system

Abstract

The system according to this embodiment aims to improve the accuracy of property selection by collecting and providing detailed information about the area surrounding the property to the user. [Solution] The system according to the embodiment comprises a collection unit, a generation unit, and a provision unit. The collection unit collects detailed data about the area around the property. The generation unit generates an aggregated statement based on the data collected by the collection unit. The provision unit provides the aggregated statement generated by the generation unit to the user.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot 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 an explanation of the chatbot's 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the detailed information of the property and the information of the surrounding area are limited, and there is a possibility that a gap between the image and the reality may occur in property selection.

[0005] The system according to the embodiment aims to improve the accuracy of property selection by collecting detailed information around the property and providing it to the user.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, a generation unit, and a provision unit. The collection unit collects detailed data around the property. The generation unit generates a summary text based on the data collected by the collection unit. The provision unit provides the summary text generated by the generation unit to the user. [Effects of the Invention]

[0007] The system according to this embodiment can improve the accuracy of property selection by collecting and providing detailed information about the area surrounding the property to the user. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

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

[0019] The smart device 14 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example of form 1) The property information provision system according to an embodiment of the present invention is a system that utilizes generative AI to provide detailed information about the surrounding area of ​​a property when searching for a property. The property information provision system solves the problem that conventional property information alone often leads to discrepancies between the image and reality when actually viewing the property, or discrepancies regarding the safety of the surrounding area, increasing the time cost of property searching. Furthermore, the information included in property details is limited, making it difficult for people from rural areas in particular to grasp the atmosphere during a viewing, which carries the risk of a decline in quality of life after moving in. The property information provision system utilizes generative AI to summarize local information, UGC content, and pedestrian flow data held by the group, and provides detailed information about the surrounding area as a summary text so that users can grasp not only the property but also the atmosphere of the town and the image of life there. The property information provision system provides a chat-based UI, enabling flexible answers to personalized questions such as "I want to know about nearby educational institutions" or "Are there any good ramen restaurants nearby?" The property information provision system also provides a function that can recommend other properties with similar conditions or towns with a similar atmosphere. For example, the property information provision system acquires detailed data about the surrounding area of ​​a property that the user is interested in. For example, the property information provision system generates aggregated text according to the values ​​that the generative AI prioritizes. The property information system regenerates aggregated text, weighting the desired information based on the user's natural language feedback (e.g., "I want to know more about nearby restaurants"). If the user wants to delve deeper into facility names mentioned in the text, the system also provides summarized results such as reviews and links to relevant services. The system also suggests similar properties nearby and areas with similar living environments as needed. This mechanism allows the system to summarize and present information about the property's surrounding area in an easy-to-understand text format. Based on user feedback, the system can provide information at a finer granularity, such as restaurants and hospitals. Furthermore, by summarizing and presenting reviews of nearby educational institutions, hospitals, and restaurants, the system can also provide insights into congestion and peak hours around the property.Furthermore, if there are few properties that meet the user's desired conditions, the property information system can recommend neighborhoods and properties with a similar atmosphere, providing a variety of options and contributing to profit generation within the group. This allows the property information system to provide detailed information about the surrounding area when searching for a property, reducing the gap in the user's property selection process.

[0029] The property information provision system according to this embodiment comprises a collection unit, a generation unit, and a provision unit. The collection unit collects detailed data about the area around the property. The collection unit can collect detailed data such as traffic information, surrounding facility information, and public safety information. For example, as traffic information, the collection unit collects information on the operation status of public transportation around the property and traffic congestion. The collection unit can also collect information on surrounding facilities such as restaurants, educational institutions, and hospitals around the property. Furthermore, the collection unit can also collect information on crime rates around the property and the locations of police stations as public safety information. The generation unit generates an aggregated text based on the data collected by the collection unit. The generation unit, for example, uses a generation AI to summarize the collected data and provides it as an easy-to-understand text. For example, the generation unit summarizes the accessibility of transportation around the property based on the collected traffic information. The generation unit can also summarize the living environment around the property based on the collected surrounding facility information. Furthermore, the generation unit can also summarize the public safety situation around the property based on the collected public safety information. The provision unit provides the aggregated text generated by the generation unit to the user. The providing unit, for example, displays the generated aggregated text on the user's device. The providing unit provides the generated aggregated text to the user, for example, through a web application or a mobile application. The providing unit can also send the generated aggregated text to the user via email. Furthermore, the providing unit can print the generated aggregated text and provide it on paper. In this way, the property information provision system according to the embodiment can reduce gaps in the property selection process by collecting and providing detailed information about the area surrounding the property to the user.

[0030] The data collection unit collects detailed data about the area surrounding the property. For example, it can collect detailed data such as traffic information, surrounding facilities information, and public safety information. Specifically, for traffic information, it collects information on the operation status of public transportation around the property and traffic congestion. This includes bus and train schedules, delay information, and the location of the nearest station and bus stop. For traffic congestion information, it can also collect congestion status and accident information on major roads. For surrounding facilities information, it collects information on restaurants, educational institutions, hospitals, supermarkets, convenience stores, parks, etc., around the property. This includes the operating hours, ratings, and details of the services offered for each facility. Furthermore, for public safety information, it collects information on the crime rate around the property and the location of police stations. This includes past crime data, types of crimes, times of occurrence, and police patrol frequency. The data collection unit can obtain this information through publicly available databases and APIs on the internet. It can also collect information from official websites of local governments, police stations, and transportation companies. Furthermore, the data collection unit is required to update the data in real time and always maintain the latest information. This allows the data collection unit to build a foundation for comprehensively collecting detailed information about the area surrounding a property and providing it to users.

[0031] The generation unit generates aggregated text based on the data collected by the collection unit. For example, the generation unit uses a generation AI to summarize the collected data and provide it as easy-to-understand text. Specifically, the generation AI uses natural language processing technology to analyze the collected data and extract important information. For example, when summarizing the convenience of transportation around a property based on transportation information, it includes the distance to the nearest station or bus stop, the frequency of service, and the time required to major destinations. When summarizing the living environment around a property based on surrounding facilities information, it includes the number of nearby restaurants and supermarkets, the presence of highly-rated educational institutions, and the availability of medical facilities. When summarizing the safety situation around a property based on public safety information, it includes past crime rates, police patrol frequency, and the safety awareness of local residents. The generation AI integrates this information and generates text in a format that is easy for users to understand. Furthermore, the generation unit can customize the content and format of the generated aggregated text according to user needs. For example, it can generate summaries that focus on specific information or summaries for comparing multiple properties. This allows the generation unit to effectively utilize the collected data and provide useful information to the user.

