system

Abstract

The system according to the embodiment aims to efficiently perform real estate appraisals and obtain information on the surrounding environment. [Solution] A system according to an embodiment includes an input unit, an acquisition unit, an analysis unit, and a presentation unit. The input unit inputs property information. The acquisition unit automatically acquires surrounding environment information based on the property information input by the input unit. The analysis unit comprehensively analyzes the information acquired by the acquisition unit. The presentation unit presents the results of the analysis by the analysis unit.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, the appraisal of real estate and the acquisition of information on the surrounding environment were time-consuming and laborious, making it difficult to carry out the process efficiently.

[0005] The system according to the embodiment aims to efficiently perform real estate appraisals and obtain information on the surrounding environment. [Means for solving the problem]

[0006] The system according to the embodiment includes an input unit, an acquisition unit, an analysis unit, and a presentation unit. The input unit inputs property information. The acquisition unit automatically acquires surrounding environment information based on the property information input by the input unit. The analysis unit comprehensively analyzes the information acquired by the acquisition unit. The presentation unit presents the results of the analysis by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently perform real estate appraisals and obtain information on the surrounding environment. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A real estate appraisal system according to an embodiment of the present invention uses AI to comprehensively analyze property information and surrounding environment information and quickly present appraisal results. This real estate appraisal system allows users to input property information (e.g., address, age, floor plan, and area) and also provides a free-form input function for nearby recommended spots, lifestyles, and key features. It also automatically acquires surrounding environment information (e.g., roadside land value, land price, and successful transactions for similar properties) and comprehensively analyzes this information using AI to present appraisal results in seconds. Furthermore, it has a comprehensive search database not found on conventional real estate brokerage sites, allowing prospective buyers to input qualitative information such as "ideal lifestyle" and achieve optimal matching. For example, it enables complementary and essential searches and suggestions, such as finding an apartment without a parking lot but with a monthly parking lot within a few minutes' walk. For individual users, this service significantly reduces time and effort compared to conventional appraisal methods, potentially opening up properties that may not have been considered for purchase. It also allows users to compare multiple appraisal results without having to contact multiple real estate agents. For real estate agents, this system improves customer satisfaction by providing prompt appraisal responses, reduces operational costs by streamlining appraisal work, and allows them to develop marketing strategies by analyzing customer data. The revenue model is based on a monthly usage fee for individual users, and on appraisal report creation fees, API usage fees, and data analysis service fees for real estate agents. This allows the real estate appraisal system to perform an integrated process from entering property information to acquiring, analyzing, and presenting information about the surrounding environment.

[0029] A real estate appraisal system according to an embodiment includes an input unit, an acquisition unit, an analysis unit, and a presentation unit. The input unit inputs property information. The property information includes, for example, address, price, floor plan, and age of the property, but is not limited to these examples. The input unit allows the user to freely input, for example, recommended neighborhood spots, lifestyles, and key points of the property in addition to the property information. The acquisition unit automatically acquires surrounding environment information based on the property information input by the input unit. The surrounding environment information includes, for example, roadside land prices, land prices, and successful transactions of similar properties, but is not limited to these examples. The acquisition unit acquires roadside land prices from a database of the Ministry of Land, Infrastructure, Transport and Tourism. The acquisition unit can also acquire official land prices and land price survey prices. The acquisition unit can also acquire successful transactions from the past year or in the same area. The analysis unit comprehensively analyzes the information acquired by the acquisition unit. The analysis is performed, for example, using a generation AI. The generation AI comprehensively analyzes the property information and surrounding environment information based on a specific machine learning model and a dataset used. For example, the generation AI performs analysis taking into consideration the interrelationship between property information and surrounding environment information. The presentation unit presents the results of the analysis performed by the analysis unit. The presentation is performed in the form of, for example, a graph, a table, or text. The presentation unit can present the analysis results in a few seconds. For example, the presentation unit improves user convenience by quickly presenting the analysis results. As a result, the real estate appraisal system according to the embodiment can consistently perform processes from inputting property information to acquiring, analyzing, and presenting surrounding environment information.

[0030] The input unit allows the user to input recommended nearby spots, lifestyles, and points of interest using free words in addition to property information. The input method and range of free words include, but are not limited to, character limits and inputtable content, for example. The input unit provides, for example, a text box in which the user can freely enter information. The input unit can also automatically analyze the input content and complement it with related information. For example, the input unit suggests information about related spots and lifestyles based on the free words entered by the user. This allows the user to input their lifestyle and points of interest in addition to property information.

[0031] The acquisition unit can automatically acquire surrounding environment information such as roadside land prices, land prices, and examples of successful transactions for similar properties. Specific content and scope of the surrounding environment information include, but are not limited to, transportation access, schools, hospitals, and commercial facilities. The acquisition unit, for example, acquires roadside land prices from a database of the Ministry of Land, Infrastructure, Transport and Tourism. The acquisition unit can also acquire the official land price price and the land price survey price. Furthermore, the acquisition unit can also acquire successful transactions from the past year and examples of successful transactions in the same area. This automatically acquires surrounding environment information, improving the accuracy of property appraisals.

[0032] The analysis unit can use the generation AI to comprehensively analyze the property information and surrounding environment information. Specific types and implementation methods of the generation AI include, but are not limited to, specific machine learning models and datasets used. For example, the analysis unit uses the generation AI to conduct analysis taking into account the interrelationship between the property information and surrounding environment information. The analysis unit can also use the generation AI to perform correlation analysis and regression analysis of the property information and surrounding environment information. This allows the generation AI to comprehensively analyze the property information and surrounding environment information.

[0033] The presentation unit can quickly present the analysis results. Specific time ranges for the quick presentation include, but are not limited to, within a few seconds or within one minute. For example, the presentation unit presents the analysis results within a few seconds. The presentation unit can also present the analysis results in the form of a graph, table, text, or the like. This allows the analysis results to be presented quickly, thereby improving user convenience.

