it presents encouraging messages or success stories depending on the input content. The coordination unit also analyzes the user's emotional responses in real time and provides an interface for eliciting positive emotions. For example, it makes positive suggestions when the emotion score is low. In this way, the emotion estimation function can be used to make suggestions that will make the user feel positive emotions.

[0080] The feedback unit analyzes feedback from users, allowing the generation AI to automatically learn and improve the proposals. For example, the feedback unit collects feedback from users and builds a system in which the generation AI automatically learns and improves the proposals based on that data. For example, it performs text analysis of the feedback content. The feedback unit also analyzes the feedback data and identifies areas for improvement in the proposals. For example, it extracts the features and conditions of the property that the user is looking for. The feedback unit also develops a system in which the generation AI dynamically adjusts the proposals based on user feedback. For example, it updates the proposals in real time in response to the feedback. In this way, the generation AI analyzes user feedback and automatically learns and improves the proposals, thereby improving the accuracy of the proposals.

[0081] The feedback unit can develop a new algorithm for the generation AI to improve the accuracy of its suggestions based on user feedback. The feedback unit, for example, analyzes user feedback data and develops a new algorithm for the generation AI to improve the accuracy of its suggestions. For example, it improves the algorithm based on the feedback. The feedback unit also builds a system to evaluate the accuracy of the suggestions based on the feedback data. For example, it analyzes user satisfaction scores. The feedback unit also develops a new algorithm and builds a feedback loop to improve the accuracy of the suggestions. For example, it regularly evaluates and improves the performance of the algorithm. In this way, the accuracy of the suggestions is improved by developing a new algorithm based on user feedback.

[0082] The feedback unit uses the emotion estimation function to analyze the emotions of the user when providing feedback and can make suggestions that elicit positive emotions. For example, the feedback unit uses the emotion estimation function to build a system that analyzes emotions in real time when the user provides feedback. For example, it analyzes facial expressions and voices when providing feedback. The feedback unit also makes suggestions that will make the user feel positive emotions based on the emotion estimation data. For example, it presents encouraging messages or success stories depending on the feedback content. The feedback unit also analyzes the user's emotional responses in real time and provides an interface for eliciting positive emotions. For example, it makes positive suggestions when the emotion score is low. In this way, the emotion estimation function can be used to make suggestions that will make the user feel positive emotions.

[0083] The feedback department can also refer to the feedback of other users when the generation AI customizes the proposal content based on user feedback. For example, the feedback department collects feedback data from other users and builds a system in which the generation AI uses it as a reference to customize the proposal content. For example, similar feedback is grouped. The feedback department also analyzes the feedback of other users and extracts common improvements and requests. For example, it identifies the property features desired by multiple users. The feedback department also develops a system that dynamically adjusts the proposal content based on the feedback of other users. For example, it analyzes feedback trends in real time and reflects them in the proposal content. In this way, the accuracy of the proposal content is improved by also referring to the feedback of other users.

[0084] The feedback unit can also take feedback from different cultural spheres and regions into consideration when the generation AI customizes its proposals based on user feedback. For example, the feedback unit collects feedback from users in different cultural spheres and regions, and builds a system in which the generation AI uses that feedback to customize its proposals. For example, it takes into account the characteristics of each region. The feedback unit also analyzes feedback from different cultural spheres and extracts common improvements and requests. For example, it identifies the characteristics of properties that are popular in specific cultural spheres. The feedback unit also develops a system that dynamically adjusts the proposals based on feedback from different cultural spheres and regions. For example, it analyzes feedback trends by region in real time and reflects them in the proposals. This improves the accuracy of the proposals by taking feedback from different cultural spheres and regions into consideration.

[0085] The feedback unit uses the emotion estimation function to analyze the emotions of the user when providing feedback in real time and make suggestions that will elicit positive emotions. For example, the feedback unit uses the emotion estimation function to build a system that analyzes emotions in real time when the user provides feedback. For example, it analyzes facial expressions and voices when providing feedback. The feedback unit also makes suggestions that will make the user feel positive emotions based on the emotion estimation data. For example, it presents encouraging messages or success stories depending on the feedback content. The feedback unit also analyzes the user's emotional responses in real time and provides an interface for eliciting positive emotions. For example, it makes positive suggestions when the emotion score is low. In this way, the emotion estimation function can be used to make suggestions that will make the user feel positive emotions.