[0032] The provider unit provides users with the aggregated text generated by the generator unit. For example, the provider unit displays the generated aggregated text on the user's device. Specifically, it provides the generated aggregated text to users through web applications or mobile applications. Through these applications, users can easily view detailed information about the property's surroundings. The provider unit can also send the generated aggregated text to users via email. This allows users to easily receive property information and refer to it when needed. Furthermore, the provider unit can print the generated aggregated text and provide it in paper format. This caters to users without internet access or those who prefer paper-based information. The provider unit can collect user feedback and continuously improve the accuracy and content of the information it provides. For example, if a user values ​​specific information, the provider unit can adjust the generation of aggregated text to highlight that information. Additionally, by supporting multiple languages, the provider unit can provide information to users who speak different languages. This allows the provider unit to provide the generated aggregated text to users in diverse ways, reducing gaps in the property selection process.

[0033] The feedback unit can receive feedback and reflect it in the generation unit. For example, the feedback unit can collect user ratings and comments and provide them to the generation unit. For example, the feedback unit can evaluate the aggregated sentences provided by users and reflect the evaluation results in the generation unit. The feedback unit can also collect user comments, and the generation unit can regenerate the aggregated sentences based on those comments. Furthermore, the feedback unit can collect user scores, and the generation unit can adjust the aggregated sentences based on those scores. In this way, the feedback unit can provide more personalized information by reflecting user feedback. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input user ratings and comments into AI and have the AI ​​perform feedback analysis.

[0034] The recommendation system can suggest similar neighborhoods or properties when there are few properties that meet the user's desired conditions. For example, the recommendation system can search for and suggest similar neighborhoods or properties based on the user's desired conditions. For example, the recommendation system can suggest similar neighborhoods or properties based on the user's desired price range or regional characteristics. The recommendation system can also suggest similar neighborhoods or properties based on the user's desired amenities and environment. Furthermore, the recommendation system can suggest similar neighborhoods or properties based on the user's desired lifestyle. In this way, the recommendation system can provide users with a variety of options and broaden their range of property choices. Some or all of the above processing in the recommendation system may be performed using AI, for example, or not. For example, the recommendation system can input the user's desired conditions into AI and have the AI ​​perform a search for similar neighborhoods or properties.

[0035] The data collection unit can analyze the user's past search history during data collection and select the optimal data collection method. For example, the data collection unit can analyze the characteristics of properties the user has searched for in the past and prioritize the collection of information on similar properties. For example, the data collection unit can collect relevant data based on information about regions the user has searched for in the past. The data collection unit can also analyze keywords the user has searched for in the past and collect information related to them. This allows the data collection unit to collect more relevant information based on the user's past search history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's search history data into a generating AI and have the generating AI select the optimal data collection method.

[0036] The data collection unit can collect real-time information on changes in the surrounding environment of a property during the collection process. For example, the data collection unit can collect real-time traffic conditions around the property to understand the degree of congestion. For example, the data collection unit can collect real-time weather information around the property to understand changes in the weather. The data collection unit can also collect real-time event information around the property to understand the vibrancy of the area. As a result, the data collection unit can grasp the latest information on the surrounding environment of the property in real time. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input traffic condition data around the property into a generating AI and have the generating AI process the changes in traffic conditions.

[0037] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during the collection process. For example, the data collection unit can prioritize the collection of information on properties close to the user's current location. If the user is interested in a particular area, the data collection unit can prioritize the collection of detailed information on that area. Furthermore, if the user is on the move, the data collection unit can collect the most relevant property information in real time based on their current location. This allows the data collection unit to collect more relevant information based on the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location data into a generating AI and have the generating AI perform the collection of highly relevant data.

[0038] The data collection unit can analyze the user's social media activity and collect relevant data during the collection process. For example, if the user frequently posts about "cafes" on social media, the data collection unit can prioritize collecting information about nearby cafes. For example, if the user frequently posts about "safety" on social media, the data collection unit can prioritize collecting information about safety. The data collection unit can also prioritize collecting information about nearby events if the user frequently posts about "events" on social media. This allows the data collection unit to collect more relevant information based on the user's social media activity. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media data into a generating AI and have the generating AI collect the relevant data.

[0039] The generation unit can adjust the level of detail in the aggregated text based on the importance of the property during generation. For example, the generation unit can generate an aggregated text containing detailed information for important properties. For example, the generation unit can generate a concise aggregated text for properties of low importance. The generation unit can also generate an aggregated text with adjusted granularity of information according to the importance of the property. This allows the generation unit to provide more appropriate information by generating aggregated texts with a level of detail appropriate to the importance of the property. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input property importance data into a generation AI and have the generation AI perform the adjustment of the level of detail in the aggregated text.

[0040] The generation unit can apply different generation algorithms depending on the property category during generation. For example, in the case of residential properties, the generation unit can generate aggregated sentences that emphasize information about livability. For example, in the case of commercial properties, the generation unit can generate aggregated sentences that emphasize information about business. Furthermore, in the case of leisure properties, the generation unit can generate aggregated sentences that emphasize information about entertainment. In this way, the generation unit can provide more appropriate information by applying a generation algorithm appropriate to the property category. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input property category data into a generation AI and have the generation AI execute the application of the generation algorithm.

[0041] The generation unit can determine the priority of aggregate statements based on the property submission date during generation. For example, the generation unit can prioritize generating aggregate statements for newly submitted properties. For example, it can postpone generating aggregate statements for older properties. The generation unit can also adjust the generation order of aggregate statements according to the submission date. This allows the generation unit to provide more appropriate information by generating aggregate statements with priority according to the property submission date. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input property submission date data into a generation AI and have the generation AI determine the priority of aggregate statements.

[0042] The generation unit can adjust the order of aggregate sentences based on the relevance of properties during generation. For example, the generation unit can prioritize generating aggregate sentences for highly relevant properties. For example, it can postpone generating aggregate sentences for less relevant properties. The generation unit can also adjust the generation order of aggregate sentences according to the relevance of properties. This allows the generation unit to provide more appropriate information by generating aggregate sentences in an order that corresponds to the relevance of properties. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input property relevance data into a generation AI and have the generation AI perform the adjustment of the order of aggregate sentences.

[0043] The service provider can select the optimal display method by referring to the user's past operation history at the time of service provision. For example, the service provider may prioritize providing display methods that the user has previously preferred. For example, the service provider may suggest the optimal display method based on the user's past operation history. The service provider may also provide a customized display method based on the display methods the user has previously used. This allows the service provider to provide a more appropriate display method based on the user's past operation history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider may input the user's operation history data into a generating AI and have the generating AI select the optimal display method.

