System

Abstract

An object of a system according to an embodiment is to efficiently search for a real estate property matching a condition desired by a user and provide appropriate advice.SOLUTION: A system includes a condition input part, an analysis part, a retrieval part, a proposal part, a consultation reception part, and an advice provision part. The condition input unit inputs a condition desired by a user. The analysis unit analyzes the condition input by the condition input unit. The retrieval part retrieves the real estate property on the basis of the condition analyzed by the analysis part. The proposal unit proposes the real estate property retrieved by the retrieval unit. The consultation reception unit receives a user's consultation about real estate. The advice providing unit analyzes the contents of the consultation received by the consultation receiving unit and provides appropriate advice.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem of making it difficult to efficiently search for real estate properties that meet a user's desired conditions and provide appropriate advice.

[0005] The system according to the embodiment aims to efficiently search for real estate properties that meet the user's desired conditions and provide appropriate advice. [Means for solving the problem]

[0006] The system according to the embodiment includes a condition input unit, an analysis unit, a search unit, a proposal unit, a consultation reception unit, and an advice providing unit. The condition input unit inputs the conditions desired by the user. The analysis unit analyzes the conditions input by the condition input unit. The search unit searches for real estate properties based on the conditions analyzed by the analysis unit. The proposal unit proposes real estate properties searched by the search unit. The consultation reception unit accepts consultations regarding real estate from users. The advice providing unit analyzes the content of the consultation accepted by the consultation reception unit and provides appropriate advice. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently search for real estate properties that meet the user's desired conditions and provide appropriate advice. [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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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 real estate matching system according to an embodiment of the present invention utilizes generative AI to enable matching searches and consultations for real estate properties. As a result, the real estate matching system can search for and propose optimal real estate properties based on the user's desired conditions. It can also analyze the content of the user's consultation and provide appropriate advice and information.

[0029] A real estate matching system according to an embodiment includes a condition input unit, an analysis unit, a search unit, a proposal unit, a consultation reception unit, and an advice provision unit. The condition input unit inputs desired conditions from a user. For example, the user can input conditions such as location, price, floor plan, and age of the building. The analysis unit analyzes the conditions input by the condition input unit. For example, a generation AI understands the user's desired conditions and searches for optimal real estate properties based on those conditions. The search unit searches for real estate properties based on the conditions analyzed by the analysis unit. For example, the search unit extracts properties that match specified conditions such as location, price range, and floor plan from a database. The proposal unit proposes real estate properties searched by the search unit. For example, the proposal may be in the form of, "Properties that match your desired conditions are listed below." The consultation reception unit accepts real estate-related inquiries from users. For example, the consultation may receive an inquiry such as, "What should I pay attention to when purchasing a new home?" The advice provision unit analyzes the consultation content received by the consultation reception unit and provides appropriate advice. For example, the advice may provide specific advice such as, "When purchasing a new home, it is important to first set a budget and then narrow down the desired area." As a result, the real estate matching system according to the embodiment can search for and propose real estate properties based on the user's desired conditions, and can also analyze the user's consultation content and provide appropriate advice.

[0030] The analysis unit analyzes a user's past search history and behavioral patterns to predict and suggest the user's potential needs. For example, when a user inputs their desired conditions, the analysis unit uses the generation AI to analyze their past search history and extract the characteristics of properties that the user was previously interested in. For example, based on the price range and area of ​​properties previously searched for, the analysis unit suggests properties that may potentially interest the user in addition to the conditions entered by the user. The analysis unit also analyzes the user's behavioral patterns to predict potential needs. For example, if a user frequently searches for a specific area, the analysis unit prioritizes suggesting new property information related to that area. The analysis unit also uses the generation AI to predict and suggest potential needs that the user has not yet noticed based on the user's past search history and behavioral patterns. For example, if a user is interested in a specific floor plan or facilities, the analysis unit suggests properties that match those conditions. This allows the analysis unit to predict the user's potential needs and make more appropriate suggestions.

[0031] The analysis unit can propose properties that are expected to increase in value in the future, taking into account future development plans and infrastructure development information for the area. For example, when a user inputs their desired conditions, the analysis unit allows the generation AI to analyze future development plans for the area and propose properties that are likely to increase in value in the future. For example, the generation AI may prioritize properties in areas where new transportation infrastructure is planned. The analysis unit also considers information on infrastructure development for the area and proposes properties that will be more convenient in the future. For example, it may propose properties in areas where new shopping malls or schools are planned to be built. Furthermore, when analyzing the conditions input by the user, the generation AI proposes properties that are expected to increase in value in the future, based on information on future development plans and infrastructure development for the area. For example, it may propose properties in areas where urban redevelopment projects are underway. This allows the analysis unit to provide useful information to users by proposing properties that are expected to increase in value in the future.

[0032] The condition input unit supports voice input and gesture input, enabling more intuitive operation. For example, the condition input unit builds a system that supports voice input when a user inputs desired conditions. For example, if a user voice-inputs "a 3LDK apartment in Tokyo," the generation AI searches for properties based on those conditions. The condition input unit also provides an interface that supports gesture input, allowing users to operate intuitively. For example, the user can input desired conditions by performing specific gestures on the screen. The condition input unit also provides an interface that combines voice input and gesture input, allowing users to input desired conditions more intuitively. For example, conditions can be input by voice and fine adjustments can be made with gestures. This allows users to operate more intuitively.