[0034] The presentation unit can complete a database for highly accurate searches. Specific criteria and methods for highly accurate searches include, but are not limited to, search algorithms and evaluation criteria, for example. The presentation unit completes a database for searches at a level not available on conventional real estate agents, for example. The presentation unit can also use a search algorithm to provide optimal property information based on the user's search criteria. By completing a database for highly accurate searches, it becomes possible to perform searches at a level not available on conventional real estate agents.

[0035] The presentation unit allows a user who wants to buy to input qualitative information about "what kind of lifestyle would be ideal" and can perform optimal matching. Specific content and input methods of the qualitative information include, but are not limited to, free description and multiple choice formats. The presentation unit, for example, provides a text box in which the user can enter information freely. The presentation unit can also input qualitative information in a format that allows the user to select from multiple choices. This makes it possible to match the optimal property based on the user's lifestyle.

[0036] The presentation unit can perform complementary and essential searches and suggestions, such as, for example, that the apartment building does not have a parking lot, but that there is a monthly parking lot within a few minutes' walk. Specific methods and criteria for complementary and essential searches and suggestions include, but are not limited to, methods for extracting related information and criteria for suggestions. For example, the presentation unit provides information that the apartment building does not have a parking lot, but that there is a monthly parking lot within a few minutes' walk. The presentation unit can also suggest complementary and essential information based on the user's search conditions. This enables complementary and essential searches and suggestions.

[0037] The input unit can analyze the user's past input history and suggest the optimal input method. Specific criteria and suggestion methods for the optimal input method include, but are not limited to, past input patterns and user preferences, for example. The input unit can automatically display property information that the user has frequently input in the past as candidates, for example. The input unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the input unit can predict and suggest property information that will be used in a specific time period based on the user's past input history. This makes it possible to suggest the optimal input method based on the user's past input history.

[0038] The input unit can simplify the input process by automatically acquiring the user's current location information when entering property information. Specific methods for acquiring and using the current location information include, but are not limited to, GPS data and Wi-Fi location information. For example, when the user opens the app, the input unit automatically acquires the user's current location and sets it as the property address. Furthermore, when the user enters property information, the input unit can also suggest the most suitable candidate location by taking into account the distance from the current location. Furthermore, when the user uses the app while on the move, the input unit can update the user's current location in real time and reflect it in the property information. This simplifies the input process by automatically acquiring the user's current location information.

[0039] When inputting property information, the input unit can automatically suggest candidate locations by referring to the user's past travel history. Specific methods of acquiring and using the past travel history include, but are not limited to, GPS data and transportation card history. The input unit can automatically display, for example, places that the user has frequently visited in the past as candidate locations. The input unit can also predict places that the user will visit on specific days of the week or during specific time periods and suggest them as candidate locations. Furthermore, the input unit can analyze the user's past travel patterns and suggest optimal candidate locations. This makes it possible to automatically suggest candidate locations based on the user's past travel history.

[0040] When inputting property information, the input unit can make suggestions based on the user's schedule by referring to the user's calendar information. Specific methods for acquiring and using the calendar information include, but are not limited to, Google (registered trademark) Calendar and Outlook (registered trademark) Calendar, for example. The input unit can automatically set property information by referring to the schedule registered in the user's calendar, for example. The input unit can also suggest locations related to specific events as candidate locations based on the user's calendar information. Furthermore, the input unit can also suggest optimal property information that matches the schedule based on the user's calendar information. This makes it possible to suggest property information based on the user's calendar information.

[0041] The acquisition unit can analyze previously acquired data and select the optimal acquisition method. Specific criteria and selection methods for the optimal acquisition method include, but are not limited to, data reliability and acquisition speed, for example. The acquisition unit selects the most efficient information acquisition method from previously acquired data, for example. The acquisition unit can also analyze previously acquired data and optimize the acquisition frequency. Furthermore, the acquisition unit can determine the priority of information to be acquired based on previously acquired data. This makes it possible to select the optimal acquisition method based on previously acquired data.

[0042] The acquisition unit can filter the acquired data based on the user's current living situation and areas of interest at the time of acquisition. Specific criteria and methods for filtering include, but are not limited to, the user's areas of interest and living situation, for example. For example, if the user is raising a child, the acquisition unit can prioritize acquiring information related to raising a child. Furthermore, if the user has a pet, the acquisition unit can also prioritize acquiring information related to the pet. Furthermore, if the user is elderly, the acquisition unit can prioritize acquiring information for elderly people. This makes it possible to filter data based on the user's living situation and areas of interest.

[0043] The acquisition unit can prioritize acquiring highly relevant information in consideration of the user's geographical location information at the time of acquisition. Specific acquisition methods and use ranges of geographical location information include, but are not limited to, GPS data and address information, for example. For example, if the user is in a specific area, the acquisition unit can prioritize acquiring information related to that area. Furthermore, if the user is traveling, the acquisition unit can also prioritize acquiring information related to the travel destination. Furthermore, if the user is considering moving, the acquisition unit can also prioritize acquiring information related to the moving destination. This makes it possible to acquire highly relevant information based on the user's geographical location information.

[0044] At the time of acquisition, the acquisition unit can analyze the user's social media activity and acquire related information. Specific acquisition methods and scopes of use of social media activity include, but are not limited to, posts on X (formerly Twitter (registered trademark)) and Facebook (registered trademark). The acquisition unit acquires related information based on, for example, information shared by the user on social media. The acquisition unit can also acquire related information based on information about accounts the user follows on social media. Furthermore, the acquisition unit can acquire related information based on information about posts the user "likes" on social media. This makes it possible to acquire related information based on the user's social media activity.