[0044] The service provider can select the optimal display method at the time of delivery, taking into account the user's device information. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. For example, if the user is using a tablet, the service provider can provide a display method optimized for a larger screen. Furthermore, if the user is using a smartwatch, the service provider can provide a concise and highly visible display method. In this way, the service provider can provide a more appropriate display method based on the user's device information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's device information into a generating AI and have the generating AI select the optimal display method.

[0045] The information provider can prioritize providing highly relevant information by considering the user's geographical location at the time of delivery. For example, the provider can prioritize providing information on properties close to the user's current location. For example, if the user is interested in a particular area, the provider can prioritize providing detailed information on that area. Furthermore, if the user is on the move, the provider can provide the most suitable property information in real time based on their current location. This allows the provider to provide more relevant information based on the user's geographical location. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the provider can input the user's geographical location data into a generating AI and have the generating AI perform the task of providing highly relevant information.

[0046] The service provider can analyze the user's social media activity and provide relevant information at the time of delivery. For example, if the user frequently posts "cafe" on social media, the service provider can prioritize providing information about nearby cafes. For example, if the user frequently posts "safety" on social media, the service provider can prioritize providing safety information. Furthermore, if the user frequently posts "events" on social media, the service provider can prioritize providing information about nearby events. This allows the service provider to provide more relevant information based on the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media data into a generating AI and have the generating AI perform the provision of relevant information.

[0047] The feedback unit can select the optimal feedback method by referring to the user's past feedback history when providing feedback. For example, if the user has provided detailed feedback in the past, the feedback unit can prioritize collecting detailed feedback. For example, if the user has provided concise feedback in the past, the feedback unit can prioritize collecting concise feedback. The feedback unit can also analyze the user's past feedback history and suggest the optimal feedback method. This allows the feedback unit to provide a more appropriate feedback method based on the user's past feedback history. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's feedback history data into a generating AI and have the generating AI select the optimal feedback method.

[0048] The feedback unit can prioritize collecting highly relevant feedback by considering the user's geographical location information when providing feedback. For example, the feedback unit can prioritize collecting feedback on properties close to the user's current location. For example, if the user is interested in a particular region, the feedback unit can prioritize collecting feedback on that region. Furthermore, if the user is on the move, the feedback unit can collect the most relevant feedback in real time based on their current location. This allows the feedback unit to collect more relevant feedback based on the user's geographical location information. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's geographical location data into a generating AI and have the generating AI collect highly relevant feedback.

[0049] The recommendation unit can select the optimal recommendation method by referring to the user's past search history when making recommendations. For example, the recommendation unit can analyze the characteristics of properties the user has searched for in the past and recommend similar properties. For example, the recommendation unit can recommend related properties and regions based on information about regions the user has searched for in the past. The recommendation unit can also analyze keywords the user has searched for in the past and recommend properties and regions related to them. In this way, the recommendation unit can provide a more appropriate recommendation method based on the user's past search history. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the user's search history data into a generating AI and have the generating AI select the optimal recommendation method.

[0050] The recommendation unit can prioritize providing highly relevant recommendations by considering the user's geographical location information during the recommendation process. For example, the recommendation unit can prioritize recommending properties or areas close to the user's current location. If the user is interested in a particular area, the recommendation unit can prioritize recommending properties or areas related to that area. Furthermore, if the user is on the move, the recommendation unit can recommend the most suitable properties or areas in real time based on their current location. This allows the recommendation unit to provide more relevant recommendations based on the user's geographical location information. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the user's geographical location data into a generating AI and have the generating AI perform the task of providing highly relevant recommendations.

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

[0052] The data collection unit can analyze a user's past purchase history and collect relevant data. For example, the data collection unit can analyze the characteristics of properties the user has purchased in the past and prioritize collecting information on similar properties. For example, the data collection unit can collect relevant data based on information about the areas where the user has purchased in the past. The data collection unit can also analyze the price range of properties the user has purchased in the past and collect information related to that. In this way, the data collection unit can collect more relevant information based on the user's past purchase history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's purchase history data into a generating AI and have the generating AI select the optimal data collection method.

[0053] The recommendation unit can analyze a user's past travel history and recommend relevant properties and regions. For example, the recommendation unit can analyze the characteristics of regions the user has visited in the past and recommend similar regions. For example, the recommendation unit can recommend relevant properties based on information about accommodations the user has stayed at in the past. The recommendation unit can also analyze information about tourist destinations the user has visited in the past and recommend properties and regions related to them. In this way, the recommendation unit can provide a more appropriate recommendation method based on the user's past travel history. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the user's travel history data into a generating AI and have the generating AI select the optimal recommendation method.

[0054] The data collection unit can prioritize collecting highly relevant data while considering the user's health status. For example, if the user is health-conscious, the data collection unit can prioritize collecting information on nearby hospitals and fitness facilities. If the user has a specific health problem, the data collection unit can prioritize collecting information on facilities related to that problem. Furthermore, if the user wants to lead a healthy lifestyle, the data collection unit can prioritize collecting information on health-related events and activities. This allows the data collection unit to collect more relevant information based on the user's health status. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's health data into a generating AI and have the generating AI collect highly relevant data.

[0055] The generation unit can adjust the content of the aggregated text based on the energy efficiency of the property during generation. For example, for properties with high energy efficiency, the generation unit can generate an aggregated text that includes detailed energy efficiency information. For properties with low energy efficiency, the generation unit can generate an aggregated text that includes areas for improvement. The generation unit can also adjust the granularity of the information according to the energy efficiency of the property when generating the aggregated text. This allows the generation unit to provide more appropriate information by generating an aggregated text with content that matches the energy efficiency of the property. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the energy efficiency data of the property into a generation AI and have the generation AI perform the adjustment of the content of the aggregated text.

[0056] The service provider can select the optimal display method by referring to the user's past purchase history at the time of delivery. For example, the service provider can analyze the characteristics of properties that the user has previously preferred to purchase and prioritize the display of information on similar properties. For example, the service provider can display information on related properties based on information about the areas in which the user has previously purchased. The service provider can also analyze the price range of properties that the user has previously purchased and display information related to that range. In this way, the service provider can provide more relevant information based on the user's past purchase history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's purchase history data into a generating AI and have the generating AI select the optimal display method.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The data collection unit collects detailed data about the area surrounding the property. The data collection unit can collect detailed data such as traffic information, information on surrounding facilities, and safety information. For example, as traffic information, the data collection unit collects information on the operation status of public transportation around the property and traffic congestion. The data collection unit can also collect information on surrounding facilities such as restaurants, educational institutions, and hospitals around the property. Furthermore, the data collection unit can also collect information on safety, such as the crime rate around the property and the location of police stations. Step 2: The generation unit generates an aggregated text based on the data collected by the collection unit. The generation unit, for example, uses a generation AI to summarize the collected data and provide it as an easy-to-understand text. For example, the generation unit can summarize the transportation convenience around the property based on the collected traffic information. The generation unit can also summarize the living environment around the property based on the collected information on surrounding facilities. Furthermore, the generation unit can summarize the safety situation around the property based on the collected safety information. Step 3: The provider provides the aggregated text generated by the generator to the user. The provider, for example, displays the generated aggregated text on the user's device. The provider provides the generated aggregated text to the user, for example, through a web application or mobile application. The provider can also send the generated aggregated text to the user via email. Furthermore, the provider can print the generated aggregated text and provide it on paper.