[0033] The analysis unit reflects market trends and price fluctuations in real time, allowing it to provide the latest information. For example, when a user inputs their desired conditions, the generation AI analyzes market trends in real time and provides the latest information. For example, it suggests properties that reflect current market prices and areas with high demand. The analysis unit also analyzes price fluctuations in real time and provides the latest price information to the user. For example, it suggests properties that fit the user's desired price range based on recent price fluctuations. The analysis unit also reflects market trends and price fluctuations in real time when a user inputs their desired conditions, allowing it to provide the latest information. For example, it suggests properties that are likely to increase in value in the future based on current market trends. This allows the user to receive the latest market information.

[0034] The suggestion unit can suggest the most suitable property by taking into consideration the user's lifestyle and hobbies and preferences. In the suggestion unit, for example, the generation AI analyzes the user's lifestyle and suggests the most suitable property based on that. For example, for a user who likes the outdoors, the suggestion unit suggests properties with parks or nature nearby. The suggestion unit also considers the user's hobbies and preferences and the generation AI suggests the most suitable property. For example, for a user who likes cooking, the suggestion unit suggests properties with spacious kitchens. The suggestion unit also analyzes the user's lifestyle and hobbies and preferences and suggests the most suitable property based on that. For example, for a user who has pets, the suggestion unit suggests properties that allow pets. This makes it possible to suggest properties that suit the user's lifestyle and hobbies and preferences.

[0035] The suggestion unit can simultaneously provide information about the property's surrounding environment. For example, when the generation AI presents property search results, the suggestion unit simultaneously provides information about the property's surrounding environment. For example, it displays information about nearby schools, hospitals, and supermarkets. The suggestion unit also builds a system that provides information about the surrounding environment along with the property search results. For example, it displays information about public transportation and parks near the property. The suggestion unit also simultaneously provides information about the property's surrounding environment when the generation AI presents property search results. For example, it displays information about restaurants and cafes near the property. This allows the user to simultaneously obtain information about the property's surrounding environment.

[0036] The suggestion unit can provide 3D models and virtual tours to enable the user to experience the property more realistically. For example, when the generation AI presents search results for properties, the suggestion unit provides 3D models to enable the user to experience the property more realistically. For example, the suggestion unit displays a 3D model that allows the user to move freely inside the property. The suggestion unit also provides virtual tours to enable the user to experience the property more realistically. For example, the suggestion unit provides a virtual tour that allows the user to have a 360-degree view of the interior of the property. The suggestion unit also provides 3D models and virtual tours when the generation AI presents search results for properties to enable the user to experience the property more realistically. For example, the suggestion unit provides a virtual tour that includes the exterior of the property and the surrounding environment. This enables the user to experience the property more realistically.

[0037] The suggestion unit can display reviews and ratings from past residents, allowing the user to understand how comfortable the property actually is to live in. For example, the suggestion unit builds a system that displays reviews and ratings from past residents when the generation AI presents property search results. For example, it displays reviews about the livability of the property and the surrounding environment. The suggestion unit also displays ratings from past residents so that the user can understand how comfortable the property actually is to live in. For example, it displays reviews that detail the advantages and disadvantages of the property. The suggestion unit also displays reviews and ratings from past residents when the generation AI presents property search results so that the user can understand how comfortable the property actually is to live in. For example, it displays the residents' satisfaction and dissatisfaction as an evaluation score. This allows the user to understand how comfortable the property actually is to live in.

[0038] The advice providing unit can provide personalized advice by taking into account the user's past behavioral history and search history. For example, the generation AI analyzes the user's past behavioral history and provides personalized advice based on that. For example, the advice providing unit provides the user with the most suitable advice based on the characteristics of properties searched for in the past. The advice providing unit also considers the user's search history and provides personalized advice by taking into account the user's search history. For example, the advice providing unit provides the user with the most suitable advice based on the areas and price ranges searched for in the past. The advice providing unit also analyzes the user's past behavioral history and search history and provides personalized advice based on that. For example, the advice providing unit provides the user with the most suitable advice based on the characteristics of properties that the user was interested in in the past. This makes it possible to provide personalized advice to the user.

[0039] The advice providing unit can provide timely information by reflecting the latest market trends and economic indicators. For example, the generation AI analyzes the latest market trends and provides timely information based on the results. For example, it provides information on current market prices and areas with high demand. The advice providing unit also reflects economic indicators and the generation AI provides timely information. For example, it provides optimal advice to users based on the latest economic indicators. The advice providing unit also analyzes the latest market trends and economic indicators and provides timely information based on the results. For example, it provides optimal advice to users based on current market trends. This makes it possible to provide users with the latest market information.