[0045] During analysis, the analysis unit can optimize the analysis algorithm by referring to past analysis data. Specific methods for acquiring and scope of use of past analysis data include, but are not limited to, past analysis results and data sets. The analysis unit, for example, selects the most efficient algorithm based on the past analysis data. The analysis unit can also use an algorithm that improves analysis accuracy by referring to the past analysis data. Furthermore, the analysis unit can also use an algorithm that optimizes analysis speed based on the past analysis data. This makes it possible to select the optimal analysis algorithm based on the past analysis data.

[0046] During analysis, the analysis unit can improve the accuracy of the analysis by taking into account the interrelationship between property information and surrounding environment information. Specific methods and criteria for considering the interrelationship include, but are not limited to, correlation analysis and regression analysis. The analysis unit can improve the accuracy of the analysis by taking into account the interrelationship between property information and surrounding environment information, for example. The analysis unit can also select the optimal analysis method based on the interrelationship between property information and surrounding environment information. Furthermore, the analysis unit can analyze the interrelationship between property information and surrounding environment information to improve the reliability of the results. This makes it possible to perform analysis that takes into account the interrelationship between property information and surrounding environment information.

[0047] The analysis unit can perform the analysis taking into account the geographical distribution of the property information. Specific methods and standards for considering the geographical distribution include, but are not limited to, displaying the property information on a map or aggregating the data by region. For example, the analysis unit performs an analysis taking into account the characteristics of each region based on the geographical distribution of the property information. The analysis unit can also analyze price trends by region by referring to the geographical distribution of the property information. Furthermore, the analysis unit can analyze supply and demand by region based on the geographical distribution of the property information. This enables an analysis that takes into account the geographical distribution of the property information.

[0048] During analysis, the analysis unit can improve the accuracy of the analysis by referring to literature related to the property information. Specific methods of acquiring and the scope of use of related literature include, but are not limited to, academic papers and industry reports. The analysis unit can improve the accuracy of the analysis, for example, based on literature related to the property information. The analysis unit can also refer to literature related to the property information and select the optimal analysis method. Furthermore, the analysis unit can improve the reliability of the analysis results based on literature related to the property information. This makes it possible to improve the accuracy of the analysis based on literature related to the property information.

[0049] When presenting information, the presentation unit can select the optimal presentation method by referring to the user's past browsing history. Specific methods for acquiring and using the past browsing history include, but are not limited to, browser history and app usage history. For example, the presentation unit preferentially presents related information based on property information previously viewed by the user. The presentation unit can also preferentially present property information of interest to the user based on the user's past browsing history. Furthermore, the presentation unit can analyze the user's past browsing history and select the optimal presentation method. This makes it possible to select the optimal presentation method based on the user's past browsing history.

[0050] The presentation unit can customize the presentation content based on the user's current living situation and areas of interest when presenting the content. Specific methods for acquiring and scope of use of the living situation include, for example, family structure and income status, but are not limited to these examples. Specific methods for acquiring and scope of use of the areas of interest include, for example, hobbies and topics of interest, but are not limited to these examples. For example, if the user is raising children, the presentation unit can prioritize presenting property information related to raising children. Furthermore, if the user has pets, the presentation unit can also prioritize presenting property information related to pets. Furthermore, if the user is elderly, the presentation unit can prioritize presenting property information for elderly people. This makes it possible to customize the presentation content based on the user's living situation and areas of interest.

[0051] When presenting information, the presentation unit can prioritize presenting highly relevant information taking into account the user's geographical location information. Specific methods and criteria for acquiring highly relevant information include, but are not limited to, the user's geographical location information and areas of interest. For example, if the user is in a specific area, the presentation unit can prioritize presenting property information related to that area. Furthermore, if the user is traveling, the presentation unit can also prioritize presenting property information related to the user's travel destination. Furthermore, if the user is considering moving, the presentation unit can also prioritize presenting property information related to the user's moving destination. This makes it possible to present highly relevant information based on the user's geographical location information.

[0052] The presentation unit can analyze the user's social media activity and present related information at the time of presentation. Specific methods of acquiring and scope of use of social media activity include, but are not limited to, posts on X (formerly Twitter) and Facebook. The presentation unit can present related property information based on, for example, information shared by the user on social media. The presentation unit can also present related property information based on information about accounts the user follows on social media. Furthermore, the presentation unit can present related property information based on information the user has "liked" on social media. This makes it possible to present related information based on the user's social media activity.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The acquisition unit can analyze the user's past search history and prioritize acquisition of related surrounding environment information. For example, based on the surrounding environment information of properties previously searched for by the user, information on similar areas can be prioritized. In addition, if the user frequently searches for specific conditions (e.g., near a school, near a commercial facility, etc.), the acquisition unit can also prioritize acquisition of surrounding environment information that matches those conditions. Furthermore, the acquisition unit can optimize the information acquired during a specific time period based on the user's past search history. This makes it possible to acquire optimal surrounding environment information based on the user's past search history.

[0055] The presentation unit can adjust the priority of property information based on the user's current living situation. For example, if the user is raising children, property information suitable for raising children can be presented preferentially. Also, if the user has a pet, property information suitable for pets can be presented preferentially. Furthermore, if the user is elderly, property information for elderly people can be presented preferentially. This makes it possible to adjust the priority of property information based on the user's current living situation.

[0056] The acquisition unit can analyze the user's social media activities and acquire related surrounding environment information. For example, the acquisition unit can acquire related surrounding environment information based on information shared by the user on social media. The acquisition unit can also acquire related surrounding environment information based on information about accounts the user follows on social media. Furthermore, the acquisition unit can also acquire related surrounding environment information based on information the user has "liked" on social media. This makes it possible to acquire related information based on the user's social media activities.

[0057] The presentation unit can analyze the user's past browsing history and prioritize the presentation of related property information. For example, similar property information can be prioritized based on property information the user has previously viewed. Also, if the user frequently views specific conditions (e.g., price range, area, etc.), property information that matches those conditions can be prioritized. Furthermore, the user's past browsing history can be used to optimize property information viewed during specific time periods. This makes it possible to present optimal property information based on the user's past browsing history.