[0059] (Example of form 2) The property information provision system according to an embodiment of the present invention is a system that utilizes generative AI to provide detailed information about the surrounding area of ​​a property when searching for a property. The property information provision system solves the problem that conventional property information alone often leads to discrepancies between the image and reality when actually viewing the property, or discrepancies regarding the safety of the surrounding area, increasing the time cost of property searching. Furthermore, the information included in property details is limited, making it difficult for people from rural areas in particular to grasp the atmosphere during a viewing, which carries the risk of a decline in quality of life after moving in. The property information provision system utilizes generative AI to summarize local information, UGC content, and pedestrian flow data held by the group, and provides detailed information about the surrounding area as a summary text so that users can grasp not only the property but also the atmosphere of the town and the image of life there. The property information provision system provides a chat-based UI, enabling flexible answers to personalized questions such as "I want to know about nearby educational institutions" or "Are there any good ramen restaurants nearby?" The property information provision system also provides a function that can recommend other properties with similar conditions or towns with a similar atmosphere. For example, the property information provision system acquires detailed data about the surrounding area of ​​a property that the user is interested in. For example, the property information provision system generates aggregated text according to the values ​​that the generative AI prioritizes. The property information system regenerates aggregated text, weighting the desired information based on the user's natural language feedback (e.g., "I want to know more about nearby restaurants"). If the user wants to delve deeper into facility names mentioned in the text, the system also provides summarized results such as reviews and links to relevant services. The system also suggests similar properties nearby and areas with similar living environments as needed. This mechanism allows the system to summarize and present information about the property's surrounding area in an easy-to-understand text format. Based on user feedback, the system can provide information at a finer granularity, such as restaurants and hospitals. Furthermore, by summarizing and presenting reviews of nearby educational institutions, hospitals, and restaurants, the system can also provide insights into congestion and peak hours around the property.Furthermore, if there are few properties that meet the user's desired conditions, the property information system can recommend neighborhoods and properties with a similar atmosphere, providing a variety of options and contributing to profit generation within the group. This allows the property information system to provide detailed information about the surrounding area when searching for a property, reducing the gap in the user's property selection process.

[0060] The property information provision system according to this embodiment comprises a collection unit, a generation unit, and a provision unit. The collection unit collects detailed data about the area around the property. The collection unit can collect detailed data such as traffic information, surrounding facility information, and public safety information. For example, as traffic information, the collection unit collects information on the operation status of public transportation around the property and traffic congestion. The collection unit can also collect information on surrounding facilities such as restaurants, educational institutions, and hospitals around the property. Furthermore, the collection unit can also collect information on crime rates around the property and the locations of police stations as public safety information. The generation unit generates an aggregated text based on the data collected by the collection unit. The generation unit, for example, uses a generation AI to summarize the collected data and provides it as an easy-to-understand text. For example, the generation unit summarizes the accessibility of transportation around the property based on the collected traffic information. The generation unit can also summarize the living environment around the property based on the collected surrounding facility information. Furthermore, the generation unit can also summarize the public safety situation around the property based on the collected public safety information. The provision unit provides the aggregated text generated by the generation unit to the user. The providing unit, for example, displays the generated aggregated text on the user's device. The providing unit provides the generated aggregated text to the user, for example, through a web application or a mobile application. The providing unit can also send the generated aggregated text to the user via email. Furthermore, the providing unit can print the generated aggregated text and provide it on paper. In this way, the property information provision system according to the embodiment can reduce gaps in the property selection process by collecting and providing detailed information about the area surrounding the property to the user.

[0061] The data collection unit collects detailed data about the area surrounding the property. For example, it can collect detailed data such as traffic information, surrounding facilities information, and public safety information. Specifically, for traffic information, it collects information on the operation status of public transportation around the property and traffic congestion. This includes bus and train schedules, delay information, and the location of the nearest station and bus stop. For traffic congestion information, it can also collect congestion status and accident information on major roads. For surrounding facilities information, it collects information on restaurants, educational institutions, hospitals, supermarkets, convenience stores, parks, etc., around the property. This includes the operating hours, ratings, and details of the services offered for each facility. Furthermore, for public safety information, it collects information on the crime rate around the property and the location of police stations. This includes past crime data, types of crimes, times of occurrence, and police patrol frequency. The data collection unit can obtain this information through publicly available databases and APIs on the internet. It can also collect information from official websites of local governments, police stations, and transportation companies. Furthermore, the data collection unit is required to update the data in real time and always maintain the latest information. This allows the data collection unit to build a foundation for comprehensively collecting detailed information about the area surrounding a property and providing it to users.

[0062] The generation unit generates aggregated text based on the data collected by the collection unit. For example, the generation unit uses a generation AI to summarize the collected data and provide it as easy-to-understand text. Specifically, the generation AI uses natural language processing technology to analyze the collected data and extract important information. For example, when summarizing the convenience of transportation around a property based on transportation information, it includes the distance to the nearest station or bus stop, the frequency of service, and the time required to major destinations. When summarizing the living environment around a property based on surrounding facilities information, it includes the number of nearby restaurants and supermarkets, the presence of highly-rated educational institutions, and the availability of medical facilities. When summarizing the safety situation around a property based on public safety information, it includes past crime rates, police patrol frequency, and the safety awareness of local residents. The generation AI integrates this information and generates text in a format that is easy for users to understand. Furthermore, the generation unit can customize the content and format of the generated aggregated text according to user needs. For example, it can generate summaries that focus on specific information or summaries for comparing multiple properties. This allows the generation unit to effectively utilize the collected data and provide useful information to the user.