[0040] The advice providing unit can utilize interactive visual content to provide information in an easy-to-understand format. For example, when the generation AI provides advice or information, the advice providing unit utilizes interactive visual content to provide information in an easy-to-understand format. For example, information is displayed visually using infographics. The advice providing unit also utilizes video content to provide advice or information to the generation AI. For example, information is provided in the form of a video so that it is visually easy for the user to understand. The advice providing unit also utilizes interactive visual content to provide information in an easy-to-understand format when the generation AI provides advice or information. For example, interactive content that can be operated by the user is provided. This makes it easier for the user to understand the information.

[0041] The advice providing unit collects user feedback in real time and can continuously improve the quality of the advice. For example, when the generation AI provides advice or information, the advice providing unit collects user feedback in real time and improves the quality of the advice based on the results. For example, it allows the user to input ratings and comments on the advice provided. The advice providing unit also analyzes user feedback in real time and the generation AI continuously improves the quality of the advice. For example, it adjusts the content of the advice based on the user's opinion. The advice providing unit also collects user feedback in real time when the generation AI provides advice or information and improves the quality of the advice based on the results. For example, it updates the content of the advice based on the user's feedback. This makes it possible to improve the quality of the advice based on the user's feedback.

[0042] The user interface can be optimized based on the user's past operation history and preferences. For example, the user interface may be personalized and optimized based on the user's past operation history. For example, functions frequently used by the user may be preferentially displayed. The user interface may also customize the interface design and layout based on the user's preferences. For example, the user's preferred colors and fonts may be used. The user interface may also be personalized and optimized based on the user's past operation history and preferences. For example, pages frequently accessed by the user may be placed on the home screen. This allows the user interface to be optimized to the user's preferences.

[0043] The user interface can make operations smoother by having the generating AI predict the user's operations and suggest the next step. For example, the user interface can predict the user's operations and suggest the next step. For example, after a user performs a property search, related property information is automatically displayed. The user interface can also make operations smoother by having the generating AI predict the user's operations and suggest the next step. For example, after a user completes an input form, the next input field is automatically displayed. The user interface can also make operations smoother by having the generating AI predict the user's operations and suggest the next step. For example, after a user views detailed information about a property, related advice and information is displayed. This makes operations smoother for the user.

[0044] The user interface can support voice assistants and gesture operation, enabling more intuitive operation. For example, the user interface may incorporate a voice assistant to allow the user to operate by voice. For example, when the user commands "search for properties" by voice, the generation AI searches for properties. The user interface may also incorporate gesture operation to allow the user to operate intuitively. For example, the user may be able to input desired conditions by performing specific gestures on the screen. The user interface may also provide an interface that combines voice assistants and gesture operation to allow the user to operate more intuitively. For example, conditions may be input by voice and fine adjustments may be made with gestures. This allows the user to operate more intuitively.

[0045] The user interface allows the generating AI to monitor the user's operations in real time and provide immediate support when a problem occurs. For example, if the user is having trouble operating a system, the generating AI will immediately display a help message. The user interface also builds a system where the generating AI monitors the user's operations in real time and provides support when a problem occurs. For example, if the user receives an error message, the generating AI will present a solution. The user interface also allows the generating AI to monitor the user's operations in real time and provide immediate support when a problem occurs. For example, if the user is unsure how to operate a system, the generating AI will suggest the next step. This allows the user to receive immediate support when they encounter a problem.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The real estate matching system can also be equipped with a health monitoring unit that monitors the user's health condition and suggests properties that are health-conscious. For example, if the user has allergies, properties in areas with fewer allergens can be suggested. Also, if the user likes to exercise, properties with nearby gyms or parks can be suggested. Furthermore, based on the user's health data, it is possible to prioritize suggestions of properties with low air quality and noise levels. This makes it possible to suggest properties that take the user's health condition into consideration.

[0048] The real estate matching system can also be equipped with a family structure analysis unit that takes into account the user's family structure and suggests properties suitable for the whole family. For example, for a family with children, it can suggest properties with nearby schools and parks. It can also suggest properties with barrier-free designs for a family with elderly people. It can also give priority to suggesting pet-friendly properties for a family with pets. This makes it possible to suggest properties suitable for the whole family.

[0049] The real estate matching system can further include a hobby analysis unit that takes into consideration the user's hobbies and interests and suggests properties that are close to facilities related to those hobbies. For example, if the user's hobby is golf, properties with nearby golf courses can be suggested. Also, if the user's hobby is cooking, properties with spacious kitchens can be suggested. Furthermore, if the user's hobby is music, properties with soundproofing can be suggested preferentially. This makes it possible to suggest properties that match the user's hobbies and interests.