[0058] The acquisition unit can prioritize acquisition of related surrounding environment information based on the user's current location information. For example, if the user is in a specific area, the acquisition unit prioritizes acquisition of surrounding environment information related to that area. Also, if the user is traveling, the acquisition unit can prioritize acquisition of surrounding environment information related to the travel destination. Furthermore, if the user is considering moving, the acquisition unit can prioritize acquisition of surrounding environment information related to the moving destination. This makes it possible to acquire highly relevant surrounding environment information based on the user's current location information.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The input unit inputs property information. Property information includes, but is not limited to, the address, price, layout, and age of the property. In addition to the property information, the input unit can also input, for example, recommended nearby spots, lifestyles, and points to promote using free words. Step 2: The acquisition unit automatically acquires surrounding environment information based on the property information input by the input unit. The surrounding environment information includes, but is not limited to, roadside land prices, land prices, and examples of successful transactions for similar properties. The acquisition unit acquires roadside land prices from a database of the Ministry of Land, Infrastructure, Transport and Tourism, for example. The acquisition unit can also acquire official land prices and surveyed land prices. Furthermore, the acquisition unit can also acquire successful transactions from the past year and in the same area. Step 3: The analysis unit comprehensively analyzes the information acquired by the acquisition unit. The analysis is performed, for example, using a generation AI. The generation AI comprehensively analyzes the property information and surrounding environment information based on a specific machine learning model and the dataset used. For example, the generation AI performs its analysis taking into account the interrelationships between the property information and surrounding environment information. Step 4: The presentation unit presents the results of the analysis performed by the analysis unit. The presentation may be in the form of a graph, a table, text, or the like. The presentation unit can present the analysis results within a few seconds. For example, the presentation unit can improve user convenience by quickly presenting the analysis results.

[0061] (Example 2) A real estate appraisal system according to an embodiment of the present invention uses AI to comprehensively analyze property information and surrounding environment information and quickly present appraisal results. This real estate appraisal system allows users to input property information (e.g., address, age, floor plan, and area) and also provides a free-form input function for nearby recommended spots, lifestyles, and key features. It also automatically acquires surrounding environment information (e.g., roadside land value, land price, and successful transactions for similar properties) and comprehensively analyzes this information using AI to present appraisal results in seconds. Furthermore, it has a comprehensive search database not found on conventional real estate brokerage sites, allowing prospective buyers to input qualitative information such as "ideal lifestyle" and achieve optimal matching. For example, it enables complementary and essential searches and suggestions, such as finding an apartment without a parking lot but with a monthly parking lot within a few minutes' walk. For individual users, this service significantly reduces time and effort compared to conventional appraisal methods, potentially opening up properties that may not have been considered for purchase. It also allows users to compare multiple appraisal results without having to contact multiple real estate agents. For real estate agents, this system improves customer satisfaction by providing prompt appraisal responses, reduces operational costs by streamlining appraisal work, and allows them to develop marketing strategies by analyzing customer data. The revenue model is based on a monthly usage fee for individual users, and on appraisal report creation fees, API usage fees, and data analysis service fees for real estate agents. This allows the real estate appraisal system to perform an integrated process from entering property information to acquiring, analyzing, and presenting information about the surrounding environment.

[0062] A real estate appraisal system according to an embodiment includes an input unit, an acquisition unit, an analysis unit, and a presentation unit. The input unit inputs property information. The property information includes, for example, address, price, floor plan, and age of the property, but is not limited to these examples. The input unit allows the user to freely input, for example, recommended neighborhood spots, lifestyles, and key points of the property in addition to the property information. The acquisition unit automatically acquires surrounding environment information based on the property information input by the input unit. The surrounding environment information includes, for example, roadside land prices, land prices, and successful transactions of similar properties, but is not limited to these examples. The acquisition unit acquires roadside land prices from a database of the Ministry of Land, Infrastructure, Transport and Tourism. The acquisition unit can also acquire official land prices and land price survey prices. The acquisition unit can also acquire successful transactions from the past year or in the same area. The analysis unit comprehensively analyzes the information acquired by the acquisition unit. The analysis is performed, for example, using a generation AI. The generation AI comprehensively analyzes the property information and surrounding environment information based on a specific machine learning model and a dataset used. For example, the generation AI performs analysis taking into consideration the interrelationship between property information and surrounding environment information. The presentation unit presents the results of the analysis performed by the analysis unit. The presentation is performed in the form of, for example, a graph, a table, or text. The presentation unit can present the analysis results in a few seconds. For example, the presentation unit improves user convenience by quickly presenting the analysis results. As a result, the real estate appraisal system according to the embodiment can consistently perform processes from inputting property information to acquiring, analyzing, and presenting surrounding environment information.

[0063] The input unit allows the user to input recommended nearby spots, lifestyles, and points of interest using free words in addition to property information. The input method and range of free words include, but are not limited to, character limits and inputtable content, for example. The input unit provides, for example, a text box in which the user can freely enter information. The input unit can also automatically analyze the input content and complement it with related information. For example, the input unit suggests information about related spots and lifestyles based on the free words entered by the user. This allows the user to input their lifestyle and points of interest in addition to property information.

[0064] The acquisition unit can automatically acquire surrounding environment information such as roadside land prices, land prices, and examples of successful transactions for similar properties. Specific content and scope of the surrounding environment information include, but are not limited to, transportation access, schools, hospitals, and commercial facilities. The acquisition unit, for example, acquires roadside land prices from a database of the Ministry of Land, Infrastructure, Transport and Tourism. The acquisition unit can also acquire the official land price price and the land price survey price. Furthermore, the acquisition unit can also acquire successful transactions from the past year and examples of successful transactions in the same area. This automatically acquires surrounding environment information, improving the accuracy of property appraisals.