[0086] The coordination unit can perform total coordination of living areas taking into account local event information or community activities. For example, the coordination unit collects local event information and community activities, and builds a system in which the generation AI performs total coordination of living areas based on that information. For example, it analyzes the local event calendar. The coordination unit also analyzes community activity information and suggests areas that suit the user's lifestyle. For example, it suggests areas where outdoor activities are popular. The coordination unit also collects local event information and community activities in real time, and develops a system that dynamically adjusts living area suggestions based on that information. For example, it updates the suggestions depending on the event status. This makes it possible to suggest living areas that are suitable for the user by taking into account local event information and community activities.

[0087] The coordination unit can coordinate living areas in a comprehensive manner, taking into account local climate or seasonal changes. For example, the coordination unit collects local climate data and seasonal changes, and builds a system in which the generation AI uses this data to coordinate living areas in a comprehensive manner. For example, it analyzes annual temperature and precipitation. The coordination unit also considers seasonal climate changes and suggests areas that suit the user's lifestyle. For example, it suggests areas where you can enjoy skiing in winter and areas close to the beach in summer. The coordination unit also collects local climate and seasonal changes in real time, and develops a system that dynamically adjusts living area suggestions based on that information. For example, it updates the suggestions by taking seasonal events and activities into account. This makes it possible to suggest living areas that are suitable for the user by taking into account local climate and seasonal changes.

[0088] The coordination unit uses the emotion estimation function to analyze the emotions a user has toward a proposed living area and can prioritize suggest areas that elicit positive emotions. The coordination unit, for example, builds a system that analyzes the emotions a user has toward a proposed living area in real time and prioritizes suggesting areas that elicit positive emotions. For example, it analyzes emotional reactions to photos and descriptions of the area. The coordination unit also identifies areas in which the user feels positive emotions based on the emotion estimation data and prioritizes suggesting those areas. For example, it displays areas with high emotion scores at the top of the list. The coordination unit also develops a system that analyzes the user's emotional reactions in real time and dynamically suggests areas that elicit positive emotions. For example, it adjusts the content of the proposal based on the emotional reactions during the viewing. In this way, the emotion estimation function can be used to prioritize suggesting living areas in which the user feels positive emotions.

[0089] The coordination unit can perform total coordination of living areas taking into account the user's commuting time or means of transportation. For example, the coordination unit collects the user's commuting time and means of transportation, and the generation AI builds a system that performs total coordination of living areas based on that information. For example, it suggests areas where commuting time can be shortened. The coordination unit also analyzes transportation information and suggests areas that suit the user's lifestyle. For example, it suggests areas with good public transportation or areas where bicycle commuting is possible. The coordination unit also collects the user's commuting time and means of transportation in real time, and develops a system that dynamically adjusts living area suggestions based on that information. For example, it updates the suggestions according to changes in traffic conditions. This makes it possible to suggest living areas that are convenient for commuting by taking into account the user's commuting time and means of transportation.

[0090] The coordination unit can compare the characteristics of different living areas and suggest the most suitable area to the user. For example, the coordination unit collects the characteristics of different living areas, and the generation AI builds a system that uses this information to suggest the most suitable area to the user. For example, it compares the facilities and environment of each area. The coordination unit also analyzes the characteristics of different areas and suggests areas that suit the user's lifestyle. For example, it suggests areas with good educational environments or areas with abundant shopping facilities. The coordination unit also collects the characteristics of different living areas in real time, and develops a system that dynamically suggests the most suitable area to the user based on this. For example, it updates the suggestions taking into account the development status of the area and information on the opening of new facilities. This makes it possible to suggest the most suitable area to the user by comparing the characteristics of different living areas.

[0091] The coordination unit can compare the characteristics of different living areas and suggest the most suitable area to the user. For example, the coordination unit collects the characteristics of different living areas, and the generation AI builds a system that uses this information to suggest the most suitable area to the user. For example, it compares the facilities and environment of each area. The coordination unit also analyzes the characteristics of different areas and suggests areas that suit the user's lifestyle. For example, it suggests areas with good educational environments or areas with abundant shopping facilities. The coordination unit also collects the characteristics of different living areas in real time, and develops a system that dynamically suggests the most suitable area to the user based on this. For example, it updates the suggestions taking into account the development status of the area and information on the opening of new facilities. This makes it possible to suggest the most suitable area to the user by comparing the characteristics of different living areas.