[0063] The provider unit provides users with the aggregated text generated by the generator unit. For example, the provider unit displays the generated aggregated text on the user's device. Specifically, it provides the generated aggregated text to users through web applications or mobile applications. Through these applications, users can easily view detailed information about the property's surroundings. The provider unit can also send the generated aggregated text to users via email. This allows users to easily receive property information and refer to it when needed. Furthermore, the provider unit can print the generated aggregated text and provide it in paper format. This caters to users without internet access or those who prefer paper-based information. The provider unit can collect user feedback and continuously improve the accuracy and content of the information it provides. For example, if a user values ​​specific information, the provider unit can adjust the generation of aggregated text to highlight that information. Additionally, by supporting multiple languages, the provider unit can provide information to users who speak different languages. This allows the provider unit to provide the generated aggregated text to users in diverse ways, reducing gaps in the property selection process.

[0064] The feedback unit can receive feedback and reflect it in the generation unit. For example, the feedback unit can collect user ratings and comments and provide them to the generation unit. For example, the feedback unit can evaluate the aggregated sentences provided by users and reflect the evaluation results in the generation unit. The feedback unit can also collect user comments, and the generation unit can regenerate the aggregated sentences based on those comments. Furthermore, the feedback unit can collect user scores, and the generation unit can adjust the aggregated sentences based on those scores. In this way, the feedback unit can provide more personalized information by reflecting user feedback. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input user ratings and comments into AI and have the AI ​​perform feedback analysis.

[0065] The recommendation system can suggest similar neighborhoods or properties when there are few properties that meet the user's desired conditions. For example, the recommendation system can search for and suggest similar neighborhoods or properties based on the user's desired conditions. For example, the recommendation system can suggest similar neighborhoods or properties based on the user's desired price range or regional characteristics. The recommendation system can also suggest similar neighborhoods or properties based on the user's desired amenities and environment. Furthermore, the recommendation system can suggest similar neighborhoods or properties based on the user's desired lifestyle. In this way, the recommendation system can provide users with a variety of options and broaden their range of property choices. Some or all of the above processing in the recommendation system may be performed using AI, for example, or not. For example, the recommendation system can input the user's desired conditions into AI and have the AI ​​perform a search for similar neighborhoods or properties.

[0066] The data collection unit can estimate the user's emotions and adjust the types of data collected based on the estimated emotions. For example, if the user is feeling anxious, the data collection unit may prioritize collecting information on public safety and nearby police stations. If the user is excited, the data collection unit may prioritize collecting information on entertainment facilities and events. If the user is relaxed, the data collection unit may prioritize collecting information on relaxing places such as parks and cafes. This allows the data collection unit to collect data according to the user's emotions and provide more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0067] The data collection unit can analyze the user's past search history during data collection and select the optimal data collection method. For example, the data collection unit can analyze the characteristics of properties the user has searched for in the past and prioritize the collection of information on similar properties. For example, the data collection unit can collect relevant data based on information about regions the user has searched for in the past. The data collection unit can also analyze keywords the user has searched for in the past and collect information related to them. This allows the data collection unit to collect more relevant information based on the user's past search history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's search history data into a generating AI and have the generating AI select the optimal data collection method.

[0068] The data collection unit can collect real-time information on changes in the surrounding environment of a property during the collection process. For example, the data collection unit can collect real-time traffic conditions around the property to understand the degree of congestion. For example, the data collection unit can collect real-time weather information around the property to understand changes in the weather. The data collection unit can also collect real-time event information around the property to understand the vibrancy of the area. As a result, the data collection unit can grasp the latest information on the surrounding environment of the property in real time. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input traffic condition data around the property into a generating AI and have the generating AI process the changes in traffic conditions.

[0069] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is feeling anxious, the data collection unit may prioritize collecting safety information. If the user is excited, the data collection unit may prioritize collecting information about entertainment facilities. If the user is relaxed, the data collection unit may prioritize collecting information about parks and cafes. In this way, the data collection unit can provide more appropriate information by prioritizing data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0070] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during the collection process. For example, the data collection unit can prioritize the collection of information on properties close to the user's current location. If the user is interested in a particular area, the data collection unit can prioritize the collection of detailed information on that area. Furthermore, if the user is on the move, the data collection unit can collect the most relevant property information in real time based on their current location. This allows the data collection unit to collect more relevant information based on the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location data into a generating AI and have the generating AI perform the collection of highly relevant data.

[0071] The data collection unit can analyze the user's social media activity and collect relevant data during the collection process. For example, if the user frequently posts about "cafes" on social media, the data collection unit can prioritize collecting information about nearby cafes. For example, if the user frequently posts about "safety" on social media, the data collection unit can prioritize collecting information about safety. The data collection unit can also prioritize collecting information about nearby events if the user frequently posts about "events" on social media. This allows the data collection unit to collect more relevant information based on the user's social media activity. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media data into a generating AI and have the generating AI collect the relevant data.

[0072] The generation unit can estimate the user's emotions and adjust the expression of the summary sentence based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate a summary sentence with soft expression. If the user is in a hurry, for example, the generation unit can generate a summary sentence with concise and to-the-point expression. Furthermore, if the user is excited, the generation unit can generate a summary sentence with visually stimulating expression. In this way, the generation unit can provide more appropriate information by generating a summary sentence with an expression that matches the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0073] The generation unit can adjust the level of detail in the aggregated text based on the importance of the property during generation. For example, the generation unit can generate an aggregated text containing detailed information for important properties. For example, the generation unit can generate a concise aggregated text for properties of low importance. The generation unit can also generate an aggregated text with adjusted granularity of information according to the importance of the property. This allows the generation unit to provide more appropriate information by generating aggregated texts with a level of detail appropriate to the importance of the property. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input property importance data into a generation AI and have the generation AI perform the adjustment of the level of detail in the aggregated text.

[0074] The generation unit can apply different generation algorithms depending on the property category during generation. For example, in the case of residential properties, the generation unit can generate aggregated sentences that emphasize information about livability. For example, in the case of commercial properties, the generation unit can generate aggregated sentences that emphasize information about business. Furthermore, in the case of leisure properties, the generation unit can generate aggregated sentences that emphasize information about entertainment. In this way, the generation unit can provide more appropriate information by applying a generation algorithm appropriate to the property category. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input property category data into a generation AI and have the generation AI execute the application of the generation algorithm.

[0075] The generation unit can estimate the user's emotions and adjust the length of the summary sentence based on the estimated emotions. For example, if the user is in a hurry, the generation unit can generate a short, concise summary sentence. If the user is relaxed, for example, the generation unit can generate a longer summary sentence that includes detailed explanations. Furthermore, if the user is excited, the generation unit can generate a summary sentence with visually stimulating effects. In this way, the generation unit can provide more appropriate information by generating summary sentences of a length appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0076] The generation unit can determine the priority of aggregate statements based on the property submission date during generation. For example, the generation unit can prioritize generating aggregate statements for newly submitted properties. For example, it can postpone generating aggregate statements for older properties. The generation unit can also adjust the generation order of aggregate statements according to the submission date. This allows the generation unit to provide more appropriate information by generating aggregate statements with priority according to the property submission date. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input property submission date data into a generation AI and have the generation AI determine the priority of aggregate statements.