[0065] The analysis unit can use the generation AI to comprehensively analyze the property information and surrounding environment information. Specific types and implementation methods of the generation AI include, but are not limited to, specific machine learning models and datasets used. For example, the analysis unit uses the generation AI to conduct analysis taking into account the interrelationship between the property information and surrounding environment information. The analysis unit can also use the generation AI to perform correlation analysis and regression analysis of the property information and surrounding environment information. This allows the generation AI to comprehensively analyze the property information and surrounding environment information.

[0066] The presentation unit can quickly present the analysis results. Specific time ranges for the quick presentation include, but are not limited to, within a few seconds or within one minute. For example, the presentation unit presents the analysis results within a few seconds. The presentation unit can also present the analysis results in the form of a graph, table, text, or the like. This allows the analysis results to be presented quickly, thereby improving user convenience.

[0067] The presentation unit can complete a database for highly accurate searches. Specific criteria and methods for highly accurate searches include, but are not limited to, search algorithms and evaluation criteria, for example. The presentation unit completes a database for searches at a level not available on conventional real estate agents, for example. The presentation unit can also use a search algorithm to provide optimal property information based on the user's search criteria. By completing a database for highly accurate searches, it becomes possible to perform searches at a level not available on conventional real estate agents.

[0068] The presentation unit allows a user who wants to buy to input qualitative information about "what kind of lifestyle would be ideal" and can perform optimal matching. Specific content and input methods of the qualitative information include, but are not limited to, free description and multiple choice formats. The presentation unit, for example, provides a text box in which the user can enter information freely. The presentation unit can also input qualitative information in a format that allows the user to select from multiple choices. This makes it possible to match the optimal property based on the user's lifestyle.

[0069] The presentation unit can perform complementary and essential searches and suggestions, such as, for example, that the apartment building does not have a parking lot, but that there is a monthly parking lot within a few minutes' walk. Specific methods and criteria for complementary and essential searches and suggestions include, but are not limited to, methods for extracting related information and criteria for suggestions. For example, the presentation unit provides information that the apartment building does not have a parking lot, but that there is a monthly parking lot within a few minutes' walk. The presentation unit can also suggest complementary and essential information based on the user's search conditions. This enables complementary and essential searches and suggestions.

[0070] The input unit can estimate the user's emotions and adjust the input interface for property information based on the estimated user emotions. Specific methods and criteria for estimating the user's emotions include, but are not limited to, facial expression recognition and voice analysis. For example, if the user is feeling stressed, the input unit can provide a simple interface and minimize input steps. Furthermore, if the user is relaxed, the input unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the input unit can prioritize voice input to enable quick input of property information. This makes it possible to adjust the input interface according to the user's emotions.

[0071] The input unit can analyze the user's past input history and suggest the optimal input method. Specific criteria and suggestion methods for the optimal input method include, but are not limited to, past input patterns and user preferences, for example. The input unit can automatically display property information that the user has frequently input in the past as candidates, for example. The input unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the input unit can predict and suggest property information that will be used in a specific time period based on the user's past input history. This makes it possible to suggest the optimal input method based on the user's past input history.

[0072] The input unit can simplify the input process by automatically acquiring the user's current location information when entering property information. Specific methods for acquiring and using the current location information include, but are not limited to, GPS data and Wi-Fi location information. For example, when the user opens the app, the input unit automatically acquires the user's current location and sets it as the property address. Furthermore, when the user enters property information, the input unit can also suggest the most suitable candidate location by taking into account the distance from the current location. Furthermore, when the user uses the app while on the move, the input unit can update the user's current location in real time and reflect it in the property information. This simplifies the input process by automatically acquiring the user's current location information.

[0073] The input unit can estimate the user's emotions and adjust the design of the input interface based on the estimated user's emotions. Specific methods and criteria for adjusting the design of the input interface include, but are not limited to, changing the color and layout. For example, if the user is nervous, the input unit can provide an interface with calm colors to reduce visual stress. Furthermore, if the user is having fun, the input unit can provide an interface with bright colors to make input work more enjoyable. Furthermore, if the user is tired, the input unit can provide a simple, highly visible interface to make input work easier. This makes it possible to adjust the design of the input interface according to the user's emotions.

[0074] When inputting property information, the input unit can automatically suggest candidate locations by referring to the user's past travel history. Specific methods of acquiring and using the past travel history include, but are not limited to, GPS data and transportation card history. The input unit can automatically display, for example, places that the user has frequently visited in the past as candidate locations. The input unit can also predict places that the user will visit on specific days of the week or during specific time periods and suggest them as candidate locations. Furthermore, the input unit can analyze the user's past travel patterns and suggest optimal candidate locations. This makes it possible to automatically suggest candidate locations based on the user's past travel history.

[0075] When inputting property information, the input unit can refer to the user's calendar information and make suggestions based on the schedule. Specific methods of acquiring and using the calendar information include, but are not limited to, Google Calendar and Outlook Calendar, for example. The input unit, for example, refers to the schedule registered in the user's calendar and automatically sets the property information. The input unit can also suggest locations related to specific events as candidate locations from the user's calendar information. Furthermore, the input unit can also suggest optimal property information that matches the schedule based on the user's calendar information. This makes it possible to suggest property information based on the user's calendar information.

[0076] The acquisition unit can estimate the user's emotions and adjust the timing of acquiring the surrounding environment information based on the estimated user emotions. Specific criteria and adjustment methods for the timing of acquiring the surrounding environment information include, but are not limited to, the user's behavioral patterns and time periods, for example. For example, if the user is relaxed, the acquisition unit can acquire information at a leisurely pace. Furthermore, if the user is in a hurry, the acquisition unit can also quickly acquire necessary information. Furthermore, if the user is excited, the acquisition unit can prioritize acquiring visually stimulating information. This makes it possible to adjust the timing of acquiring the surrounding environment information according to the user's emotions.