[0050] The real estate matching system can also be equipped with a commute time analysis unit that takes into account the user's commute time and suggests properties that are convenient for commuting. For example, if the user inputs the time it takes to commute, the generation AI will analyze the optimal commute route and suggest properties with a short commute time. Also, if the user uses public transportation, it can suggest properties that are close to the nearest station or bus stop. Furthermore, if the user wishes to commute by bicycle, it can also prioritize suggesting properties with bicycle parking spaces. This makes it possible to suggest properties that take the user's commute time into consideration.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The condition input section allows the user to input desired conditions. For example, the user can input conditions such as location, price, layout, and age of the building. Step 2: The analysis unit analyzes the conditions entered by the condition input unit. For example, the generation AI understands the user's desired conditions and searches for the most suitable real estate property based on them. Step 3: The search unit searches for real estate properties based on the conditions analyzed by the analysis unit. For example, it extracts properties from the database that match the specified conditions, such as location, price range, and floor plan. Step 4: The proposal unit proposes the real estate properties found by the search unit. For example, it suggests properties that meet your desired conditions, such as "The following properties meet your desired conditions." Step 5: The consultation reception unit receives real estate-related inquiries from users. For example, a question such as "What should I pay attention to when buying a new house?" Step 6: The advice provider analyzes the consultation received by the consultation reception unit and provides appropriate advice. For example, they may provide specific advice such as, "When purchasing a new home, it is important to first set a budget and then narrow down the area you want to live in."

[0053] (Example 2) The real estate matching system according to an embodiment of the present invention utilizes generative AI to enable matching searches and consultations for real estate properties. As a result, the real estate matching system can search for and propose optimal real estate properties based on the user's desired conditions. It can also analyze the content of the user's consultation and provide appropriate advice and information.

[0054] A real estate matching system according to an embodiment includes a condition input unit, an analysis unit, a search unit, a proposal unit, a consultation reception unit, and an advice provision unit. The condition input unit inputs desired conditions from a user. For example, the user can input conditions such as location, price, floor plan, and age of the building. The analysis unit analyzes the conditions input by the condition input unit. For example, a generation AI understands the user's desired conditions and searches for optimal real estate properties based on those conditions. The search unit searches for real estate properties based on the conditions analyzed by the analysis unit. For example, the search unit extracts properties that match specified conditions such as location, price range, and floor plan from a database. The proposal unit proposes real estate properties searched by the search unit. For example, the proposal may be in the form of, "Properties that match your desired conditions are listed below." The consultation reception unit accepts real estate-related inquiries from users. For example, the consultation may receive an inquiry such as, "What should I pay attention to when purchasing a new home?" The advice provision unit analyzes the consultation content received by the consultation reception unit and provides appropriate advice. For example, the advice may provide specific advice such as, "When purchasing a new home, it is important to first set a budget and then narrow down the desired area." As a result, the real estate matching system according to the embodiment can search for and propose real estate properties based on the user's desired conditions, and can also analyze the user's consultation content and provide appropriate advice.

[0055] The analysis unit analyzes a user's past search history and behavioral patterns to predict and suggest the user's potential needs. For example, when a user inputs their desired conditions, the analysis unit uses the generation AI to analyze their past search history and extract the characteristics of properties that the user was previously interested in. For example, based on the price range and area of ​​properties previously searched for, the analysis unit suggests properties that may potentially interest the user in addition to the conditions entered by the user. The analysis unit also analyzes the user's behavioral patterns to predict potential needs. For example, if a user frequently searches for a specific area, the analysis unit prioritizes suggesting new property information related to that area. The analysis unit also uses the generation AI to predict and suggest potential needs that the user has not yet noticed based on the user's past search history and behavioral patterns. For example, if a user is interested in a specific floor plan or facilities, the analysis unit suggests properties that match those conditions. This allows the analysis unit to predict the user's potential needs and make more appropriate suggestions.

[0056] The analysis unit can propose properties that are expected to increase in value in the future, taking into account future development plans and infrastructure development information for the area. For example, when a user inputs their desired conditions, the analysis unit allows the generation AI to analyze future development plans for the area and propose properties that are likely to increase in value in the future. For example, the generation AI may prioritize properties in areas where new transportation infrastructure is planned. The analysis unit also considers information on infrastructure development for the area and proposes properties that will be more convenient in the future. For example, it may propose properties in areas where new shopping malls or schools are planned to be built. Furthermore, when analyzing the conditions input by the user, the generation AI proposes properties that are expected to increase in value in the future, based on information on future development plans and infrastructure development for the area. For example, it may propose properties in areas where urban redevelopment projects are underway. This allows the analysis unit to provide useful information to users by proposing properties that are expected to increase in value in the future.

[0057] The analysis unit can use the emotion estimation function to analyze the user's emotion when entering information and improve the interface to reduce stress. For example, when the user enters desired conditions, the analysis unit uses the emotion estimation function to analyze the user's emotion and improve the interface to reduce stress. For example, if the user is feeling stressed, the color or design of the interface can be changed. The analysis unit also uses the emotion estimation function to analyze the user's emotion when entering information in real time and make suggestions to reduce stress. For example, if the user is feeling impatient, the input process can be simplified. The analysis unit also uses the emotion estimation function to analyze the user's emotion when entering desired conditions and improve the interface to reduce stress. For example, music or a message that helps the user relax can be displayed. This reduces the user's stress and provides a more comfortable operating environment.

[0058] The condition input unit supports voice input and gesture input, enabling more intuitive operation. For example, the condition input unit builds a system that supports voice input when a user inputs desired conditions. For example, if a user voice-inputs "a 3LDK apartment in Tokyo," the generation AI searches for properties based on those conditions. The condition input unit also provides an interface that supports gesture input, allowing users to operate intuitively. For example, the user can input desired conditions by performing specific gestures on the screen. The condition input unit also provides an interface that combines voice input and gesture input, allowing users to input desired conditions more intuitively. For example, conditions can be input by voice and fine adjustments can be made with gestures. This allows users to operate more intuitively.