[0077] The generation unit can adjust the order of aggregate sentences based on the relevance of properties during generation. For example, the generation unit can prioritize generating aggregate sentences for highly relevant properties. For example, it can postpone generating aggregate sentences for less relevant properties. The generation unit can also adjust the generation order of aggregate sentences according to the relevance of properties. This allows the generation unit to provide more appropriate information by generating aggregate sentences in an order that corresponds to the relevance of properties. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input property relevance data into a generation AI and have the generation AI perform the adjustment of the order of aggregate sentences.

[0078] The service provider can estimate the user's emotions and adjust the way information is displayed based on the estimated emotions. For example, if the user is tense, the service provider can provide a simple and highly visible display method. If the user is relaxed, the service provider can provide a display method that includes detailed information. If the user is in a hurry, the service provider can also provide a concise display method. In this way, the service provider can provide more appropriate information by providing information in a display method that suits the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0079] The service provider can select the optimal display method by referring to the user's past operation history at the time of service provision. For example, the service provider may prioritize providing display methods that the user has previously preferred. For example, the service provider may suggest the optimal display method based on the user's past operation history. The service provider may also provide a customized display method based on the display methods the user has previously used. This allows the service provider to provide a more appropriate display method based on the user's past operation history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider may input the user's operation history data into a generating AI and have the generating AI select the optimal display method.

[0080] The service provider can select the optimal display method at the time of delivery, taking into account the user's device information. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. For example, if the user is using a tablet, the service provider can provide a display method optimized for a larger screen. Furthermore, if the user is using a smartwatch, the service provider can provide a concise and highly visible display method. In this way, the service provider can provide a more appropriate display method based on the user's device information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's device information into a generating AI and have the generating AI select the optimal display method.

[0081] The information provider can estimate the user's emotions and determine the priority of the information to provide based on the estimated emotions. For example, if the user is feeling anxious, the information provider can prioritize providing safety information. For example, if the user is excited, the information provider can prioritize providing information about entertainment facilities. Also, if the user is relaxed, the information provider can prioritize providing information about parks and cafes. In this way, the information provider can provide more appropriate information by prioritizing information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the information provider can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0082] The information provider can prioritize providing highly relevant information by considering the user's geographical location at the time of delivery. For example, the provider can prioritize providing information on properties close to the user's current location. For example, if the user is interested in a particular area, the provider can prioritize providing detailed information on that area. Furthermore, if the user is on the move, the provider can provide the most suitable property information in real time based on their current location. This allows the provider to provide more relevant information based on the user's geographical location. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the provider can input the user's geographical location data into a generating AI and have the generating AI perform the task of providing highly relevant information.

[0083] The service provider can analyze the user's social media activity and provide relevant information at the time of delivery. For example, if the user frequently posts "cafe" on social media, the service provider can prioritize providing information about nearby cafes. For example, if the user frequently posts "safety" on social media, the service provider can prioritize providing safety information. Furthermore, if the user frequently posts "events" on social media, the service provider can prioritize providing information about nearby events. This allows the service provider to provide more relevant information based on the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media data into a generating AI and have the generating AI perform the provision of relevant information.

[0084] The feedback unit can estimate the user's emotions and adjust the weighting of feedback based on the estimated emotions. For example, if the user is feeling anxious, the feedback unit may increase the weight of feedback regarding safety information. For example, if the user is excited, the feedback unit may increase the weight of feedback regarding entertainment facilities. Also, if the user is relaxed, the feedback unit may increase the weight of feedback regarding parks and cafes. In this way, the feedback unit can provide more appropriate information by adjusting the weighting of feedback according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0085] The feedback unit can select the optimal feedback method by referring to the user's past feedback history when providing feedback. For example, if the user has provided detailed feedback in the past, the feedback unit can prioritize collecting detailed feedback. For example, if the user has provided concise feedback in the past, the feedback unit can prioritize collecting concise feedback. The feedback unit can also analyze the user's past feedback history and suggest the optimal feedback method. This allows the feedback unit to provide a more appropriate feedback method based on the user's past feedback history. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's feedback history data into a generating AI and have the generating AI select the optimal feedback method.

[0086] The feedback unit can estimate the user's emotions and determine the priority of feedback based on the estimated emotions. For example, if the user is feeling anxious, the feedback unit will prioritize collecting feedback on safety information. For example, if the user is excited, the feedback unit can prioritize collecting feedback on entertainment facilities. Similarly, if the user is relaxed, the feedback unit can prioritize collecting feedback on parks and cafes. This allows the feedback unit to provide more appropriate information by prioritizing feedback according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0087] The feedback unit can prioritize collecting highly relevant feedback by considering the user's geographical location information when providing feedback. For example, the feedback unit can prioritize collecting feedback on properties close to the user's current location. For example, if the user is interested in a particular region, the feedback unit can prioritize collecting feedback on that region. Furthermore, if the user is on the move, the feedback unit can collect the most relevant feedback in real time based on their current location. This allows the feedback unit to collect more relevant feedback based on the user's geographical location information. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's geographical location data into a generating AI and have the generating AI collect highly relevant feedback.

[0088] The recommendation unit can estimate the user's emotions and adjust its recommendation method based on the estimated emotions. For example, if the user is relaxed, the recommendation unit can recommend properties or areas where the user can relax. If the user is excited, the recommendation unit can recommend properties or areas with plenty of entertainment facilities. If the user is feeling anxious, the recommendation unit can also recommend properties or areas with good public safety. In this way, the recommendation unit can provide more appropriate information by offering recommendation methods that correspond to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or not using AI. For example, the recommendation unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0089] The recommendation unit can select the optimal recommendation method by referring to the user's past search history when making recommendations. For example, the recommendation unit can analyze the characteristics of properties the user has searched for in the past and recommend similar properties. For example, the recommendation unit can recommend related properties and regions based on information about regions the user has searched for in the past. The recommendation unit can also analyze keywords the user has searched for in the past and recommend properties and regions related to them. In this way, the recommendation unit can provide a more appropriate recommendation method based on the user's past search history. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the user's search history data into a generating AI and have the generating AI select the optimal recommendation method.

[0090] The recommendation unit can estimate the user's emotions and determine recommendation priorities based on the estimated emotions. For example, if the user is feeling anxious, the recommendation unit will prioritize recommending properties or areas with good safety. If the user is excited, the recommendation unit can prioritize recommending properties or areas with abundant entertainment facilities. If the user is relaxed, the recommendation unit can also prioritize recommending properties or areas where users can relax. In this way, the recommendation unit can provide more appropriate information by offering recommendations with priorities that match the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input user facial expression data into a generating AI and have the AI ​​perform emotion estimation.