[0077] The acquisition unit can analyze previously acquired data and select the optimal acquisition method. Specific criteria and selection methods for the optimal acquisition method include, but are not limited to, data reliability and acquisition speed, for example. The acquisition unit selects the most efficient information acquisition method from previously acquired data, for example. The acquisition unit can also analyze previously acquired data and optimize the acquisition frequency. Furthermore, the acquisition unit can determine the priority of information to be acquired based on previously acquired data. This makes it possible to select the optimal acquisition method based on previously acquired data.

[0078] The acquisition unit can filter the acquired data based on the user's current living situation and areas of interest at the time of acquisition. Specific criteria and methods for filtering include, but are not limited to, the user's areas of interest and living situation, for example. For example, if the user is raising a child, the acquisition unit can prioritize acquiring information related to raising a child. Furthermore, if the user has a pet, the acquisition unit can also prioritize acquiring information related to the pet. Furthermore, if the user is elderly, the acquisition unit can prioritize acquiring information for elderly people. This makes it possible to filter data based on the user's living situation and areas of interest.

[0079] The acquisition unit can estimate the user's emotions and determine the priority of information to be acquired based on the estimated user's emotions. Specific methods and criteria for determining the priority of information include, but are not limited to, the user's emotions and the importance of information. For example, when the user is relaxed, the acquisition unit can prioritize acquiring detailed information. Furthermore, when the user is in a hurry, the acquisition unit can prioritize acquiring the minimum amount of information necessary. Furthermore, when the user is excited, the acquisition unit can prioritize acquiring visually stimulating information. This makes it possible to determine the priority of information according to the user's emotions.

[0080] The acquisition unit can prioritize acquiring highly relevant information in consideration of the user's geographical location information at the time of acquisition. Specific acquisition methods and use ranges of geographical location information include, but are not limited to, GPS data and address information, for example. For example, if the user is in a specific area, the acquisition unit can prioritize acquiring information related to that area. Furthermore, if the user is traveling, the acquisition unit can also prioritize acquiring information related to the travel destination. Furthermore, if the user is considering moving, the acquisition unit can also prioritize acquiring information related to the moving destination. This makes it possible to acquire highly relevant information based on the user's geographical location information.

[0081] At the time of acquisition, the acquisition unit can analyze the user's social media activity and acquire related information. Specific acquisition methods and scope of use of social media activity include, but are not limited to, posts on X (formerly Twitter) and Facebook. The acquisition unit can acquire related information based on, for example, information shared by the user on social media. The acquisition unit can also acquire related information based on information about accounts the user follows on social media. Furthermore, the acquisition unit can acquire related information based on information about posts the user has "liked" on social media. This makes it possible to acquire related information based on the user's social media activity.

[0082] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user's emotions. Specific types and adjustment methods of the analysis algorithm include, but are not limited to, machine learning models and parameter adjustments. For example, if the user is relaxed, the analysis unit uses an algorithm that performs a detailed analysis. Furthermore, if the user is in a hurry, the analysis unit can use an algorithm that produces quick results. Furthermore, if the user is excited, the analysis unit can use an algorithm that produces visually stimulating results. This makes it possible to adjust the analysis algorithm according to the user's emotions.

[0083] During analysis, the analysis unit can optimize the analysis algorithm by referring to past analysis data. Specific methods for acquiring and scope of use of past analysis data include, but are not limited to, past analysis results and data sets. The analysis unit, for example, selects the most efficient algorithm based on the past analysis data. The analysis unit can also use an algorithm that improves analysis accuracy by referring to the past analysis data. Furthermore, the analysis unit can also use an algorithm that optimizes analysis speed based on the past analysis data. This makes it possible to select the optimal analysis algorithm based on the past analysis data.

[0084] During analysis, the analysis unit can improve the accuracy of the analysis by taking into account the interrelationship between property information and surrounding environment information. Specific methods and criteria for considering the interrelationship include, but are not limited to, correlation analysis and regression analysis. The analysis unit can improve the accuracy of the analysis by taking into account the interrelationship between property information and surrounding environment information, for example. The analysis unit can also select the optimal analysis method based on the interrelationship between property information and surrounding environment information. Furthermore, the analysis unit can analyze the interrelationship between property information and surrounding environment information to improve the reliability of the results. This makes it possible to perform analysis that takes into account the interrelationship between property information and surrounding environment information.

[0085] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. Specific adjustment methods and standards for the display method of the analysis results include, but are not limited to, graphs, tables, text, etc. For example, if the user is nervous, the analysis unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. This makes it possible to adjust the display method of the analysis results according to the user's emotions.

[0086] The analysis unit can perform the analysis taking into account the geographical distribution of the property information. Specific methods and standards for considering the geographical distribution include, but are not limited to, displaying the property information on a map or aggregating the data by region. For example, the analysis unit performs an analysis taking into account the characteristics of each region based on the geographical distribution of the property information. The analysis unit can also analyze price trends by region by referring to the geographical distribution of the property information. Furthermore, the analysis unit can analyze supply and demand by region based on the geographical distribution of the property information. This enables an analysis that takes into account the geographical distribution of the property information.

[0087] During analysis, the analysis unit can improve the accuracy of the analysis by referring to literature related to the property information. Specific methods of acquiring and the scope of use of related literature include, but are not limited to, academic papers and industry reports. The analysis unit can improve the accuracy of the analysis, for example, based on literature related to the property information. The analysis unit can also refer to literature related to the property information and select the optimal analysis method. Furthermore, the analysis unit can improve the reliability of the analysis results based on literature related to the property information. This makes it possible to improve the accuracy of the analysis based on literature related to the property information.

[0088] The presentation unit can estimate the user's emotions and adjust the presentation method based on the estimated user's emotions. Specific methods and criteria for adjusting the presentation method include, but are not limited to, display format and interface design. For example, when the user is nervous, the presentation unit can provide a simple, highly visible display method. When the user is relaxed, the presentation unit can also provide a display method including detailed information. Furthermore, when the user is in a hurry, the presentation unit can also provide a display method that focuses on the main points. This makes it possible to adjust the presentation method according to the user's emotions.