[0059] The analysis unit reflects market trends and price fluctuations in real time, allowing it to provide the latest information. For example, when a user inputs their desired conditions, the generation AI analyzes market trends in real time and provides the latest information. For example, it suggests properties that reflect current market prices and areas with high demand. The analysis unit also analyzes price fluctuations in real time and provides the latest price information to the user. For example, it suggests properties that fit the user's desired price range based on recent price fluctuations. The analysis unit also reflects market trends and price fluctuations in real time when a user inputs their desired conditions, allowing it to provide the latest information. For example, it suggests properties that are likely to increase in value in the future based on current market trends. This allows the user to receive the latest market information.

[0060] The analysis unit can use the emotion estimation function to analyze the user's emotions in real time when they are entering their desired conditions, and make suggestions to elicit positive emotions. For example, the analysis unit can use the emotion estimation function to analyze the user's emotions in real time when they are entering their desired conditions, and make suggestions to elicit positive emotions. For example, the analysis unit can display a message that helps the user relax. The analysis unit can also use the emotion estimation function to analyze the user's emotions in real time when they are entering their desired conditions, and improve the interface to elicit positive emotions. For example, the analysis unit can add interactive elements that the user can enjoy. The analysis unit can also use the emotion estimation function to analyze the user's emotions in real time when they are entering their desired conditions, and make suggestions to elicit positive emotions. For example, the analysis unit can display an encouraging message when the user has completed entering their desired conditions. This can elicit positive emotions from the user, and provide a better operating experience.

[0061] The suggestion unit can suggest the most suitable property by taking into consideration the user's lifestyle and hobbies and preferences. In the suggestion unit, for example, the generation AI analyzes the user's lifestyle and suggests the most suitable property based on that. For example, for a user who likes the outdoors, the suggestion unit suggests properties with parks or nature nearby. The suggestion unit also considers the user's hobbies and preferences and the generation AI suggests the most suitable property. For example, for a user who likes cooking, the suggestion unit suggests properties with spacious kitchens. The suggestion unit also analyzes the user's lifestyle and hobbies and preferences and suggests the most suitable property based on that. For example, for a user who has pets, the suggestion unit suggests properties that allow pets. This makes it possible to suggest properties that suit the user's lifestyle and hobbies and preferences.

[0062] The suggestion unit can simultaneously provide information about the property's surrounding environment. For example, when the generation AI presents property search results, the suggestion unit simultaneously provides information about the property's surrounding environment. For example, it displays information about nearby schools, hospitals, and supermarkets. The suggestion unit also builds a system that provides information about the surrounding environment along with the property search results. For example, it displays information about public transportation and parks near the property. The suggestion unit also simultaneously provides information about the property's surrounding environment when the generation AI presents property search results. For example, it displays information about restaurants and cafes near the property. This allows the user to simultaneously obtain information about the property's surrounding environment.

[0063] The suggestion unit uses the emotion estimation function to analyze the user's emotion when viewing search results, and can highlight property information that elicits positive emotions. For example, when the user views search results, the suggestion unit uses the emotion estimation function to analyze the user's emotion and highlight property information that elicits positive emotions. For example, it highlights property features that are likely to interest the user. The suggestion unit also uses the emotion estimation function to analyze the user's emotion when viewing search results in real time, and makes suggestions that elicit positive emotions. For example, it highlights properties that have designs and facilities that the user prefers. The suggestion unit also uses the emotion estimation function to analyze the user's emotion when viewing search results, and highlights property information that elicits positive emotions. For example, it highlights property features that make the user feel relaxed. This elicits positive emotions from the user and highlights property information.

[0064] The suggestion unit can provide 3D models and virtual tours to enable the user to experience the property more realistically. For example, when the generation AI presents search results for properties, the suggestion unit provides 3D models to enable the user to experience the property more realistically. For example, the suggestion unit displays a 3D model that allows the user to move freely inside the property. The suggestion unit also provides virtual tours to enable the user to experience the property more realistically. For example, the suggestion unit provides a virtual tour that allows the user to have a 360-degree view of the interior of the property. The suggestion unit also provides 3D models and virtual tours when the generation AI presents search results for properties to enable the user to experience the property more realistically. For example, the suggestion unit provides a virtual tour that includes the exterior of the property and the surrounding environment. This enables the user to experience the property more realistically.

[0065] The suggestion unit can display reviews and ratings from past residents, allowing the user to understand how comfortable the property actually is to live in. For example, the suggestion unit builds a system that displays reviews and ratings from past residents when the generation AI presents property search results. For example, it displays reviews about the livability of the property and the surrounding environment. The suggestion unit also displays ratings from past residents so that the user can understand how comfortable the property actually is to live in. For example, it displays reviews that detail the advantages and disadvantages of the property. The suggestion unit also displays reviews and ratings from past residents when the generation AI presents property search results so that the user can understand how comfortable the property actually is to live in. For example, it displays the residents' satisfaction and dissatisfaction as an evaluation score. This allows the user to understand how comfortable the property actually is to live in.