[0091] The recommendation unit can prioritize providing highly relevant recommendations by considering the user's geographical location information during the recommendation process. For example, the recommendation unit can prioritize recommending properties or areas close to the user's current location. If the user is interested in a particular area, the recommendation unit can prioritize recommending properties or areas related to that area. Furthermore, if the user is on the move, the recommendation unit can recommend the most suitable properties or areas in real time based on their current location. This allows the recommendation unit to provide more relevant recommendations based on the user's geographical location information. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the user's geographical location data into a generating AI and have the generating AI perform the task of providing highly relevant recommendations.

[0092] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0093] The data collection unit can analyze a user's past purchase history and collect relevant data. For example, the data collection unit can analyze the characteristics of properties the user has purchased in the past and prioritize collecting information on similar properties. For example, the data collection unit can collect relevant data based on information about the areas where the user has purchased in the past. The data collection unit can also analyze the price range of properties the user has purchased in the past and collect information related to that. In this way, the data collection unit can collect more relevant information based on the user's past purchase history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's purchase history data into a generating AI and have the generating AI select the optimal data collection method.

[0094] The feedback unit can estimate the user's emotions and adjust the content of the feedback based on the estimated emotions. For example, if the user is feeling anxious, the feedback unit can prioritize collecting feedback on safety information. For example, if the user is excited, the feedback unit can prioritize collecting feedback on entertainment facilities. Also, if the user is relaxed, the feedback unit can prioritize collecting feedback on parks and cafes. In this way, the feedback unit can provide more appropriate information by adjusting the content of the feedback according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0095] The recommendation unit can analyze a user's past travel history and recommend relevant properties and regions. For example, the recommendation unit can analyze the characteristics of regions the user has visited in the past and recommend similar regions. For example, the recommendation unit can recommend relevant properties based on information about accommodations the user has stayed at in the past. The recommendation unit can also analyze information about tourist destinations the user has visited in the past and recommend properties and regions related to them. In this way, the recommendation unit can provide a more appropriate recommendation method based on the user's past travel history. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the user's travel history data into a generating AI and have the generating AI select the optimal recommendation method.

[0096] The data collection unit can estimate the user's emotions and adjust the granularity of the data it collects based on the estimated emotions. For example, if the user is feeling anxious, the data collection unit can collect detailed safety information. For example, if the user is excited, the data collection unit can collect detailed information about entertainment facilities. Also, if the user is relaxed, the data collection unit can collect detailed information about parks and cafes. In this way, the data collection unit can provide more appropriate information by adjusting the granularity of the data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0097] The data collection unit can prioritize collecting highly relevant data while considering the user's health status. For example, if the user is health-conscious, the data collection unit can prioritize collecting information on nearby hospitals and fitness facilities. If the user has a specific health problem, the data collection unit can prioritize collecting information on facilities related to that problem. Furthermore, if the user wants to lead a healthy lifestyle, the data collection unit can prioritize collecting information on health-related events and activities. This allows the data collection unit to collect more relevant information based on the user's health status. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's health data into a generating AI and have the generating AI collect highly relevant data.

[0098] The data collection unit can estimate the user's emotions and adjust the update frequency of the collected data based on the estimated user emotions. For example, if the user is feeling anxious, the data collection unit can increase the update frequency of safety information. For example, if the user is excited, the data collection unit can increase the update frequency of information about entertainment facilities. Also, if the user is relaxed, the data collection unit can increase the update frequency of information about parks and cafes. In this way, the data collection unit can provide more appropriate information by adjusting the data update frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0099] The generation unit can adjust the content of the aggregated text based on the energy efficiency of the property during generation. For example, for properties with high energy efficiency, the generation unit can generate an aggregated text that includes detailed energy efficiency information. For properties with low energy efficiency, the generation unit can generate an aggregated text that includes areas for improvement. The generation unit can also adjust the granularity of the information according to the energy efficiency of the property when generating the aggregated text. This allows the generation unit to provide more appropriate information by generating an aggregated text with content that matches the energy efficiency of the property. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the energy efficiency data of the property into a generation AI and have the generation AI perform the adjustment of the content of the aggregated text.

[0100] The generation unit can estimate the user's emotions and adjust the format of the summary text based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate the summary text in a soft font and color scheme. If the user is in a hurry, the generation unit can generate the summary text in a concise and highly legible format. Furthermore, if the user is excited, the generation unit can generate the summary text in a visually stimulating format. This allows the generation unit to provide more appropriate information by generating the summary text in a format that matches the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI, or not. For example, the generation unit can input user facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0101] The service provider can select the optimal display method by referring to the user's past purchase history at the time of delivery. For example, the service provider can analyze the characteristics of properties that the user has previously preferred to purchase and prioritize the display of information on similar properties. For example, the service provider can display information on related properties based on information about the areas in which the user has previously purchased. The service provider can also analyze the price range of properties that the user has previously purchased and display information related to that range. In this way, the service provider can provide more relevant information based on the user's past purchase history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's purchase history data into a generating AI and have the generating AI select the optimal display method.

[0102] The information provider can estimate the user's emotions and adjust the order in which it provides information based on the estimated emotions. For example, if the user is feeling anxious, the information provider can provide safety information first. If the user is excited, the information provider can provide information about entertainment facilities first. If the user is relaxed, the information provider can provide information about parks and cafes first. In this way, the information provider can provide more appropriate information by providing information in an order that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the information provider can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0103] The following briefly describes the processing flow for example form 2.

[0104] Step 1: The data collection unit collects detailed data about the area surrounding the property. The data collection unit can collect detailed data such as traffic information, information on surrounding facilities, and safety information. For example, as traffic information, the data collection unit collects information on the operation status of public transportation around the property and traffic congestion. The data collection unit can also collect information on surrounding facilities such as restaurants, educational institutions, and hospitals around the property. Furthermore, the data collection unit can also collect information on safety, such as the crime rate around the property and the location of police stations. Step 2: The generation unit generates an aggregated text based on the data collected by the collection unit. The generation unit, for example, uses a generation AI to summarize the collected data and provide it as an easy-to-understand text. For example, the generation unit can summarize the transportation convenience around the property based on the collected traffic information. The generation unit can also summarize the living environment around the property based on the collected information on surrounding facilities. Furthermore, the generation unit can summarize the safety situation around the property based on the collected safety information. Step 3: The provider provides the aggregated text generated by the generator to the user. The provider, for example, displays the generated aggregated text on the user's device. The provider provides the generated aggregated text to the user, for example, through a web application or mobile application. The provider can also send the generated aggregated text to the user via email. Furthermore, the provider can print the generated aggregated text and provide it on paper.