[0089] When presenting information, the presentation unit can select the optimal presentation method by referring to the user's past browsing history. Specific methods for acquiring and using the past browsing history include, but are not limited to, browser history and app usage history. For example, the presentation unit preferentially presents related information based on property information previously viewed by the user. The presentation unit can also preferentially present property information of interest to the user based on the user's past browsing history. Furthermore, the presentation unit can analyze the user's past browsing history and select the optimal presentation method. This makes it possible to select the optimal presentation method based on the user's past browsing history.

[0090] The presentation unit can customize the presentation content based on the user's current living situation and areas of interest when presenting the content. Specific methods for acquiring and scope of use of the living situation include, for example, family structure and income status, but are not limited to these examples. Specific methods for acquiring and scope of use of the areas of interest include, for example, hobbies and topics of interest, but are not limited to these examples. For example, if the user is raising children, the presentation unit can prioritize presenting property information related to raising children. Furthermore, if the user has pets, the presentation unit can also prioritize presenting property information related to pets. Furthermore, if the user is elderly, the presentation unit can prioritize presenting property information for elderly people. This makes it possible to customize the presentation content based on the user's living situation and areas of interest.

[0091] The presentation unit can estimate the user's emotions and determine the priority of the content to be presented based on the estimated user's emotions. Specific methods and criteria for determining the priority of the content to be presented include, but are not limited to, the user's emotions and the importance of information, for example. For example, when the user is relaxed, the presentation unit can prioritize presenting detailed information. Furthermore, when the user is in a hurry, the presentation unit can prioritize presenting the minimum necessary information. Furthermore, when the user is excited, the presentation unit can prioritize presenting visually stimulating information. This makes it possible to determine the priority of the content to be presented according to the user's emotions.

[0092] When presenting information, the presentation unit can prioritize presenting highly relevant information taking into account the user's geographical location information. Specific methods and criteria for acquiring highly relevant information include, but are not limited to, the user's geographical location information and areas of interest. For example, if the user is in a specific area, the presentation unit can prioritize presenting property information related to that area. Furthermore, if the user is traveling, the presentation unit can also prioritize presenting property information related to the user's travel destination. Furthermore, if the user is considering moving, the presentation unit can also prioritize presenting property information related to the user's moving destination. This makes it possible to present highly relevant information based on the user's geographical location information.

[0093] The presentation unit can analyze the user's social media activity and present related information at the time of presentation. Specific methods of acquiring and scope of use of social media activity include, but are not limited to, posts on X (formerly Twitter) and Facebook. The presentation unit can present related property information based on, for example, information shared by the user on social media. The presentation unit can also present related property information based on information about accounts the user follows on social media. Furthermore, the presentation unit can present related property information based on information the user has "liked" on social media. This makes it possible to present related information based on the user's social media activity. === Hard Collateral 1-1 === Each of the multiple elements including the input unit, acquisition unit, analysis unit, and presentation unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the input unit is realized by the reception device 38 of the smart device 14, allowing the user to input property information and free words. The acquisition unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and automatically acquires surrounding environment information. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and comprehensively analyzes the property information and surrounding environment information using a generation AI. The presentation unit is realized, for example, by the output device 40 of the smart device 14, and presents the analysis results to the user in graph or text format. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned input unit, acquisition unit, analysis unit, and presentation unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the input unit is realized by the microphone 238 of the smart glasses 214, allowing the user to input property information and free words by voice. The acquisition unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and automatically acquires surrounding environment information. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and comprehensively analyzes the property information and surrounding environment information using a generation AI. The presentation unit is realized, for example, by the speaker 240 of the smart glasses 214, and presents the analysis results to the user by voice. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned input unit, acquisition unit, analysis unit, and presentation unit is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the input unit is realized by the microphone 238 of the headset terminal 314, allowing the user to input property information and free words by voice. The acquisition unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and automatically acquires surrounding environment information. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and comprehensively analyzes the property information and surrounding environment information using a generation AI. The presentation unit is realized, for example, by the display 343 of the headset terminal 314, and presents the analysis results to the user in graph or text format. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned input unit, acquisition unit, analysis unit, and presentation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the input unit is realized by the microphone 238 of the robot 414, allowing the user to input property information and free words by voice. The acquisition unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and automatically acquires surrounding environment information. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and comprehensively analyzes the property information and surrounding environment information using a generation AI. The presentation unit is realized, for example, by the speaker 240 of the robot 414, and presents the analysis results to the user by voice.

[0094] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0095] The acquisition unit can analyze the user's past search history and prioritize acquisition of related surrounding environment information. For example, based on the surrounding environment information of properties previously searched for by the user, information on similar areas can be prioritized. In addition, if the user frequently searches for specific conditions (e.g., near a school, near a commercial facility, etc.), the acquisition unit can also prioritize acquisition of surrounding environment information that matches those conditions. Furthermore, the acquisition unit can optimize the information acquired during a specific time period based on the user's past search history. This makes it possible to acquire optimal surrounding environment information based on the user's past search history.

[0096] The analysis unit can estimate the user's emotions and adjust the level of detail in the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can provide concise and to-the-point analysis results. If the user is relaxed, the analysis unit can provide detailed analysis results. Furthermore, if the user is excited, the analysis unit can provide analysis results using visually appealing graphs and charts. This makes it possible to adjust the level of detail in the analysis results according to the user's emotions.

[0097] The presentation unit can adjust the priority of property information based on the user's current living situation. For example, if the user is raising children, property information suitable for raising children can be presented preferentially. Also, if the user has a pet, property information suitable for pets can be presented preferentially. Furthermore, if the user is elderly, property information for elderly people can be presented preferentially. This makes it possible to adjust the priority of property information based on the user's current living situation.