[0066] The suggestion unit can use the emotion estimation function to analyze the user's emotions in real time when viewing search results and make suggestions to elicit positive emotions. For example, when the user views search results, the suggestion unit uses the emotion estimation function to analyze the user's emotions in real time and make suggestions to elicit positive emotions. For example, it highlights features of properties that are likely to interest the user. The suggestion unit also uses the emotion estimation function to analyze the user's emotions in real time when viewing search results and improves the interface to elicit positive emotions. For example, it adds interactive elements that the user can enjoy. The suggestion unit also uses the emotion estimation function to analyze the user's emotions in real time when viewing search results and make suggestions to elicit positive emotions. For example, it highlights features of properties that will help the user relax. This elicits positive emotions from the user and enables better property suggestions.

[0067] The advice providing unit can provide personalized advice by taking into account the user's past behavioral history and search history. For example, the generation AI analyzes the user's past behavioral history and provides personalized advice based on that. For example, the advice providing unit provides the user with the most suitable advice based on the characteristics of properties searched for in the past. The advice providing unit also considers the user's search history and provides personalized advice by taking into account the user's search history. For example, the advice providing unit provides the user with the most suitable advice based on the areas and price ranges searched for in the past. The advice providing unit also analyzes the user's past behavioral history and search history and provides personalized advice based on that. For example, the advice providing unit provides the user with the most suitable advice based on the characteristics of properties that the user was interested in in the past. This makes it possible to provide personalized advice to the user.

[0068] The advice providing unit can provide timely information by reflecting the latest market trends and economic indicators. For example, the generation AI analyzes the latest market trends and provides timely information based on the results. For example, it provides information on current market prices and areas with high demand. The advice providing unit also reflects economic indicators and the generation AI provides timely information. For example, it provides optimal advice to users based on the latest economic indicators. The advice providing unit also analyzes the latest market trends and economic indicators and provides timely information based on the results. For example, it provides optimal advice to users based on current market trends. This makes it possible to provide users with the latest market information.

[0069] The advice providing unit can use the emotion estimation function to analyze the emotion of the user when receiving advice, and provide information to elicit positive emotions. For example, the advice providing unit uses the emotion estimation function to analyze the emotion of the user when receiving advice, and provides information to elicit positive emotions. For example, it provides information that allows the user to relax. Furthermore, the advice providing unit uses the emotion estimation function to analyze the emotion of the user when receiving advice in real time, and provides information to elicit positive emotions. For example, it provides information that the user can enjoy. Furthermore, the advice providing unit uses the emotion estimation function to analyze the emotion of the user when receiving advice, and provides information to elicit positive emotions. For example, it provides information that makes the user feel at ease. This elicits positive emotions from the user, and makes it possible to provide better information.

[0070] The advice providing unit can utilize interactive visual content to provide information in an easy-to-understand format. For example, when the generation AI provides advice or information, the advice providing unit utilizes interactive visual content to provide information in an easy-to-understand format. For example, information is displayed visually using infographics. The advice providing unit also utilizes video content to provide advice or information to the generation AI. For example, information is provided in the form of a video so that it is visually easy for the user to understand. The advice providing unit also utilizes interactive visual content to provide information in an easy-to-understand format when the generation AI provides advice or information. For example, interactive content that can be operated by the user is provided. This makes it easier for the user to understand the information.

[0071] The advice providing unit collects user feedback in real time and can continuously improve the quality of the advice. For example, when the generation AI provides advice or information, the advice providing unit collects user feedback in real time and improves the quality of the advice based on the results. For example, it allows the user to input ratings and comments on the advice provided. The advice providing unit also analyzes user feedback in real time and the generation AI continuously improves the quality of the advice. For example, it adjusts the content of the advice based on the user's opinion. The advice providing unit also collects user feedback in real time when the generation AI provides advice or information and improves the quality of the advice based on the results. For example, it updates the content of the advice based on the user's feedback. This makes it possible to improve the quality of the advice based on the user's feedback.

[0072] The advice providing unit can use the emotion estimation function to analyze the emotion of the user when receiving advice in real time, and provide information to elicit positive emotions. For example, the advice providing unit can use the emotion estimation function to analyze the emotion of the user when receiving advice in real time, and provide information to elicit positive emotions. For example, it can provide information that allows the user to relax. Furthermore, the advice providing unit can use the emotion estimation function to analyze the emotion of the user when receiving advice in real time, and provide information to elicit positive emotions. For example, it can provide information that the user can enjoy. Furthermore, the advice providing unit can use the emotion estimation function to analyze the emotion of the user when receiving advice in real time, and provide information to elicit positive emotions. For example, it can provide information that the user can enjoy. Furthermore, the advice providing unit can use the emotion estimation function to analyze the emotion of the user when receiving advice in real time, and provide information to elicit positive emotions. For example, it can provide information that makes the user feel at ease. This can elicit positive emotions from the user, and enable better information to be provided.