[0092] The coordination unit uses the emotion estimation function to analyze the emotions of the user when entering living area information in real time and can make suggestions that elicit positive emotions. For example, the coordination unit builds a system that uses the emotion estimation function to analyze emotions in real time when the user enters living area information. For example, it analyzes facial expressions and voice at the time of input. The coordination unit also makes suggestions that will make the user feel positive emotions based on the emotion estimation data. For example, it presents encouraging messages or success stories depending on the input content. The coordination unit also analyzes the user's emotional responses in real time and provides an interface for eliciting positive emotions. For example, it makes positive suggestions when the emotion score is low. In this way, the emotion estimation function can be used to make suggestions that will make the user feel positive emotions.

[0093] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0094] The lifestyle information collection unit collects the user's health and fitness data, and the generation AI can make property suggestions based on that. For example, it can suggest properties near gyms and parks to health-conscious users. It can also analyze fitness data and suggest properties that match the user's exercise habits and health goals. For example, it can suggest properties with nearby running courses. It will also develop a system that monitors the user's health condition in real time and dynamically adjusts property suggestions based on that. For example, it can update the suggestions according to changes in health condition. This will make it possible to suggest properties that are suitable for health-conscious users by taking into account the user's health and fitness data.

[0095] The lifestyle information collection unit can analyze a user's past travel history and identify lifestyle information trends. For example, it can understand hobbies and interests from the places visited and the types of accommodations. It can also identify the user's preferred travel style (resort, adventure, cultural experience, etc.) based on the travel history. Furthermore, it can integrate the travel history with other lifestyle information to identify more detailed lifestyle trends. This allows for a more accurate understanding of lifestyle trends by analyzing the user's travel history.

[0096] The lifestyle information collection unit collects information about the user's lifestyle and can use the emotion estimation function to identify elements that elicit positive emotions. For example, it analyzes emotion scores for specific activities and places. It can also analyze the user's emotional responses in real time when entering lifestyle information and make suggestions that elicit positive emotions. For example, it analyzes facial expressions and voices when entering information. It can also identify lifestyle elements that evoke positive emotions in the user based on the emotion estimation data and make suggestions that emphasize those elements. For example, it prioritizes suggestions of activities and places with high emotion scores. As a result, the emotion estimation function can identify lifestyle elements that evoke positive emotions in the user and make more appropriate suggestions.

[0097] The lifestyle information collection unit collects information about the user's lifestyle, and the generation AI can use that information to predict future changes in the user's lifestyle. For example, it takes into account changes in age and family composition. It can also predict future lifestyle changes and make property suggestions based on that. For example, it can suggest larger properties in consideration of future increases in children. It can also develop a system that predicts lifestyle changes in real time and dynamically adjusts the content of suggestions. For example, it can update property suggestions in response to life events. This makes it possible to predict future changes in the user's lifestyle and suggest properties that meet future needs.

[0098] The lifestyle information collection unit collects information about the user's lifestyle and can use the emotion estimation function to identify elements that elicit positive emotions. For example, it analyzes emotion scores for specific activities and places. It can also analyze the user's emotional responses in real time when entering lifestyle information and make suggestions that elicit positive emotions. For example, it analyzes facial expressions and voices when entering information. It can also identify lifestyle elements that evoke positive emotions in the user based on the emotion estimation data and make suggestions that emphasize those elements. For example, it prioritizes suggestions of activities and places with high emotion scores. As a result, the emotion estimation function can identify lifestyle elements that evoke positive emotions in the user and make more appropriate suggestions.

[0099] The lifestyle information collection unit collects information about the user's lifestyle, and the generation AI can use that information to predict future changes in the user's lifestyle. For example, it takes into account changes in age and family composition. It can also predict future lifestyle changes and make property suggestions based on that. For example, it can suggest larger properties in consideration of future increases in children. It can also develop a system that predicts lifestyle changes in real time and dynamically adjusts the content of suggestions. For example, it can update property suggestions in response to life events. This makes it possible to predict future changes in the user's lifestyle and suggest properties that meet future needs.

[0100] The lifestyle information collection unit collects information about the user's lifestyle and can use the emotion estimation function to identify elements that elicit positive emotions. For example, it analyzes emotion scores for specific activities and places. It can also analyze the user's emotional responses in real time when entering lifestyle information and make suggestions that elicit positive emotions. For example, it analyzes facial expressions and voices when entering information. It can also identify lifestyle elements that evoke positive emotions in the user based on the emotion estimation data and make suggestions that emphasize those elements. For example, it prioritizes suggestions of activities and places with high emotion scores. As a result, the emotion estimation function can identify lifestyle elements that evoke positive emotions in the user and make more appropriate suggestions.