[0098] The input unit can estimate the user's emotions and provide an input assistance function based on the estimated user's emotions. For example, if the user is feeling stressed, the input assistance function can provide auto-completion or suggestions. If the user is relaxed, the input unit can provide detailed input guidance. Furthermore, if the user is in a hurry, the input unit can provide voice input with priority. This makes it possible to provide an input assistance function that corresponds to the user's emotions.

[0099] The acquisition unit can analyze the user's social media activities and acquire related surrounding environment information. For example, the acquisition unit can acquire related surrounding environment information based on information shared by the user on social media. The acquisition unit can also acquire related surrounding environment information based on information about accounts the user follows on social media. Furthermore, the acquisition unit can also acquire related surrounding environment information based on information the user has "liked" on social media. This makes it possible to acquire related information based on the user's social media activities.

[0100] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated user's emotions. For example, if the user is relaxed, a detailed analysis can be prioritized. If the user is in a hurry, an analysis that produces results quickly can be prioritized. Furthermore, if the user is excited, a visually appealing analysis can be prioritized. This makes it possible to determine the priority of analysis according to the user's emotions.

[0101] The presentation unit can analyze the user's past browsing history and prioritize the presentation of related property information. For example, similar property information can be prioritized based on property information the user has previously viewed. Also, if the user frequently views specific conditions (e.g., price range, area, etc.), property information that matches those conditions can be prioritized. Furthermore, the user's past browsing history can be used to optimize property information viewed during specific time periods. This makes it possible to present optimal property information based on the user's past browsing history.

[0102] The input unit can estimate the user's emotions and adjust the layout of the input interface based on the estimated user's emotions. For example, if the user is nervous, a simple, highly visible layout can be provided. If the user is relaxed, detailed input options can be provided. Furthermore, if the user is having fun, a colorful, visually appealing layout can be provided. This makes it possible to adjust the layout of the input interface according to the user's emotions.

[0103] The acquisition unit can prioritize acquisition of related surrounding environment information based on the user's current location information. For example, if the user is in a specific area, the acquisition unit prioritizes acquisition of surrounding environment information related to that area. Also, if the user is traveling, the acquisition unit can prioritize acquisition of surrounding environment information related to the travel destination. Furthermore, if the user is considering moving, the acquisition unit can prioritize acquisition of surrounding environment information related to the moving destination. This makes it possible to acquire highly relevant surrounding environment information based on the user's current location information.

[0104] The analysis unit can estimate the user's emotions and adjust the display format of the analysis results based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible display format can be provided. If the user is relaxed, a display format including detailed information can be provided. Furthermore, if the user is in a hurry, a display format that focuses on the main points can be provided. This makes it possible to adjust the display format of the analysis results according to the user's emotions.

[0105] The processing flow of the second embodiment will be briefly explained below.

[0106] Step 1: The input unit inputs property information. Property information includes, but is not limited to, the address, price, layout, and age of the property. In addition to the property information, the input unit can also input, for example, recommended nearby spots, lifestyles, and points to promote using free words. Step 2: The acquisition unit automatically acquires surrounding environment information based on the property information input by the input unit. The surrounding environment information includes, but is not limited to, roadside land prices, land prices, and examples of successful transactions for similar properties. The acquisition unit acquires roadside land prices from a database of the Ministry of Land, Infrastructure, Transport and Tourism, for example. The acquisition unit can also acquire official land prices and surveyed land prices. Furthermore, the acquisition unit can also acquire successful transactions from the past year and in the same area. Step 3: The analysis unit comprehensively analyzes the information acquired by the acquisition unit. The analysis is performed, for example, using a generation AI. The generation AI comprehensively analyzes the property information and surrounding environment information based on a specific machine learning model and the dataset used. For example, the generation AI performs its analysis taking into account the interrelationships between the property information and surrounding environment information. Step 4: The presentation unit presents the results of the analysis performed by the analysis unit. The presentation may be in the form of a graph, a table, text, or the like. The presentation unit can present the analysis results within a few seconds. For example, the presentation unit can improve user convenience by quickly presenting the analysis results.

[0107] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0109] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0110] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0111] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0112] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0113] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0114] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0115] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0116] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0117] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0118] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0119] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0120] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0121] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0122] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0123] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0124] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0125] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0126] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0127] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0128] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0129] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0130] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0131] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0133] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0134] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0135] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0136] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0137] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0138] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0139] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0141] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0142] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0143] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0144] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0145] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0146] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0147] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0149] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0150] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0151] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0152] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0153] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0154] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0155] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0156] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0157] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0158] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0159] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0160] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0161] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0162] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0163] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0164] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0165] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0166] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0167] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0168] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0169] 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.

[0170] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0171] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0172] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0173] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0174] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0175] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0176] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0177] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0178] [Explanation of symbols]

[0179] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an input section for inputting property information; an acquisition unit that automatically acquires surrounding environment information based on the property information input by the input unit; an analysis unit that comprehensively analyzes the information acquired by the acquisition unit; a presentation unit that presents the results of the analysis performed by the analysis unit. A system characterized by:

2. The input unit In addition to the property information, enter recommended spots in the neighborhood, your lifestyle, or points you want to highlight using free words.

2. The system of claim 1.

3. The acquisition unit Automatically obtain information on the surrounding environment, such as roadside land prices, land prices, and examples of similar property transactions 2. The system of claim 1.

4. The analysis unit Using generative AI to comprehensively analyze property information and surrounding environment information 2. The system of claim 1.

5. The presentation unit Rapidly presenting analytical results 2. The system of claim 1.

6. The presentation unit Complete the database for high-precision searches 2. The system of claim 1.

7. The presentation unit The apartment building does not have a parking lot, but there is a monthly parking lot within a few minutes' walk. Complementary and essential search and suggestions are performed.

2. The system of claim 1.

8. The input unit Estimates user emotions and adjusts the property information input interface based on the estimated user emotions.

2. The system of claim 1.

9. The input unit Analyzes the user's past input history and suggests the optimal input method 2. The system of claim 1.

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