[0073] The user interface can be optimized based on the user's past operation history and preferences. For example, the user interface may be personalized and optimized based on the user's past operation history. For example, functions frequently used by the user may be preferentially displayed. The user interface may also customize the interface design and layout based on the user's preferences. For example, the user's preferred colors and fonts may be used. The user interface may also be personalized and optimized based on the user's past operation history and preferences. For example, pages frequently accessed by the user may be placed on the home screen. This allows the user interface to be optimized to the user's preferences.

[0074] The user interface can make operations smoother by having the generating AI predict the user's operations and suggest the next step. For example, the user interface can predict the user's operations and suggest the next step. For example, after a user performs a property search, related property information is automatically displayed. The user interface can also make operations smoother by having the generating AI predict the user's operations and suggest the next step. For example, after a user completes an input form, the next input field is automatically displayed. The user interface can also make operations smoother by having the generating AI predict the user's operations and suggest the next step. For example, after a user views detailed information about a property, related advice and information is displayed. This makes operations smoother for the user.

[0075] A user interface can use an emotion estimation function to analyze a user's emotion when operating the interface and improve the design to reduce stress. For example, when a user operates the interface, the user interface uses the emotion estimation function to analyze the user's emotion and improve the design to reduce stress. For example, if the user is feeling stressed, the color or design of the interface can be changed. The user interface can also use the emotion estimation function to analyze the user's emotion when operating the interface in real time and make suggestions to reduce stress. For example, if the user is feeling impatient, the operation process can be simplified. The user interface can also use the emotion estimation function to analyze the user's emotion when operating the interface and improve the design to reduce stress. For example, music or a message that helps the user relax can be displayed. This reduces the user's stress and provides a more comfortable operating environment.

[0076] The user interface can support voice assistants and gesture operation, enabling more intuitive operation. For example, the user interface may incorporate a voice assistant to allow the user to operate by voice. For example, when the user commands "search for properties" by voice, the generation AI searches for properties. The user interface may also incorporate gesture operation to allow the user to operate intuitively. For example, the user may be able to input desired conditions by performing specific gestures on the screen. The user interface may also provide an interface that combines voice assistants and gesture operation to allow the user to operate more intuitively. For example, conditions may be input by voice and fine adjustments may be made with gestures. This allows the user to operate more intuitively.

[0077] The user interface allows the generating AI to monitor the user's operations in real time and provide immediate support when a problem occurs. For example, if the user is having trouble operating a system, the generating AI will immediately display a help message. The user interface also builds a system where the generating AI monitors the user's operations in real time and provides support when a problem occurs. For example, if the user receives an error message, the generating AI will present a solution. The user interface also allows the generating AI to monitor the user's operations in real time and provide immediate support when a problem occurs. For example, if the user is unsure how to operate a system, the generating AI will suggest the next step. This allows the user to receive immediate support when they encounter a problem.

[0078] A user interface can use an emotion estimation function to analyze the user's emotions in real time when operating the interface and improve the design to elicit positive emotions. For example, the user interface can use the emotion estimation function to analyze the user's emotions in real time when operating the interface and improve the design to elicit positive emotions. For example, a design that allows the user to relax can be adopted. The user interface can also use the emotion estimation function to analyze the user's emotions in real time when operating the interface and make suggestions to elicit positive emotions. For example, interactive elements that the user can enjoy can be added. The user interface can also use the emotion estimation function to analyze the user's emotions in real time when operating the interface and improve the design to elicit positive emotions. For example, an encouraging message can be displayed when the user completes input. This can elicit positive emotions from the user and provide a better operating experience.

[0079] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0080] The real estate matching system can also be equipped with a health monitoring unit that monitors the user's health condition and suggests properties that are health-conscious. For example, if the user has allergies, properties in areas with fewer allergens can be suggested. Also, if the user likes to exercise, properties with nearby gyms or parks can be suggested. Furthermore, based on the user's health data, it is possible to prioritize suggestions of properties with low air quality and noise levels. This makes it possible to suggest properties that take the user's health condition into consideration.

[0081] The real estate matching system can also be equipped with a family structure analysis unit that takes into account the user's family structure and suggests properties suitable for the whole family. For example, for a family with children, it can suggest properties with nearby schools and parks. It can also suggest properties with barrier-free designs for a family with elderly people. It can also give priority to suggesting pet-friendly properties for a family with pets. This makes it possible to suggest properties suitable for the whole family.

[0082] The real estate matching system can further include a hobby analysis unit that takes into consideration the user's hobbies and interests and suggests properties that are close to facilities related to those hobbies. For example, if the user's hobby is golf, properties with nearby golf courses can be suggested. Also, if the user's hobby is cooking, properties with spacious kitchens can be suggested. Furthermore, if the user's hobby is music, properties with soundproofing can be suggested preferentially. This makes it possible to suggest properties that match the user's hobbies and interests.

[0083] The real estate matching system can also be equipped with a commute time analysis unit that takes into account the user's commute time and suggests properties that are convenient for commuting. For example, if the user inputs the time it takes to commute, the generation AI will analyze the optimal commute route and suggest properties with a short commute time. Also, if the user uses public transportation, it can suggest properties that are close to the nearest station or bus stop. Furthermore, if the user wishes to commute by bicycle, it can also prioritize suggesting properties with bicycle parking spaces. This makes it possible to suggest properties that take the user's commute time into consideration.