[0101] The lifestyle information collection unit collects information about the user's lifestyle, and the generation AI can use that information to predict future changes in the user's lifestyle. For example, it takes into account changes in age and family composition. It can also predict future lifestyle changes and make property suggestions based on that. For example, it can suggest larger properties in consideration of future increases in children. It can also develop a system that predicts lifestyle changes in real time and dynamically adjusts the content of suggestions. For example, it can update property suggestions in response to life events. This makes it possible to predict future changes in the user's lifestyle and suggest properties that meet future needs.

[0102] The lifestyle information collection unit collects information about the user's lifestyle and can use the emotion estimation function to identify elements that elicit positive emotions. For example, it analyzes emotion scores for specific activities and places. It can also analyze the user's emotional responses in real time when entering lifestyle information and make suggestions that elicit positive emotions. For example, it analyzes facial expressions and voices when entering information. It can also identify lifestyle elements that evoke positive emotions in the user based on the emotion estimation data and make suggestions that emphasize those elements. For example, it prioritizes suggestions of activities and places with high emotion scores. As a result, the emotion estimation function can identify lifestyle elements that evoke positive emotions in the user and make more appropriate suggestions.

[0103] The lifestyle information collection unit collects information about the user's lifestyle, and the generation AI can use that information to predict future changes in the user's lifestyle. For example, it takes into account changes in age and family composition. It can also predict future lifestyle changes and make property suggestions based on that. For example, it can suggest larger properties in consideration of future increases in children. It can also develop a system that predicts lifestyle changes in real time and dynamically adjusts the content of suggestions. For example, it can update property suggestions in response to life events. This makes it possible to predict future changes in the user's lifestyle and suggest properties that meet future needs.

[0104] The processing flow of the second embodiment will be briefly explained below.

[0105] Step 1: The lifestyle information collection unit collects information about the user's lifestyle, such as their preferred holiday activities, work style (such as remote work or frequency of commuting), specific lifestyle preferences (such as eco-friendly living or living with pets), and hobbies (such as sports, art, cooking). Step 2: The property information collection department collects property information, such as the property's layout, facilities, rent, nearest train line, surrounding facilities (parks, gyms, cafes, hospitals, etc.), local safety, transportation access, and educational environment. Step 3: The Coordination Department coordinates the optimal property for the user based on the information collected by the Lifestyle Information Collection Department and the Property Information Collection Department. For example, for a user who primarily works remotely, it will suggest a property with high-speed internet access in a quiet environment. For a user who enjoys the outdoors, it will suggest a property in an area with a nearby park or abundant nature. Step 4: The feedback unit receives feedback from the user and customizes the proposed content. For example, if a user provides feedback such as "I want a larger living room" or "I prefer a property closer to the station," the feedback unit will re-analyze the feedback and propose a more suitable property.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0123] 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.

[0124] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0125] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0138] 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.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0160] 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."

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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]

[0173] 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 lifestyle information collection unit that collects lifestyle information of a user; a property information collection unit that collects property information; a coordinating unit that performs total coordination of a property that is most suitable for the user based on the information collected by the lifestyle information collecting unit and the property information collecting unit; a feedback unit that receives feedback from the user and customizes the content of the suggestions. A system characterized by:

2. The lifestyle information collection unit Automatically collect lifestyle information from users' social media or blogs and analyze it with generative AI 2. The system of claim 1.

3. The property information collection unit Collect reviews or ratings from past property owners and analyze them with generative AI 2. The system of claim 1.

4. The coordinating unit Predicting future lifestyle changes for users and making property recommendations based on those changes 2. The system of claim 1.

5. The feedback unit Analyze user feedback and let the generative AI automatically learn and improve the suggestions.

2. The system of claim 1.

6. The lifestyle information collection unit Analyze the emotions users have toward a particular lifestyle and identify lifestyle elements that elicit positive emotions 2. The system of claim 1.

7. The property information collection unit Analyze users' feelings about the property and identify the property elements that elicit positive emotions 2. The system of claim 1.

8. The coordinating unit Analyzes the user's feelings toward proposed properties and prioritizes properties that evoke positive emotions 2. The system of claim 1.

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