[0105] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0106] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0107] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0108] Each of the multiple elements described above, including the collection unit, generation unit, provision unit, feedback unit, and recommendation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects detailed data about the area around the property using the camera 42 and communication I / F 44 of the smart device 14. The generation unit generates an aggregated statement based on the collected data using the specific processing unit 290 of the data processing unit 12. The provision unit provides the generated aggregated statement to the user through the display 40A and speaker 40B of the smart device 14. The feedback unit collects user evaluations and comments using the receiving device 38 of the smart device 14 and reflects them in the generation unit. The recommendation unit suggests similar towns and properties based on the user's desired conditions using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0110] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0111] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0112] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0113] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0114] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0115] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0116] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0117] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0118] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0119] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0120] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0121] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0122] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0123] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0124] Each of the multiple elements described above, including the collection unit, generation unit, provision unit, feedback unit, and recommendation unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects detailed data about the area around a property using the camera 42 and communication I / F 44 of the smart glasses 214. The generation unit generates an aggregated statement based on the data collected by the specific processing unit 290 of the data processing unit 12. The provision unit provides the generated aggregated statement to the user, for example, through the display and speaker 240 of the smart glasses 214. The feedback unit collects user evaluations and comments using the microphone 238 of the smart glasses 214 and reflects them in the generation unit. The recommendation unit suggests similar towns and properties based on the user's desired conditions using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0125] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0126] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0127] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0128] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0129] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0131] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0132] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0133] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0134] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0135] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0136] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0137] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0138] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0139] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0140] Each of the multiple elements described above, including the collection unit, generation unit, provision unit, feedback unit, and recommendation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects detailed data about the area around the property using the camera 42 and communication I / F 44 of the headset terminal 314. The generation unit generates an aggregated statement based on the collected data, for example, by the specific processing unit 290 of the data processing unit 12. The provision unit provides the generated aggregated statement to the user, for example, through the display 343 and speaker 240 of the headset terminal 314. The feedback unit collects user evaluations and comments using the microphone 238 of the headset terminal 314 and reflects them in the generation unit. The recommendation unit suggests similar towns and properties based on the user's desired conditions, for example, by the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0141] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0142] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0143] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0144] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0145] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0147] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0148] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0149] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0150] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0151] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0152] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0153] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0154] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0155] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0156] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0157] Each of the multiple elements described above, including the collection unit, generation unit, provision unit, feedback unit, and recommendation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects detailed data about the area around the property using the camera 42 and communication I / F 44 of the robot 414. The generation unit generates an aggregated statement based on the data collected by, for example, the specific processing unit 290 of the data processing unit 12. The provision unit provides the generated aggregated statement to the user through, for example, the speaker 240 and display device of the robot 414. The feedback unit collects user evaluations and comments using, for example, the microphone 238 of the robot 414 and reflects them in the generation unit. The recommendation unit suggests similar towns and properties based on the user's desired conditions using, for example, the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0158] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0159] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0160] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0161] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0162] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0163] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0164] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0165] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0166] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0167] 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.

[0168] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0169] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0170] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0171] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0172] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0173] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0174] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0175] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0176] (Note 1) A data collection unit that collects detailed data about the area surrounding the property, A generation unit generates an aggregated statement based on the data collected by the collection unit, A providing unit that provides the aggregated sentence generated by the generation unit to the user, Equipped with A system characterized by the following features. (Note 2) The system includes a feedback unit that receives feedback and reflects it in the generation unit. The system described in Appendix 1, characterized by the features described herein. (Note 3) When there are few properties that meet the user's desired conditions, the system includes a recommendation section that suggests similar neighborhoods or properties. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is It estimates the user's emotions and adjusts the types of data collected based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is During data collection, the system analyzes the user's past search history to select the most suitable data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is During data collection, real-time information on changes in the surrounding environment of the property is collected. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is During data collection, the system prioritizes collecting highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is During data collection, the system analyzes the user's social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 10) The generating unit is The system estimates the user's emotions and adjusts the way aggregate sentences are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The generating unit is During generation, adjust the level of detail in the aggregated sentences based on the importance of the property. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is During generation, different generation algorithms are applied depending on the property category. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is It estimates the user's sentiment and adjusts the length of the aggregated sentence based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is During generation, the priority of aggregate statements is determined based on when the property was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is During generation, the order of aggregate sentences is adjusted based on the relevance of the properties. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned supply unit is, It estimates the user's emotions and adjusts how information is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned supply unit is, When providing the service, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, When providing the service, the optimal display method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, It estimates the user's emotions and prioritizes the information provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing information, we prioritize providing highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing the service, we analyze the user's social media activity and provide relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned feedback unit is It estimates the user's emotions and adjusts the weighting of feedback based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned feedback unit is When providing feedback, the system selects the most suitable feedback method by referring to the user's past feedback history. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned feedback unit is It estimates the user's emotions and prioritizes feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned feedback unit is When providing feedback, the system prioritizes collecting highly relevant feedback by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 26) The recommendation unit is, It estimates the user's emotions and adjusts the recommendation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The recommendation unit is, When making recommendations, the system selects the most suitable recommendation method by referring to the user's past search history. The system described in Appendix 1, characterized by the features described herein. (Note 28) The recommendation unit is, It estimates the user's emotions and determines the priority of recommendations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The recommendation unit is, When providing recommendations, the system prioritizes highly relevant recommendations by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0177] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects detailed data about the area surrounding the property, A generation unit generates an aggregated statement based on the data collected by the collection unit, A providing unit that provides the aggregated sentence generated by the generation unit to the user, Equipped with A system characterized by the following features.

2. The system includes a feedback unit that receives feedback and reflects it in the generation unit. The system according to feature 1.

3. When there are few properties that meet the user's desired conditions, the system includes a recommendation section that suggests similar neighborhoods or properties. The system according to feature 1.

4. The aforementioned collection unit is It estimates the user's emotions and adjusts the types of data collected based on those estimated emotions. The system according to feature 1.

5. The aforementioned collection unit is During data collection, the system analyzes the user's past search history to select the most suitable data collection method. The system according to feature 1.

6. The aforementioned collection unit is During data collection, real-time information on changes in the surrounding environment of the property is collected. The system according to feature 1.

7. The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is During data collection, the system prioritizes collecting highly relevant data, taking into account the user's geographical location. The system according to feature 1.

9. The aforementioned collection unit is During data collection, the system analyzes the user's social media activity and collects relevant data. The system according to feature 1.

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