[0084] The real estate matching system can further estimate the user's emotions and adjust the order in which properties are proposed based on the estimated emotions. For example, if the user is feeling stressed, properties with relaxing environments can be suggested first. If the user is feeling excited, properties with quiet environments can be suggested first. Furthermore, if the user is feeling anxious, properties with high safety can be suggested first. This makes it possible to suggest properties that take the user's emotions into consideration.

[0085] The real estate matching system can further estimate the user's emotions and dynamically change the interface design based on the estimated emotions. For example, if the user is feeling stressed, the interface color can be changed to a calm color. Alternatively, if the user is feeling relaxed, a bright color can be used. Furthermore, if the user is feeling impatient, the interface operation can be simplified. This provides an interface design that corresponds to the user's emotions, making operation more comfortable.

[0086] The real estate matching system can also estimate the user's emotions and adjust the content of advice based on the estimated emotions. For example, if the user is feeling anxious, it can provide advice that gives a sense of security. If the user is excited, it can also provide advice to help the user stay calm. Furthermore, if the user is unsure, it can also provide advice to help narrow down the options. This allows the system to provide advice that takes the user's emotions into consideration, enabling more appropriate support.

[0087] The real estate matching system can further estimate the user's emotions and adjust the order in which detailed property information is displayed based on the estimated emotions. For example, if the user is relaxed, information about the property's exterior and surrounding environment can be displayed with priority. If the user is feeling anxious, information about the property's price and layout can be displayed with priority. Furthermore, if the user is excited, information about the property's features and convenience can be displayed with priority. This provides information display that matches the user's emotions, making property selection smoother.

[0088] The real estate matching system can also estimate the user's emotions and adjust the property suggestion method based on the estimated emotions. For example, if the user is feeling stressed, the system can simplify the suggestions and narrow down the options. If the user is relaxed, the system can provide detailed information and present multiple options. Furthermore, if the user is excited, the system can make suggestions in stages and provide information in small increments. This provides a suggestion method that takes the user's emotions into consideration, making property selection more effective.

[0089] The real estate matching system can further estimate the user's emotions and adjust the order in which properties are proposed based on the estimated emotions. For example, if the user is feeling stressed, properties with relaxing environments can be suggested first. If the user is feeling excited, properties with quiet environments can be suggested first. Furthermore, if the user is feeling anxious, properties with high safety can be suggested first. This makes it possible to suggest properties that take the user's emotions into consideration.

[0090] The processing flow of the second embodiment will be briefly explained below.

[0091] Step 1: The condition input section allows the user to input desired conditions. For example, the user can input conditions such as location, price, layout, and age of the building. Step 2: The analysis unit analyzes the conditions entered by the condition input unit. For example, the generation AI understands the user's desired conditions and searches for the most suitable real estate property based on them. Step 3: The search unit searches for real estate properties based on the conditions analyzed by the analysis unit. For example, it extracts properties from the database that match the specified conditions, such as location, price range, and floor plan. Step 4: The proposal unit proposes the real estate properties found by the search unit. For example, it suggests properties that meet your desired conditions, such as "The following properties meet your desired conditions." Step 5: The consultation reception unit receives real estate-related inquiries from users. For example, a question such as "What should I pay attention to when buying a new house?" Step 6: The advice provider analyzes the consultation received by the consultation reception unit and provides appropriate advice. For example, they may provide specific advice such as, "When purchasing a new home, it is important to first set a budget and then narrow down the area you want to live in."

[0092] 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.

[0093] 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.

[0094] 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.

[0095] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0096] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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).

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0105] 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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0106] 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.

[0107] 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.

[0108] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0109] 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.

[0110] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0111] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0118] 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.

[0119] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0120] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0121] 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.

[0122] 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.

[0123] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0124] 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.

[0125] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0126] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0136] In the robot 414, 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. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0137] 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.

[0138] 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.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0146] 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."

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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]

[0159] 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 system equipped with a generative AI, a condition input section for inputting desired conditions by a user; an analysis unit that analyzes the conditions input by the condition input unit; a search unit that searches for real estate properties based on the conditions analyzed by the analysis unit; a proposal unit that proposes the real estate property searched by the search unit; a consultation reception unit that receives consultations regarding real estate from users; an advice providing unit that analyzes the consultation content received by the consultation receiving unit and provides appropriate advice. A system characterized by:

2. The analysis unit Analyze the user's past search history and behavioral patterns to predict and suggest potential needs of the user.

2. The system of claim 1.

3. The analysis unit Taking into consideration future development plans and infrastructure development information for the area, we propose properties that are expected to increase in value in the future.

2. The system of claim 1.

4. The analysis unit Analyze the emotions of the user when inputting information and improve the interface to reduce stress.

2. The system of claim 1.

5. The condition input unit Supports voice and gesture input for more intuitive operation 2. The system of claim 1.

6. The analysis unit Provides the latest information by reflecting market trends and price fluctuations in real time 2. The system of claim 1.

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